





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowercase													=			logging.get_logger(__name__)

__lowercase													=			{
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}

class   __snake_case				(			_a   ):






				'''simple docstring'''
				_lowerCAmelCase     =	"""bigbird_pegasus"""
				_lowerCAmelCase     =	["""past_key_values"""]
				_lowerCAmelCase     =	{
				    """num_attention_heads""": """encoder_attention_heads""",
				    """hidden_size""": """d_model""",
				    """attention_probs_dropout_prob""": """attention_dropout""",
				}





				def __init__(   self						:		Any			,						snake_case						:		Optional[int]=96_103			,						snake_case						:		Tuple=4_096			,						snake_case						:		str=16			,						snake_case						:		Any=4_096			,						snake_case						:		Any=16			,						snake_case						:		Dict=16			,						snake_case						:		str=4_096			,						snake_case						:		Tuple=16			,						snake_case						:		Dict=0.0			,						snake_case						:		str=0.0			,						snake_case						:		Union[str, Any]=True			,						snake_case						:		List[Any]=True			,						snake_case						:		Optional[Any]="gelu_new"			,						snake_case						:		Union[str, Any]=1_024			,						snake_case						:		Union[str, Any]=0.1			,						snake_case						:		int=0.0			,						snake_case						:		Optional[Any]=0.0			,						snake_case						:		str=0.02			,						snake_case						:		int=2			,						snake_case						:		Any=0.0			,						snake_case						:		str=True			,						snake_case						:		Tuple=0			,						snake_case						:		Any=2			,						snake_case						:		int=1			,						snake_case						:		Any="block_sparse"			,						snake_case						:		List[Any]=64			,						snake_case						:		Dict=3			,						snake_case						:		Optional[Any]=False			,						**snake_case						:		Dict			,						):

						"""simple docstring"""





						__lowerCAmelCase			=				vocab_size
						__lowerCAmelCase			=				max_position_embeddings
						__lowerCAmelCase			=				d_model
						__lowerCAmelCase			=				encoder_ffn_dim
						__lowerCAmelCase			=				encoder_layers
						__lowerCAmelCase			=				encoder_attention_heads
						__lowerCAmelCase			=				decoder_ffn_dim
						__lowerCAmelCase			=				decoder_layers
						__lowerCAmelCase			=				decoder_attention_heads
						__lowerCAmelCase			=				dropout
						__lowerCAmelCase			=				attention_dropout
						__lowerCAmelCase			=				activation_dropout
						__lowerCAmelCase			=				activation_function
						__lowerCAmelCase			=				init_std
						__lowerCAmelCase			=				encoder_layerdrop
						__lowerCAmelCase			=				decoder_layerdrop
						__lowerCAmelCase			=				classifier_dropout
						__lowerCAmelCase			=				use_cache
						__lowerCAmelCase			=				encoder_layers
						__lowerCAmelCase			=				scale_embedding  # scale factor will be sqrt(d_model) if True

						# extra config
						__lowerCAmelCase			=				attention_type
						__lowerCAmelCase			=				block_size
						__lowerCAmelCase			=				num_random_blocks
						__lowerCAmelCase			=				use_bias

						super().__init__(
						    pad_token_id=snake_case			,						bos_token_id=snake_case			,						eos_token_id=snake_case			,						is_encoder_decoder=snake_case			,						decoder_start_token_id=snake_case			,						**snake_case			,						)


class   __snake_case				(			_a   ):






				'''simple docstring'''





				@property
				def 	A		(   self						:		List[str]							):

						"""simple docstring"""





						if self.task in ["default", "seq2seq-lm"]:
								__lowerCAmelCase			=				OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
								        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
								    ]							)

								if self.use_past:
										__lowerCAmelCase			=				{0: '''batch'''}
										__lowerCAmelCase			=				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
								else:
										__lowerCAmelCase			=				{0: '''batch''', 1: '''decoder_sequence'''}
										__lowerCAmelCase			=				{0: '''batch''', 1: '''decoder_sequence'''}

								if self.use_past:
										self.fill_with_past_key_values_(snake_case			,						direction='''inputs'''							)
						elif self.task == "causal-lm":
								# TODO: figure this case out.
								__lowerCAmelCase			=				OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
								        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
								    ]							)
								if self.use_past:
										__lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
										for i in range(snake_case							):
												__lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
												__lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
						else:
								__lowerCAmelCase			=				OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
								        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
								        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
								        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
								    ]							)

						return common_inputs





				@property
				def 	A		(   self						:		Optional[int]							):

						"""simple docstring"""





						if self.task in ["default", "seq2seq-lm"]:
								__lowerCAmelCase			=				super().outputs
						else:
								__lowerCAmelCase			=				super(snake_case			,						self							).outputs
								if self.use_past:
										__lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
										for i in range(snake_case							):
												__lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
												__lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
						return common_outputs





				def 	A		(   self						:		List[Any]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

						"""simple docstring"""





						__lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)

						# Generate decoder inputs
						__lowerCAmelCase			=				seq_length if not self.use_past else 1
						__lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)
						__lowerCAmelCase			=				{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
						__lowerCAmelCase			=				dict(**snake_case			,						**snake_case							)

						if self.use_past:
								if not is_torch_available():
										raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
								else:
										import torch
								__lowerCAmelCase	,						__lowerCAmelCase			=				common_inputs['''input_ids'''].shape
								__lowerCAmelCase			=				common_inputs['''decoder_input_ids'''].shape[1]
								__lowerCAmelCase	,						__lowerCAmelCase			=				self.num_attention_heads
								__lowerCAmelCase			=				(
								    batch,
								    num_encoder_attention_heads,
								    encoder_seq_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)
								__lowerCAmelCase			=				decoder_seq_length + 3
								__lowerCAmelCase			=				(
								    batch,
								    num_decoder_attention_heads,
								    decoder_past_length,
								    self._config.hidden_size // num_decoder_attention_heads,
								)

								__lowerCAmelCase			=				torch.cat(
								    [common_inputs['''decoder_attention_mask'''], torch.ones(snake_case			,						snake_case							)]			,						dim=1							)

								__lowerCAmelCase			=				[]
								# If the number of encoder and decoder layers are present in the model configuration, both are considered
								__lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
								__lowerCAmelCase			=				min(snake_case			,						snake_case							)
								__lowerCAmelCase			=				max(snake_case			,						snake_case							) - min_num_layers
								__lowerCAmelCase			=				'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

								for _ in range(snake_case							):
										common_inputs["past_key_values"].append(
										    (
										        torch.zeros(snake_case							),
										        torch.zeros(snake_case							),
										        torch.zeros(snake_case							),
										        torch.zeros(snake_case							),
										    )							)
								# TODO: test this.
								__lowerCAmelCase			=				encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
								for _ in range(snake_case			,						snake_case							):
										common_inputs["past_key_values"].append((torch.zeros(snake_case							), torch.zeros(snake_case							))							)
						return common_inputs





				def 	A		(   self						:		Tuple			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

						"""simple docstring"""





						__lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)

						if self.use_past:
								if not is_torch_available():
										raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
								else:
										import torch
								__lowerCAmelCase	,						__lowerCAmelCase			=				common_inputs['''input_ids'''].shape
								# Not using the same length for past_key_values
								__lowerCAmelCase			=				seqlen + 2
								__lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
								__lowerCAmelCase	,						__lowerCAmelCase			=				self.num_attention_heads
								__lowerCAmelCase			=				(
								    batch,
								    num_encoder_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)

								__lowerCAmelCase			=				common_inputs['''attention_mask'''].dtype
								__lowerCAmelCase			=				torch.cat(
								    [common_inputs['''attention_mask'''], torch.ones(snake_case			,						snake_case			,						dtype=snake_case							)]			,						dim=1							)
								__lowerCAmelCase			=				[
								    (torch.zeros(snake_case							), torch.zeros(snake_case							)) for _ in range(snake_case							)
								]
						return common_inputs





				def 	A		(   self						:		Optional[Any]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

						"""simple docstring"""





						__lowerCAmelCase			=				compute_effective_axis_dimension(
						    snake_case			,						fixed_dimension=OnnxConfig.default_fixed_batch			,						num_token_to_add=0							)

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						__lowerCAmelCase			=				tokenizer.num_special_tokens_to_add(snake_case							)
						__lowerCAmelCase			=				compute_effective_axis_dimension(
						    snake_case			,						fixed_dimension=OnnxConfig.default_fixed_sequence			,						num_token_to_add=snake_case							)

						# Generate dummy inputs according to compute batch and sequence
						__lowerCAmelCase			=				[''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
						__lowerCAmelCase			=				dict(tokenizer(snake_case			,						return_tensors=snake_case							)							)
						return common_inputs





				def 	A		(   self						:		List[str]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

						"""simple docstring"""





						if self.task in ["default", "seq2seq-lm"]:
								__lowerCAmelCase			=				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
								    snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)

						elif self.task == "causal-lm":
								__lowerCAmelCase			=				self._generate_dummy_inputs_for_causal_lm(
								    snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)
						else:
								__lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)

						return common_inputs





				def 	A		(   self						:		int			,						snake_case						:		Optional[int]			,						snake_case						:		List[Any]			,						snake_case						:		str			,						snake_case						:		Union[str, Any]							):

						"""simple docstring"""





						if self.task in ["default", "seq2seq-lm"]:
								__lowerCAmelCase			=				super()._flatten_past_key_values_(snake_case			,						snake_case			,						snake_case			,						snake_case							)
						else:
								__lowerCAmelCase			=				super(snake_case			,						self							)._flatten_past_key_values_(
								    snake_case			,						snake_case			,						snake_case			,						snake_case							)







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment

class   __snake_case				(			unittest.TestCase   ):






    '''simple docstring'''





    def 	A		(   self						:		int							):

      """simple docstring"""





      __lowerCAmelCase			=				inspect.getfile(accelerate.test_utils							)
      __lowerCAmelCase			=				os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ['''scripts''', '''test_sync.py''']							)





    @require_cpu
    def 	A		(   self						:		str							):

      """simple docstring"""





      debug_launcher(test_sync.main			,						num_processes=1							)





    @require_cpu
    def 	A		(   self						:		List[str]							):

      """simple docstring"""





      debug_launcher(test_sync.main							)





    @require_single_gpu
    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      test_sync.main()





    @require_multi_gpu
    def 	A		(   self						:		Union[str, Any]							):

      """simple docstring"""





      print(f'''Found {torch.cuda.device_count()} devices.'''							)
      __lowerCAmelCase			=				['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
      with patch_environment(omp_num_threads=1							):
        execute_subprocess_async(snake_case			,						env=os.environ.copy()							)
