




import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__UpperCamelCase      : Tuple					      =   logging.getLogger(__name__)





def 	__A	(			__lowerCamelCase					)   -> Any:
	a		       =							p * torch.log(__lowerCamelCase					)
	a		       =							0
	return -plogp.sum(dim=-1					)





def 	__A	(			__lowerCamelCase					)   -> Tuple:
	logger.info("""lv, h >\t""" + """\t""".join(f'{x + 1}' for x in range(len(__lowerCamelCase					)					)					)					)
	for row in range(len(__lowerCamelCase					)					):
		if tensor.dtype != torch.long:
			logger.info(f'layer {row + 1}:\t' + """\t""".join(f'{x:.5f}' for x in tensor[row].cpu().data					)					)
		else:
			logger.info(f'layer {row + 1}:\t' + """\t""".join(f'{x:d}' for x in tensor[row].cpu().data					)					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase=True							,    __lowerCamelCase=True							,    __lowerCamelCase=None							,    __lowerCamelCase=False					)   -> str:
	a       ,				a		       =							model.config.num_hidden_layers, model.config.num_attention_heads
	a		       =							torch.zeros(__lowerCamelCase							,    __lowerCamelCase					).to(args.device					)
	a		       =							torch.zeros(__lowerCamelCase							,    __lowerCamelCase					).to(args.device					)

	if head_mask is None:
		a		       =							torch.ones(__lowerCamelCase							,    __lowerCamelCase					).to(args.device					)

	head_mask.requires_grad_(requires_grad=__lowerCamelCase					)
	# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
	if actually_pruned:
		a		       =							None

	a		       =							None
	a		       =							None
	a		       =							0.0

	for step, inputs in enumerate(tqdm(__lowerCamelCase							,    desc="""Iteration"""							,    disable=args.local_rank not in [-1, 0]					)					):
		for k, v in inputs.items():
			a		       =							v.to(args.device					)

		# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
		a		       =							model(**__lowerCamelCase							,    head_mask=__lowerCamelCase					)
		a       ,				a       ,				a		       =							(
		    outputs[0],
		    outputs[1],
		    outputs[-1],
		)  # Loss and logits are the first, attention the last
		loss.backward()  # Backpropagate to populate the gradients in the head mask

		if compute_entropy:
			for layer, attn in enumerate(__lowerCamelCase					):
				a		       =							entropy(attn.detach()					) * inputs["""attention_mask"""].float().unsqueeze(1					)
				attn_entropy[layer] += masked_entropy.sum(-1					).sum(0					).detach()

		if compute_importance:
			head_importance += head_mask.grad.abs().detach()

		# Also store our logits/labels if we want to compute metrics afterwards
		if preds is None:
			a		       =							logits.detach().cpu().numpy()
			a		       =							inputs["""labels"""].detach().cpu().numpy()
		else:
			a		       =							np.append(__lowerCamelCase							,    logits.detach().cpu().numpy()							,    axis=0					)
			a		       =							np.append(__lowerCamelCase							,    inputs["""labels"""].detach().cpu().numpy()							,    axis=0					)

		tot_tokens += inputs["attention_mask"].float().detach().sum().data

	# Normalize
	attn_entropy /= tot_tokens
	head_importance /= tot_tokens
	# Layerwise importance normalization
	if not args.dont_normalize_importance_by_layer:
		a		       =							2
		a		       =							torch.pow(torch.pow(__lowerCamelCase							,    __lowerCamelCase					).sum(-1					)							,    1 / exponent					)
		head_importance /= norm_by_layer.unsqueeze(-1					) + 1E-2_0

	if not args.dont_normalize_global_importance:
		a		       =							(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

	# Print/save matrices
	np.save(os.path.join(args.output_dir							,    """attn_entropy.npy"""					)							,    attn_entropy.detach().cpu().numpy()					)
	np.save(os.path.join(args.output_dir							,    """head_importance.npy"""					)							,    head_importance.detach().cpu().numpy()					)

	logger.info("""Attention entropies"""					)
	print_ad_tensor(__lowerCamelCase					)
	logger.info("""Head importance scores"""					)
	print_ad_tensor(__lowerCamelCase					)
	logger.info("""Head ranked by importance scores"""					)
	a		       =							torch.zeros(head_importance.numel()							,    dtype=torch.long							,    device=args.device					)
	a		       =							torch.arange(
	    head_importance.numel()							,    device=args.device					)
	a		       =							head_ranks.view_as(__lowerCamelCase					)
	print_ad_tensor(__lowerCamelCase					)

	return attn_entropy, head_importance, preds, labels





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> Optional[Any]:
	a       ,				a       ,				a       ,				a		       =							compute_heads_importance(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    compute_entropy=__lowerCamelCase					)
	a		       =							np.argmax(__lowerCamelCase							,    axis=1					) if args.output_mode == """classification""" else np.squeeze(__lowerCamelCase					)
	a		       =							glue_compute_metrics(args.task_name							,    __lowerCamelCase							,    __lowerCamelCase					)[args.metric_name]
	logger.info("""Pruning: original score: %f, threshold: %f"""							,    __lowerCamelCase							,    original_score * args.masking_threshold					)

	a		       =							torch.ones_like(__lowerCamelCase					)
	a		       =							max(1							,    int(new_head_mask.numel() * args.masking_amount					)					)

	a		       =							original_score
	while current_score >= original_score * args.masking_threshold:
		a		       =							new_head_mask.clone()  # save current head mask
		# heads from least important to most - keep only not-masked heads
		a		       =							float("""Inf"""					)
		a		       =							head_importance.view(-1					).sort()[1]

		if len(__lowerCamelCase					) <= num_to_mask:
			break

		# mask heads
		a		       =							current_heads_to_mask[:num_to_mask]
		logger.info("""Heads to mask: %s"""							,    str(current_heads_to_mask.tolist()					)					)
		a		       =							new_head_mask.view(-1					)
		a		       =							0.0
		a		       =							new_head_mask.view_as(__lowerCamelCase					)
		a		       =							new_head_mask.clone().detach()
		print_ad_tensor(__lowerCamelCase					)

		# Compute metric and head importance again
		a       ,				a       ,				a       ,				a		       =							compute_heads_importance(
		    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    compute_entropy=__lowerCamelCase							,    head_mask=__lowerCamelCase					)
		a		       =							np.argmax(__lowerCamelCase							,    axis=1					) if args.output_mode == """classification""" else np.squeeze(__lowerCamelCase					)
		a		       =							glue_compute_metrics(args.task_name							,    __lowerCamelCase							,    __lowerCamelCase					)[args.metric_name]
		logger.info(
		    """Masking: current score: %f, remaining heads %d (%.1f percents)"""							,    __lowerCamelCase							,    new_head_mask.sum()							,    new_head_mask.sum() / new_head_mask.numel() * 100							,    )

	logger.info("""Final head mask"""					)
	print_ad_tensor(__lowerCamelCase					)
	np.save(os.path.join(args.output_dir							,    """head_mask.npy"""					)							,    head_mask.detach().cpu().numpy()					)

	return head_mask





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> List[Any]:
	a		       =							datetime.now()
	a       ,				a       ,				a       ,				a		       =							compute_heads_importance(
	    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    compute_entropy=__lowerCamelCase							,    compute_importance=__lowerCamelCase							,    head_mask=__lowerCamelCase					)
	a		       =							np.argmax(__lowerCamelCase							,    axis=1					) if args.output_mode == """classification""" else np.squeeze(__lowerCamelCase					)
	a		       =							glue_compute_metrics(args.task_name							,    __lowerCamelCase							,    __lowerCamelCase					)[args.metric_name]
	a		       =							datetime.now() - before_time

	a		       =							sum(p.numel() for p in model.parameters()					)
	a		       =							{
	    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__lowerCamelCase					)					)
	}

	assert sum(len(__lowerCamelCase					) for h in heads_to_prune.values()					) == (1 - head_mask.long()).sum().item()
	model.prune_heads(__lowerCamelCase					)
	a		       =							sum(p.numel() for p in model.parameters()					)

	a		       =							datetime.now()
	a       ,				a       ,				a       ,				a		       =							compute_heads_importance(
	    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    compute_entropy=__lowerCamelCase							,    compute_importance=__lowerCamelCase							,    head_mask=__lowerCamelCase							,    actually_pruned=__lowerCamelCase							,    )
	a		       =							np.argmax(__lowerCamelCase							,    axis=1					) if args.output_mode == """classification""" else np.squeeze(__lowerCamelCase					)
	a		       =							glue_compute_metrics(args.task_name							,    __lowerCamelCase							,    __lowerCamelCase					)[args.metric_name]
	a		       =							datetime.now() - before_time

	logger.info(
	    """Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"""							,    __lowerCamelCase							,    __lowerCamelCase							,    pruned_num_params / original_num_params * 100							,    )
	logger.info("""Pruning: score with masking: %f score with pruning: %f"""							,    __lowerCamelCase							,    __lowerCamelCase					)
	logger.info("""Pruning: speed ratio (new timing / original timing): %f percents"""							,    original_time / new_time * 100					)





def 	__A	(			)   -> Any:
	a		       =							argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--data_dir"""							,    default=__lowerCamelCase							,    type=__lowerCamelCase							,    required=__lowerCamelCase							,    help="""The input data dir. Should contain the .tsv files (or other data files) for the task."""							,    )
	parser.add_argument(
	    """--model_name_or_path"""							,    default=__lowerCamelCase							,    type=__lowerCamelCase							,    required=__lowerCamelCase							,    help="""Path to pretrained model or model identifier from huggingface.co/models"""							,    )
	parser.add_argument(
	    """--task_name"""							,    default=__lowerCamelCase							,    type=__lowerCamelCase							,    required=__lowerCamelCase							,    help="""The name of the task to train selected in the list: """ + """, """.join(glue_processors.keys()					)							,    )
	parser.add_argument(
	    """--output_dir"""							,    default=__lowerCamelCase							,    type=__lowerCamelCase							,    required=__lowerCamelCase							,    help="""The output directory where the model predictions and checkpoints will be written."""							,    )

	# Other parameters
	parser.add_argument(
	    """--config_name"""							,    default=""""""							,    type=__lowerCamelCase							,    help="""Pretrained config name or path if not the same as model_name_or_path"""							,    )
	parser.add_argument(
	    """--tokenizer_name"""							,    default=""""""							,    type=__lowerCamelCase							,    help="""Pretrained tokenizer name or path if not the same as model_name_or_path"""							,    )
	parser.add_argument(
	    """--cache_dir"""							,    default=__lowerCamelCase							,    type=__lowerCamelCase							,    help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""							,    )
	parser.add_argument(
	    """--data_subset"""							,    type=__lowerCamelCase							,    default=-1							,    help="""If > 0: limit the data to a subset of data_subset instances."""					)
	parser.add_argument(
	    """--overwrite_output_dir"""							,    action="""store_true"""							,    help="""Whether to overwrite data in output directory"""					)
	parser.add_argument(
	    """--overwrite_cache"""							,    action="""store_true"""							,    help="""Overwrite the cached training and evaluation sets"""					)

	parser.add_argument(
	    """--dont_normalize_importance_by_layer"""							,    action="""store_true"""							,    help="""Don't normalize importance score by layers"""					)
	parser.add_argument(
	    """--dont_normalize_global_importance"""							,    action="""store_true"""							,    help="""Don't normalize all importance scores between 0 and 1"""							,    )

	parser.add_argument(
	    """--try_masking"""							,    action="""store_true"""							,    help="""Whether to try to mask head until a threshold of accuracy."""					)
	parser.add_argument(
	    """--masking_threshold"""							,    default=0.9							,    type=__lowerCamelCase							,    help="""masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."""							,    )
	parser.add_argument(
	    """--masking_amount"""							,    default=0.1							,    type=__lowerCamelCase							,    help="""Amount to heads to masking at each masking step."""					)
	parser.add_argument("""--metric_name"""							,    default="""acc"""							,    type=__lowerCamelCase							,    help="""Metric to use for head masking."""					)

	parser.add_argument(
	    """--max_seq_length"""							,    default=128							,    type=__lowerCamelCase							,    help=(
	        """The maximum total input sequence length after WordPiece tokenization. \n"""
	        """Sequences longer than this will be truncated, sequences shorter padded."""
	    )							,    )
	parser.add_argument("""--batch_size"""							,    default=1							,    type=__lowerCamelCase							,    help="""Batch size."""					)

	parser.add_argument("""--seed"""							,    type=__lowerCamelCase							,    default=42					)
	parser.add_argument("""--local_rank"""							,    type=__lowerCamelCase							,    default=-1							,    help="""local_rank for distributed training on gpus"""					)
	parser.add_argument("""--no_cuda"""							,    action="""store_true"""							,    help="""Whether not to use CUDA when available"""					)
	parser.add_argument("""--server_ip"""							,    type=__lowerCamelCase							,    default=""""""							,    help="""Can be used for distant debugging."""					)
	parser.add_argument("""--server_port"""							,    type=__lowerCamelCase							,    default=""""""							,    help="""Can be used for distant debugging."""					)
	a		       =							parser.parse_args()

	if args.server_ip and args.server_port:
		# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
		import ptvsd

		print("""Waiting for debugger attach"""					)
		ptvsd.enable_attach(address=(args.server_ip, args.server_port)							,    redirect_output=__lowerCamelCase					)
		ptvsd.wait_for_attach()

	# Setup devices and distributed training
	if args.local_rank == -1 or args.no_cuda:
		a		       =							torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""					)
		a		       =							0 if args.no_cuda else torch.cuda.device_count()
	else:
		torch.cuda.set_device(args.local_rank					)
		a		       =							torch.device("""cuda"""							,    args.local_rank					)
		a		       =							1
		torch.distributed.init_process_group(backend="""nccl"""					)  # Initializes the distributed backend

	# Setup logging
	logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN					)
	logger.info("""device: {} n_gpu: {}, distributed: {}""".format(args.device							,    args.n_gpu							,    bool(args.local_rank != -1					)					)					)
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(args.local_rank					):
		transformers.utils.logging.set_verbosity_info()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

	# Set seeds
	set_seed(args.seed					)

	# Prepare GLUE task
	a		       =							args.task_name.lower()
	if args.task_name not in glue_processors:
		raise ValueError("""Task not found: %s""" % (args.task_name)					)
	a		       =							glue_processors[args.task_name]()
	a		       =							glue_output_modes[args.task_name]
	a		       =							processor.get_labels()
	a		       =							len(__lowerCamelCase					)

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	a		       =							AutoConfig.from_pretrained(
	    args.config_name if args.config_name else args.model_name_or_path							,    num_labels=__lowerCamelCase							,    finetuning_task=args.task_name							,    output_attentions=__lowerCamelCase							,    cache_dir=args.cache_dir							,    )
	a		       =							AutoTokenizer.from_pretrained(
	    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path							,    cache_dir=args.cache_dir							,    )
	a		       =							AutoModelForSequenceClassification.from_pretrained(
	    args.model_name_or_path							,    from_tf=bool(""".ckpt""" in args.model_name_or_path					)							,    config=__lowerCamelCase							,    cache_dir=args.cache_dir							,    )

	# Distributed and parallel training
	model.to(args.device					)
	if args.local_rank != -1:
		a		       =							nn.parallel.DistributedDataParallel(
		    __lowerCamelCase							,    device_ids=[args.local_rank]							,    output_device=args.local_rank							,    find_unused_parameters=__lowerCamelCase					)
	elif args.n_gpu > 1:
		a		       =							nn.DataParallel(__lowerCamelCase					)

	# Print/save training arguments
	os.makedirs(args.output_dir							,    exist_ok=__lowerCamelCase					)
	torch.save(__lowerCamelCase							,    os.path.join(args.output_dir							,    """run_args.bin"""					)					)
	logger.info("""Training/evaluation parameters %s"""							,    __lowerCamelCase					)

	# Prepare dataset for the GLUE task
	a		       =							GlueDataset(__lowerCamelCase							,    tokenizer=__lowerCamelCase							,    mode="""dev"""					)
	if args.data_subset > 0:
		a		       =							Subset(__lowerCamelCase							,    list(range(min(args.data_subset							,    len(__lowerCamelCase					)					)					)					)					)
	a		       =							SequentialSampler(__lowerCamelCase					) if args.local_rank == -1 else DistributedSampler(__lowerCamelCase					)
	a		       =							DataLoader(
	    __lowerCamelCase							,    sampler=__lowerCamelCase							,    batch_size=args.batch_size							,    collate_fn=__lowerCamelCase					)

	# Compute head entropy and importance score
	compute_heads_importance(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)

	# Try head masking (set heads to zero until the score goes under a threshole)
	# and head pruning (remove masked heads and see the effect on the network)
	if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
		a		       =							mask_heads(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)
		prune_heads(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)


if __name__ == "__main__":
		main()







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.gptj.modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    shape_list,
		)

class 	__lowerCAmelCase			:
							def __init__(		self		:Tuple	,   __magic_name__		:str       ):







								'''simple docstring'''







								a		       =							parent
								a		       =							13
								a		       =							7
								a		       =							True
								a		       =							True
								a		       =							True
								a		       =							True
								a		       =							True
								a		       =							99
								a		       =							32
								a		       =							4
								a		       =							2
								a		       =							4
								a		       =							37
								a		       =							"""gelu"""
								a		       =							0.1
								a		       =							0.1
								a		       =							512
								a		       =							16
								a		       =							2
								a		       =							0.02
								a		       =							3
								a		       =							4
								a		       =							None
								a		       =							self.vocab_size - 1
								a		       =							self.vocab_size - 1
								a		       =							self.vocab_size - 1
							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







								'''simple docstring'''







								a		       =							ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size       )

								a		       =							None
								if self.use_input_mask:
									a		       =							random_attention_mask([self.batch_size, self.seq_length]       )

								a		       =							None
								if self.use_token_type_ids:
									a		       =							ids_tensor([self.batch_size, self.seq_length]	,   self.type_vocab_size       )

								a		       =							None
								if self.use_mc_token_ids:
									a		       =							ids_tensor([self.batch_size, self.num_choices]	,   self.seq_length       )

								a		       =							None
								a		       =							None
								a		       =							None
								if self.use_labels:
									a		       =							ids_tensor([self.batch_size]	,   self.type_sequence_label_size       )
									a		       =							ids_tensor([self.batch_size, self.seq_length]	,   self.num_labels       )
									a		       =							ids_tensor([self.batch_size]	,   self.num_choices       )

								a		       =							GPTJConfig(
								    vocab_size=self.vocab_size	,   n_embd=self.hidden_size	,   n_layer=self.num_hidden_layers	,   n_head=self.num_attention_heads	,   intermediate_size=self.intermediate_size	,   hidden_act=self.hidden_act	,   hidden_dropout_prob=self.hidden_dropout_prob	,   attention_probs_dropout_prob=self.attention_probs_dropout_prob	,   n_positions=self.max_position_embeddings	,   type_vocab_size=self.type_vocab_size	,   initializer_range=self.initializer_range	,   bos_token_id=self.bos_token_id	,   eos_token_id=self.eos_token_id	,   pad_token_id=self.pad_token_id	,   rotary_dim=self.rotary_dim	,   return_dict=__magic_name__	,   )

								a		       =							ids_tensor([self.num_hidden_layers, self.num_attention_heads]	,   2       )

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								    token_type_ids,
								    mc_token_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:Any	,   __magic_name__		:Tuple	,   __magic_name__		:str	,   __magic_name__		:Any	,   __magic_name__		:Optional[Any]	,   *__magic_name__		:Optional[Any]       ):







								'''simple docstring'''







								a		       =							TFGPTJModel(config=__magic_name__       )
								a		       =							{
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}
								a		       =							model(__magic_name__       )

								a		       =							[input_ids, None, input_mask]  # None is the input for 'past'
								a		       =							model(__magic_name__       )

								a		       =							model(__magic_name__       )

								self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)       )
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:List[str]	,   __magic_name__		:str	,   __magic_name__		:Tuple	,   __magic_name__		:Union[str, Any]	,   *__magic_name__		:str       ):







								'''simple docstring'''







								a		       =							TFGPTJModel(config=__magic_name__       )

								# first forward pass
								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__	,   use_cache=__magic_name__       )
								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__       )
								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__	,   use_cache=__magic_name__       )

								self.parent.assertTrue(len(__magic_name__       ) == len(__magic_name__       )       )
								self.parent.assertTrue(len(__magic_name__       ) == len(__magic_name__       ) + 1       )

								a       ,				a		       =							outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								a		       =							ids_tensor((self.batch_size, 1)	,   config.vocab_size       )
								a		       =							ids_tensor([self.batch_size, 1]	,   self.type_vocab_size       )

								# append to next input_ids and token_type_ids
								a		       =							tf.concat([input_ids, next_tokens]	,   axis=-1       )
								a		       =							tf.concat([token_type_ids, next_token_types]	,   axis=-1       )

								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__       )["""last_hidden_state"""]
								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__	,   past_key_values=__magic_name__       )[
								    """last_hidden_state"""
								]

								# select random slice
								a		       =							int(ids_tensor((1,)	,   shape_list(__magic_name__       )[-1]       )       )
								a		       =							output_from_no_past[:, -1, random_slice_idx]
								a		       =							output_from_past[:, 0, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(__magic_name__	,   __magic_name__	,   rtol=1E-6       )
							def    lowerCamelCase__							(		self		:List[Any]	,   __magic_name__		:List[str]	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:Optional[int]	,   __magic_name__		:str	,   __magic_name__		:str	,   *__magic_name__		:str       ):







								'''simple docstring'''







								a		       =							TFGPTJModel(config=__magic_name__       )

								# create attention mask
								a		       =							self.seq_length // 2
								a		       =							tf.ones((self.batch_size, half_seq_length)	,   dtype=tf.intaa       )
								a		       =							tf.zeros((self.batch_size, self.seq_length - half_seq_length)	,   dtype=tf.intaa       )
								a		       =							tf.concat([attn_mask_begin, attn_mask_end]	,   axis=1       )

								# first forward pass
								a       ,				a		       =							model(__magic_name__	,   attention_mask=__magic_name__       ).to_tuple()

								# create hypothetical next token and extent to next_input_ids
								a		       =							ids_tensor((self.batch_size, 1)	,   config.vocab_size       )

								# change a random masked slice from input_ids
								a		       =							ids_tensor((1,)	,   __magic_name__       ).numpy() + 1
								a		       =							ids_tensor((self.batch_size, self.seq_length)	,   config.vocab_size       )
								a		       =							tf.range(self.seq_length       ) == (self.seq_length - random_seq_idx_to_change)
								a		       =							tf.transpose(
								    tf.broadcast_to(tf.expand_dims(__magic_name__	,   -1       )	,   (self.seq_length, self.batch_size)       )       )
								a		       =							tf.where(__magic_name__	,   __magic_name__	,   __magic_name__       )

								# append to next input_ids and attn_mask
								a		       =							tf.concat([input_ids, next_tokens]	,   axis=-1       )
								a		       =							tf.concat([attn_mask, tf.ones((shape_list(__magic_name__       )[0], 1)	,   dtype=tf.intaa       )]	,   axis=1       )

								# get two different outputs
								a		       =							model(__magic_name__	,   attention_mask=__magic_name__       )["""last_hidden_state"""]
								a		       =							model(__magic_name__	,   past_key_values=__magic_name__	,   attention_mask=__magic_name__       )[
								    """last_hidden_state"""
								]

								# select random slice
								a		       =							int(ids_tensor((1,)	,   shape_list(__magic_name__       )[-1]       )       )
								a		       =							output_from_no_past[:, -1, random_slice_idx]
								a		       =							output_from_past[:, 0, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(__magic_name__	,   __magic_name__	,   rtol=1E-1_2       )
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:List[Any]	,   __magic_name__		:List[str]	,   __magic_name__		:str	,   __magic_name__		:str	,   __magic_name__		:Optional[int]	,   *__magic_name__		:Any       ):







								'''simple docstring'''







								a		       =							TFGPTJModel(config=__magic_name__       )

								a		       =							input_ids[:1, :]
								a		       =							input_mask[:1, :]
								a		       =							token_type_ids[:1, :]
								a		       =							1

								# first forward pass
								a		       =							model(__magic_name__	,   attention_mask=__magic_name__	,   token_type_ids=__magic_name__	,   use_cache=__magic_name__       )

								a       ,				a		       =							outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								a		       =							ids_tensor((self.batch_size, 3)	,   config.vocab_size       )
								a		       =							ids_tensor((self.batch_size, 3)	,   2       )
								a		       =							ids_tensor((self.batch_size, 3)	,   self.type_vocab_size       )

								# append to next input_ids and token_type_ids
								a		       =							tf.concat([input_ids, next_tokens]	,   axis=-1       )
								a		       =							tf.concat([input_mask, next_attn_mask]	,   axis=-1       )
								a		       =							tf.concat([token_type_ids, next_token_types]	,   axis=-1       )

								a		       =							model(
								    __magic_name__	,   token_type_ids=__magic_name__	,   attention_mask=__magic_name__       )["""last_hidden_state"""]
								a		       =							model(
								    __magic_name__	,   token_type_ids=__magic_name__	,   attention_mask=__magic_name__	,   past_key_values=__magic_name__	,   )["""last_hidden_state"""]
								self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]       )

								# select random slice
								a		       =							int(ids_tensor((1,)	,   shape_list(__magic_name__       )[-1]       )       )
								a		       =							output_from_no_past[:, -3:, random_slice_idx]
								a		       =							output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(__magic_name__	,   __magic_name__	,   rtol=1E-3       )
							def    lowerCamelCase__							(		self		:str	,   __magic_name__		:Optional[int]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:str	,   __magic_name__		:int	,   *__magic_name__		:List[Any]       ):







								'''simple docstring'''







								a		       =							TFGPTJForCausalLM(config=__magic_name__       )
								a		       =							{
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}
								a		       =							model(__magic_name__       )
								self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.vocab_size)       )





							def    lowerCamelCase__							(		self		:int       ):







								'''simple docstring'''







								a		       =							self.prepare_config_and_inputs()

								(
								    (
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				
								)		       =							config_and_inputs

								a		       =							{
								    """input_ids""": input_ids,
								    """token_type_ids""": token_type_ids,
								    """attention_mask""": input_mask,
								}
								return config, inputs_dict







@require_tf
class 	__lowerCAmelCase			(			__magic_name__   , __magic_name__   , __magic_name__   , unittest.TestCase				):
							UpperCamelCase__        =						(
							    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
							    if is_tf_available()
							    else ()
							)

							UpperCamelCase__        =						(TFGPTJForCausalLM,) if is_tf_available() else ()
							UpperCamelCase__        =						(
							    {
							        '''feature-extraction''': TFGPTJModel,
							        '''question-answering''': TFGPTJForQuestionAnswering,
							        '''text-classification''': TFGPTJForSequenceClassification,
							        '''text-generation''': TFGPTJForCausalLM,
							        '''zero-shot''': TFGPTJForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							UpperCamelCase__        =						False
							UpperCamelCase__        =						False
							UpperCamelCase__        =						False
							UpperCamelCase__        =						False
							def    lowerCamelCase__							(		self		:Union[str, Any]	,   __magic_name__		:Any	,   __magic_name__		:Optional[int]	,   __magic_name__		:int	,   __magic_name__		:List[str]	,   __magic_name__		:Union[str, Any]       ):







								'''simple docstring'''







								if (
								    pipeline_test_casse_name == "QAPipelineTests"
								    and tokenizer_name is not None
								    and not tokenizer_name.endswith("""Fast"""       )
								):
									# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
									# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
									# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
									return True

								return False
							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								a		       =							TFGPTJModelTester(self       )
								a		       =							ConfigTester(self	,   config_class=__magic_name__	,   n_embd=37       )
							def    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								self.config_tester.run_common_tests()
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model(*__magic_name__       )
							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_past(*__magic_name__       )
							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_attention_mask_past(*__magic_name__       )
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_past_large_inputs(*__magic_name__       )
							def    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_lm_head_model(*__magic_name__       )
							@slow
							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""       )       ) > 0	,   """skip testing on GPU for now to avoid GPU OOM."""	,   )
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								a		       =							TFGPTJModel.from_pretrained("""EleutherAI/gpt-j-6B"""	,   from_pt=__magic_name__       )
								self.assertIsNotNone(__magic_name__       )





							@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""       )
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								super().test_resize_token_embeddings()







@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class 	__lowerCAmelCase			(			unittest.TestCase				):
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""	,   from_pt=__magic_name__       )
								a		       =							tf.convert_to_tensor([[464, 3290]]	,   dtype=tf.intaa       )  # The dog
								# fmt: off
								# The dog is a man's best friend. It is a loyal companion, and it is a friend
								a		       =							[464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 1_5185, 11, 290, 340, 318, 257, 1545]
								# fmt: on
								a		       =							model.generate(__magic_name__	,   do_sample=__magic_name__       )
								self.assertListEqual(output_ids[0].numpy().tolist()	,   __magic_name__       )
							def    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								a		       =							AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""	,   revision="""float16"""       )
								a		       =							TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""	,   revision="""float16"""	,   from_pt=__magic_name__       )

								a		       =							tokenizer("""Today is a nice day and"""	,   return_tensors="""tf"""       )
								# forces the generation to happen on CPU, to avoid GPU-related quirks
								with tf.device(""":/CPU:0"""       ):
									a		       =							model.generate(**__magic_name__	,   do_sample=__magic_name__	,   seed=[42, 0]       )
								a		       =							tokenizer.decode(output_ids[0]	,   skip_special_tokens=__magic_name__       )

								a		       =							"""Today is a nice day and I’m going to go for a walk. I’"""
								self.assertEqual(__magic_name__	,   __magic_name__       )
							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								a		       =							TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""	,   revision="""float16"""	,   from_pt=__magic_name__       )
								a		       =							AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""	,   revision="""float16"""       )

								a		       =							"""left"""

								# Define PAD Token = EOS Token = 50256
								a		       =							tokenizer.eos_token
								a		       =							model.config.eos_token_id

								# use different length sentences to test batching
								a		       =							[
								    """Hello, my dog is a little""",
								    """Today, I""",
								]
								a		       =							[
								    """Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia""",
								    """Today, I’m going to be talking about a topic that’""",
								]
								return model, tokenizer, sentences, expected_output_sentences
							def    lowerCamelCase__							(		self		:List[Any]       ):







								'''simple docstring'''







								a       ,				a       ,				a       ,				a		       =							self._get_beam_search_test_objects()

								a		       =							tokenizer(__magic_name__	,   return_tensors="""tf"""	,   padding=__magic_name__       )
								a		       =							model.generate(**__magic_name__	,   do_sample=__magic_name__	,   num_beams=2       )
								a		       =							tokenizer.batch_decode(__magic_name__	,   skip_special_tokens=__magic_name__       )
								self.assertListEqual(__magic_name__	,   __magic_name__       )
							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								a       ,				a       ,				a       ,				a		       =							self._get_beam_search_test_objects()

								a		       =							tokenizer(__magic_name__	,   return_tensors="""tf"""	,   padding=__magic_name__       )
								a		       =							tokenizer(sentences[0]	,   return_tensors="""tf"""       )
								a		       =							model.generate(**__magic_name__	,   do_sample=__magic_name__	,   num_beams=2       )
								a		       =							(
								    shape_list(inputs_non_padded["""input_ids"""]       )[-1]
								    - tf.reduce_sum(tf.cast(inputs["""attention_mask"""][-1]	,   tf.intaa       )       ).numpy()
								)
								a		       =							tokenizer(sentences[1]	,   return_tensors="""tf"""       )
								a		       =							model.generate(
								    **__magic_name__	,   do_sample=__magic_name__	,   num_beams=2	,   max_length=model.config.max_length - num_paddings       )
								a		       =							tokenizer.decode(output_non_padded[0]	,   skip_special_tokens=__magic_name__       )
								a		       =							tokenizer.decode(output_padded[0]	,   skip_special_tokens=__magic_name__       )
								self.assertListEqual(__magic_name__	,   [non_padded_sentence, padded_sentence]       )





							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								a       ,				a       ,				a       ,				a		       =							self._get_beam_search_test_objects()

								a		       =							tokenizer(__magic_name__	,   return_tensors="""tf"""	,   padding=__magic_name__       )
								a		       =							tf.function(model.generate	,   jit_compile=__magic_name__       )
								a		       =							xla_generate(**__magic_name__	,   do_sample=__magic_name__	,   num_beams=2       )
								a		       =							tokenizer.batch_decode(__magic_name__	,   skip_special_tokens=__magic_name__       )
								self.assertListEqual(__magic_name__	,   __magic_name__       )

