

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 						__lowercase       :




			"""simple docstring"""







			def __init__(				self						:  Union[str, Any]     ,		lowerCAmelCase__						:  List[str]):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= parent
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= 13
									SCREAMING_SNAKE_CASE_:				str							= 7
									SCREAMING_SNAKE_CASE_:				List[Any]							= True
									SCREAMING_SNAKE_CASE_:				str							= True
									SCREAMING_SNAKE_CASE_:				List[Any]							= True
									SCREAMING_SNAKE_CASE_:				Optional[int]							= True
									SCREAMING_SNAKE_CASE_:				int							= True
									SCREAMING_SNAKE_CASE_:				List[str]							= 99
									SCREAMING_SNAKE_CASE_:				List[str]							= 32
									SCREAMING_SNAKE_CASE_:				Tuple							= 4
									SCREAMING_SNAKE_CASE_:				List[str]							= 2
									SCREAMING_SNAKE_CASE_:				Optional[int]							= 4
									SCREAMING_SNAKE_CASE_:				Dict							= 37
									SCREAMING_SNAKE_CASE_:				Any							= "gelu"
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 0.1
									SCREAMING_SNAKE_CASE_:				List[Any]							= 0.1
									SCREAMING_SNAKE_CASE_:				List[Any]							= 512
									SCREAMING_SNAKE_CASE_:				Optional[int]							= 16
									SCREAMING_SNAKE_CASE_:				Any							= 2
									SCREAMING_SNAKE_CASE_:				Dict							= 0.02
									SCREAMING_SNAKE_CASE_:				str							= 3
									SCREAMING_SNAKE_CASE_:				Dict							= 4
									SCREAMING_SNAKE_CASE_:				str							= None
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.vocab_size - 1
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.vocab_size - 1
									SCREAMING_SNAKE_CASE_:				str							= self.vocab_size - 1



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size)

									SCREAMING_SNAKE_CASE_:				str							= None
									if self.use_input_mask:
															SCREAMING_SNAKE_CASE_:				Optional[int]							= random_attention_mask([self.batch_size, self.seq_length])

									SCREAMING_SNAKE_CASE_:				List[str]							= None
									if self.use_token_type_ids:
															SCREAMING_SNAKE_CASE_:				Dict							= ids_tensor([self.batch_size, self.seq_length]     ,		self.type_vocab_size)

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= None
									if self.use_mc_token_ids:
															SCREAMING_SNAKE_CASE_:				Any							= ids_tensor([self.batch_size, self.num_choices]     ,		self.seq_length)

									SCREAMING_SNAKE_CASE_:				Tuple							= None
									SCREAMING_SNAKE_CASE_:				List[Any]							= None
									SCREAMING_SNAKE_CASE_:				List[Any]							= None
									if self.use_labels:
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= ids_tensor([self.batch_size]     ,		self.type_sequence_label_size)
															SCREAMING_SNAKE_CASE_:				int							= ids_tensor([self.batch_size, self.seq_length]     ,		self.num_labels)
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= ids_tensor([self.batch_size]     ,		self.num_choices)

									SCREAMING_SNAKE_CASE_:				str							= 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=lowerCAmelCase__     ,		)

									SCREAMING_SNAKE_CASE_:				Optional[int]							= 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  _SCREAMING_SNAKE_CASE    (				self						:  List[str]     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  List[Any]     ,		lowerCAmelCase__						:  Dict     ,		lowerCAmelCase__						:  Optional[int]     ,		*lowerCAmelCase__						:  str):
									SCREAMING_SNAKE_CASE_:				Tuple							= TFGPTJModel(config=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				int							= {
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}
									SCREAMING_SNAKE_CASE_:				List[str]							= model(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= [input_ids, None, input_mask]  # None is the input for 'past'
									SCREAMING_SNAKE_CASE_:				int							= model(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				Optional[int]							= model(lowerCAmelCase__)

									self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size))



			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  Union[str, Any]     ,		lowerCAmelCase__						:  Optional[Any]     ,		*lowerCAmelCase__						:  Dict):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= TFGPTJModel(config=lowerCAmelCase__)

									# first forward pass
									SCREAMING_SNAKE_CASE_:				Optional[int]							= model(lowerCAmelCase__     ,		token_type_ids=lowerCAmelCase__     ,		use_cache=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Dict							= model(lowerCAmelCase__     ,		token_type_ids=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				List[Any]							= model(lowerCAmelCase__     ,		token_type_ids=lowerCAmelCase__     ,		use_cache=lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Optional[Any]							= outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									SCREAMING_SNAKE_CASE_:				List[Any]							= ids_tensor((self.batch_size, 1)     ,		config.vocab_size)
									SCREAMING_SNAKE_CASE_:				int							= ids_tensor([self.batch_size, 1]     ,		self.type_vocab_size)

									# append to next input_ids and token_type_ids
									SCREAMING_SNAKE_CASE_:				Dict							= tf.concat([input_ids, next_tokens]     ,		axis=-1)
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= tf.concat([token_type_ids, next_token_types]     ,		axis=-1)

									SCREAMING_SNAKE_CASE_:				List[Any]							= model(lowerCAmelCase__     ,		token_type_ids=lowerCAmelCase__)["last_hidden_state"]
									SCREAMING_SNAKE_CASE_:				Optional[int]							= model(lowerCAmelCase__     ,		token_type_ids=lowerCAmelCase__     ,		past_key_values=lowerCAmelCase__)[
									    "last_hidden_state"
									]

									# select random slice
									SCREAMING_SNAKE_CASE_:				List[str]							= int(ids_tensor((1,)     ,		shape_list(lowerCAmelCase__)[-1]))
									SCREAMING_SNAKE_CASE_:				Any							= output_from_no_past[:, -1, random_slice_idx]
									SCREAMING_SNAKE_CASE_:				List[str]							= output_from_past[:, 0, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(lowerCAmelCase__     ,		lowerCAmelCase__     ,		rtol=1E-6)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  List[str]     ,		lowerCAmelCase__						:  Tuple     ,		*lowerCAmelCase__						:  Optional[int]):
									SCREAMING_SNAKE_CASE_:				List[Any]							= TFGPTJModel(config=lowerCAmelCase__)

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

									# first forward pass
									SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				str							= model(lowerCAmelCase__     ,		attention_mask=lowerCAmelCase__).to_tuple()

									# create hypothetical next token and extent to next_input_ids
									SCREAMING_SNAKE_CASE_:				Optional[int]							= ids_tensor((self.batch_size, 1)     ,		config.vocab_size)

									# change a random masked slice from input_ids
									SCREAMING_SNAKE_CASE_:				Tuple							= ids_tensor((1,)     ,		lowerCAmelCase__).numpy() + 1
									SCREAMING_SNAKE_CASE_:				Tuple							= ids_tensor((self.batch_size, self.seq_length)     ,		config.vocab_size)
									SCREAMING_SNAKE_CASE_:				Any							= tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
									SCREAMING_SNAKE_CASE_:				Any							= tf.transpose(
									    tf.broadcast_to(tf.expand_dims(lowerCAmelCase__     ,		-1)     ,		(self.seq_length, self.batch_size)))
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= tf.where(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__)

									# append to next input_ids and attn_mask
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= tf.concat([input_ids, next_tokens]     ,		axis=-1)
									SCREAMING_SNAKE_CASE_:				List[str]							= tf.concat([attn_mask, tf.ones((shape_list(lowerCAmelCase__)[0], 1)     ,		dtype=tf.intaa)]     ,		axis=1)

									# get two different outputs
									SCREAMING_SNAKE_CASE_:				List[str]							= model(lowerCAmelCase__     ,		attention_mask=lowerCAmelCase__)["last_hidden_state"]
									SCREAMING_SNAKE_CASE_:				Dict							= model(lowerCAmelCase__     ,		past_key_values=lowerCAmelCase__     ,		attention_mask=lowerCAmelCase__)[
									    "last_hidden_state"
									]

									# select random slice
									SCREAMING_SNAKE_CASE_:				List[str]							= int(ids_tensor((1,)     ,		shape_list(lowerCAmelCase__)[-1]))
									SCREAMING_SNAKE_CASE_:				Dict							= output_from_no_past[:, -1, random_slice_idx]
									SCREAMING_SNAKE_CASE_:				Tuple							= output_from_past[:, 0, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(lowerCAmelCase__     ,		lowerCAmelCase__     ,		rtol=1E-12)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  List[Any]     ,		lowerCAmelCase__						:  List[str]     ,		lowerCAmelCase__						:  int     ,		*lowerCAmelCase__						:  Optional[Any]):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= TFGPTJModel(config=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				List[str]							= input_ids[:1, :]
									SCREAMING_SNAKE_CASE_:				List[Any]							= input_mask[:1, :]
									SCREAMING_SNAKE_CASE_:				Tuple							= token_type_ids[:1, :]
									SCREAMING_SNAKE_CASE_:				List[Any]							= 1

									# first forward pass
									SCREAMING_SNAKE_CASE_:				List[Any]							= model(lowerCAmelCase__     ,		attention_mask=lowerCAmelCase__     ,		token_type_ids=lowerCAmelCase__     ,		use_cache=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Union[str, Any]							= outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									SCREAMING_SNAKE_CASE_:				Tuple							= ids_tensor((self.batch_size, 3)     ,		config.vocab_size)
									SCREAMING_SNAKE_CASE_:				Tuple							= ids_tensor((self.batch_size, 3)     ,		2)
									SCREAMING_SNAKE_CASE_:				List[Any]							= ids_tensor((self.batch_size, 3)     ,		self.type_vocab_size)

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

									SCREAMING_SNAKE_CASE_:				Dict							= model(
									    lowerCAmelCase__     ,		token_type_ids=lowerCAmelCase__     ,		attention_mask=lowerCAmelCase__)["last_hidden_state"]
									SCREAMING_SNAKE_CASE_:				Optional[int]							= model(
									    lowerCAmelCase__     ,		token_type_ids=lowerCAmelCase__     ,		attention_mask=lowerCAmelCase__     ,		past_key_values=lowerCAmelCase__     ,		)["last_hidden_state"]
									self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])

									# select random slice
									SCREAMING_SNAKE_CASE_:				Any							= int(ids_tensor((1,)     ,		shape_list(lowerCAmelCase__)[-1]))
									SCREAMING_SNAKE_CASE_:				str							= output_from_no_past[:, -3:, random_slice_idx]
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= output_from_past[:, :, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(lowerCAmelCase__     ,		lowerCAmelCase__     ,		rtol=1E-3)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]     ,		lowerCAmelCase__						:  Dict     ,		lowerCAmelCase__						:  Union[str, Any]     ,		lowerCAmelCase__						:  List[str]     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  List[str]     ,		*lowerCAmelCase__						:  Dict):
									SCREAMING_SNAKE_CASE_:				int							= TFGPTJForCausalLM(config=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				str							= {
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "token_type_ids": token_type_ids,
									}
									SCREAMING_SNAKE_CASE_:				List[str]							= model(lowerCAmelCase__)
									self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.vocab_size))




			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.prepare_config_and_inputs()

									(
									    (
									    SCREAMING_SNAKE_CASE_
									)	,					(
									    SCREAMING_SNAKE_CASE_
									)	,					(
									    SCREAMING_SNAKE_CASE_
									)	,					(
									    SCREAMING_SNAKE_CASE_
									)	,					(
									    SCREAMING_SNAKE_CASE_
									)	,					(
									    SCREAMING_SNAKE_CASE_
									)	,					(
									    SCREAMING_SNAKE_CASE_
									)	,					(
									    SCREAMING_SNAKE_CASE_
									)	,					(
									    SCREAMING_SNAKE_CASE_
									)	,					
									):				Optional[int]							= config_and_inputs

									SCREAMING_SNAKE_CASE_:				Optional[int]							= {
									    "input_ids": input_ids,
									    "token_type_ids": token_type_ids,
									    "attention_mask": input_mask,
									}
									return config, inputs_dict







@require_tf
class 						__lowercase       (				UpperCAmelCase_	, UpperCAmelCase_	, UpperCAmelCase_	, unittest.TestCase			):




			"""simple docstring"""







			_UpperCAmelCase     :							List[str]    							=  (
			    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
			    if is_tf_available()
			    else ()
			)

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



			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict     ,		lowerCAmelCase__						:  Dict     ,		lowerCAmelCase__						:  List[str]     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  List[Any]     ,		lowerCAmelCase__						:  Optional[Any]):
									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  _SCREAMING_SNAKE_CASE    (				self						:  int):
									SCREAMING_SNAKE_CASE_:				List[Any]							= TFGPTJModelTester(self)
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= ConfigTester(self     ,		config_class=lowerCAmelCase__     ,		n_embd=37)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]):
									self.config_tester.run_common_tests()



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_gptj_model(*lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_gptj_model_past(*lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  str):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_gptj_model_attention_mask_past(*lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]):
									SCREAMING_SNAKE_CASE_:				Tuple							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_gptj_model_past_large_inputs(*lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]):
									SCREAMING_SNAKE_CASE_:				Any							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_gptj_lm_head_model(*lowerCAmelCase__)



			@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  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									SCREAMING_SNAKE_CASE_:				str							= TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B"     ,		from_pt=lowerCAmelCase__)
									self.assertIsNotNone(lowerCAmelCase__)




			@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor.")
			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]):
									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 						__lowercase       (				unittest.TestCase			):




			"""simple docstring"""







			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									SCREAMING_SNAKE_CASE_:				Dict							= TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"     ,		from_pt=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Any							= 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
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 1_5185, 11, 290, 340, 318, 257, 1545]
									# fmt: on
									SCREAMING_SNAKE_CASE_:				Dict							= model.generate(lowerCAmelCase__     ,		do_sample=lowerCAmelCase__)
									self.assertListEqual(output_ids[0].numpy().tolist()     ,		lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]):
									SCREAMING_SNAKE_CASE_:				Tuple							= AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"     ,		revision="float16")
									SCREAMING_SNAKE_CASE_:				str							= TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"     ,		revision="float16"     ,		from_pt=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= 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"):
															SCREAMING_SNAKE_CASE_:				Optional[int]							= model.generate(**lowerCAmelCase__     ,		do_sample=lowerCAmelCase__     ,		seed=[42, 0])
									SCREAMING_SNAKE_CASE_:				Dict							= tokenizer.decode(output_ids[0]     ,		skip_special_tokens=lowerCAmelCase__)

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



			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									SCREAMING_SNAKE_CASE_:				Tuple							= TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"     ,		revision="float16"     ,		from_pt=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Optional[int]							= AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"     ,		revision="float16")

									SCREAMING_SNAKE_CASE_:				int							= "left"

									# Define PAD Token = EOS Token = 50256
									SCREAMING_SNAKE_CASE_:				int							= tokenizer.eos_token
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= model.config.eos_token_id

									# use different length sentences to test batching
									SCREAMING_SNAKE_CASE_:				List[str]							= [
									    "Hello, my dog is a little",
									    "Today, I",
									]
									SCREAMING_SNAKE_CASE_:				List[str]							= [
									    "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  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									# Confirms that we get the expected results with left-padded beam search
									SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Any							= self._get_beam_search_test_objects()

									SCREAMING_SNAKE_CASE_:				int							= tokenizer(lowerCAmelCase__     ,		return_tensors="tf"     ,		padding=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				List[str]							= model.generate(**lowerCAmelCase__     ,		do_sample=lowerCAmelCase__     ,		num_beams=2)
									SCREAMING_SNAKE_CASE_:				List[str]							= tokenizer.batch_decode(lowerCAmelCase__     ,		skip_special_tokens=lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__     ,		lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict):
									# Confirms that left-padding is working properly
									SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Optional[int]							= self._get_beam_search_test_objects()

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= tokenizer(lowerCAmelCase__     ,		return_tensors="tf"     ,		padding=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				List[str]							= tokenizer(sentences[0]     ,		return_tensors="tf")
									SCREAMING_SNAKE_CASE_:				Dict							= model.generate(**lowerCAmelCase__     ,		do_sample=lowerCAmelCase__     ,		num_beams=2)
									SCREAMING_SNAKE_CASE_:				int							= (
									    shape_list(inputs_non_padded["input_ids"])[-1]
									    - tf.reduce_sum(tf.cast(inputs["attention_mask"][-1]     ,		tf.intaa)).numpy()
									)
									SCREAMING_SNAKE_CASE_:				Any							= tokenizer(sentences[1]     ,		return_tensors="tf")
									SCREAMING_SNAKE_CASE_:				Dict							= model.generate(
									    **lowerCAmelCase__     ,		do_sample=lowerCAmelCase__     ,		num_beams=2     ,		max_length=model.config.max_length - num_paddings)
									SCREAMING_SNAKE_CASE_:				Dict							= tokenizer.decode(output_non_padded[0]     ,		skip_special_tokens=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Tuple							= tokenizer.decode(output_padded[0]     ,		skip_special_tokens=lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__     ,		[non_padded_sentence, padded_sentence])




			def  _SCREAMING_SNAKE_CASE    (				self						:  Any):
									# Confirms that XLA is working properly
									SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				int							= self._get_beam_search_test_objects()

									SCREAMING_SNAKE_CASE_:				str							= tokenizer(lowerCAmelCase__     ,		return_tensors="tf"     ,		padding=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= tf.function(model.generate     ,		jit_compile=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= xla_generate(**lowerCAmelCase__     ,		do_sample=lowerCAmelCase__     ,		num_beams=2)
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= tokenizer.batch_decode(lowerCAmelCase__     ,		skip_special_tokens=lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__     ,		lowerCAmelCase__)









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							UNetaDModel
			_UpperCAmelCase     :							PNDMScheduler



			def __init__(				self						:  Optional[int]     ,		lowerCAmelCase__						:  UNetaDModel     ,		lowerCAmelCase__						:  PNDMScheduler):
									super().__init__()

									SCREAMING_SNAKE_CASE_:				Dict							= PNDMScheduler.from_config(scheduler.config)

									self.register_modules(unet=lowerCAmelCase__     ,		scheduler=lowerCAmelCase__)




			@torch.no_grad()
			def __call__(				self						:  int     ,		lowerCAmelCase__						:  int = 1     ,		lowerCAmelCase__						:  int = 50     ,		lowerCAmelCase__						:  Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		lowerCAmelCase__						:  Optional[str] = "pil"     ,		lowerCAmelCase__						:  bool = True     ,		**lowerCAmelCase__						:  str     ,		):
									SCREAMING_SNAKE_CASE_:				Dict							= randn_tensor(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,		generator=lowerCAmelCase__     ,		device=self.device     ,		)

									self.scheduler.set_timesteps(lowerCAmelCase__)
									for t in self.progress_bar(self.scheduler.timesteps):
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.unet(lowerCAmelCase__     ,		lowerCAmelCase__).sample

															SCREAMING_SNAKE_CASE_:				Any							= self.scheduler.step(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__).prev_sample

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= (image / 2 + 0.5).clamp(0     ,		1)
									SCREAMING_SNAKE_CASE_:				Dict							= image.cpu().permute(0     ,		2     ,		3     ,		1).numpy()
									if output_type == "pil":
															SCREAMING_SNAKE_CASE_:				Any							= self.numpy_to_pil(lowerCAmelCase__)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowerCAmelCase__)






