



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







					def 			_lowercase      (							self   :  Union[str, Any]):
											A__						:				int         =			ids_tensor([self.batch_size, self.seq_length]      ,		self.vocab_size)

											A__						:				Any         =			None
											if self.use_input_mask:
																	A__						:				Tuple         =			random_attention_mask([self.batch_size, self.seq_length])

											A__						:				List[Any]         =			None
											if self.use_token_type_ids:
																	A__						:				Optional[int]         =			ids_tensor([self.batch_size, self.seq_length]      ,		self.type_vocab_size)

											A__						:				int         =			None
											if self.use_mc_token_ids:
																	A__						:				Optional[Any]         =			ids_tensor([self.batch_size, self.num_choices]      ,		self.seq_length)

											A__						:				Dict         =			None
											A__						:				Any         =			None
											A__						:				str         =			None
											if self.use_labels:
																	A__						:				Dict         =			ids_tensor([self.batch_size]      ,		self.type_sequence_label_size)
																	A__						:				List[str]         =			ids_tensor([self.batch_size, self.seq_length]      ,		self.num_labels)
																	A__						:				List[str]         =			ids_tensor([self.batch_size]      ,		self.num_choices)

											A__						:				List[Any]         =			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=_A      ,		)

											A__						:				Any         =			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 			_lowercase      (							self   :  Optional[Any]      ,		_A   :  Optional[Any]      ,		_A   :  List[str]      ,		_A   :  Optional[Any]      ,		_A   :  List[Any]      ,		_A   :  List[Any]      ,		*_A   :  Any):
											A__						:				List[str]         =			TFGPTJModel(config=_A)
											A__						:				str         =			{
											    "input_ids": input_ids,
											    "attention_mask": input_mask,
											    "token_type_ids": token_type_ids,
											}
											A__						:				List[Any]         =			model(_A)

											A__						:				List[Any]         =			[input_ids, None, input_mask]  # None is the input for 'past'
											A__						:				Tuple         =			model(_A)

											A__						:				List[Any]         =			model(_A)

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







					def 			_lowercase      (							self   :  List[str]      ,		_A   :  Dict      ,		_A   :  List[Any]      ,		_A   :  List[str]      ,		_A   :  Tuple      ,		_A   :  Dict      ,		*_A   :  str):
											A__						:				Dict         =			TFGPTJModel(config=_A)

											# first forward pass
											A__						:				Union[str, Any]         =			model(_A      ,		token_type_ids=_A      ,		use_cache=_A)
											A__						:				Optional[Any]         =			model(_A      ,		token_type_ids=_A)
											A__						:				Tuple         =			model(_A      ,		token_type_ids=_A      ,		use_cache=_A)

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

											A__     ,							A__						:				List[Any]         =			outputs.to_tuple()

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

											# append to next input_ids and token_type_ids
											A__						:				Tuple         =			tf.concat([input_ids, next_tokens]      ,		axis=-1)
											A__						:				List[Any]         =			tf.concat([token_type_ids, next_token_types]      ,		axis=-1)

											A__						:				Tuple         =			model(_A      ,		token_type_ids=_A)["last_hidden_state"]
											A__						:				Dict         =			model(_A      ,		token_type_ids=_A      ,		past_key_values=_A)[
											    "last_hidden_state"
											]

											# select random slice
											A__						:				Optional[Any]         =			int(ids_tensor((1,)      ,		shape_list(_A)[-1]))
											A__						:				Union[str, Any]         =			output_from_no_past[:, -1, random_slice_idx]
											A__						:				Tuple         =			output_from_past[:, 0, random_slice_idx]

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







					def 			_lowercase      (							self   :  Dict      ,		_A   :  Union[str, Any]      ,		_A   :  Dict      ,		_A   :  int      ,		_A   :  int      ,		_A   :  Tuple      ,		*_A   :  Tuple):
											A__						:				str         =			TFGPTJModel(config=_A)

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

											# first forward pass
											A__     ,							A__						:				Optional[Any]         =			model(_A      ,		attention_mask=_A).to_tuple()

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

											# change a random masked slice from input_ids
											A__						:				Dict         =			ids_tensor((1,)      ,		_A).numpy() + 1
											A__						:				List[str]         =			ids_tensor((self.batch_size, self.seq_length)      ,		config.vocab_size)
											A__						:				str         =			tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
											A__						:				str         =			tf.transpose(
											    tf.broadcast_to(tf.expand_dims(_A      ,		-1)      ,		(self.seq_length, self.batch_size)))
											A__						:				str         =			tf.where(_A      ,		_A      ,		_A)

											# append to next input_ids and attn_mask
											A__						:				Optional[int]         =			tf.concat([input_ids, next_tokens]      ,		axis=-1)
											A__						:				Tuple         =			tf.concat([attn_mask, tf.ones((shape_list(_A)[0], 1)      ,		dtype=tf.intaa)]      ,		axis=1)

											# get two different outputs
											A__						:				Optional[Any]         =			model(_A      ,		attention_mask=_A)["last_hidden_state"]
											A__						:				List[Any]         =			model(_A      ,		past_key_values=_A      ,		attention_mask=_A)[
											    "last_hidden_state"
											]

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

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







					def 			_lowercase      (							self   :  str      ,		_A   :  Tuple      ,		_A   :  Optional[int]      ,		_A   :  Any      ,		_A   :  Optional[Any]      ,		_A   :  Optional[Any]      ,		*_A   :  Union[str, Any]):
											A__						:				Dict         =			TFGPTJModel(config=_A)

											A__						:				Union[str, Any]         =			input_ids[:1, :]
											A__						:				Union[str, Any]         =			input_mask[:1, :]
											A__						:				Any         =			token_type_ids[:1, :]
											A__						:				Dict         =			1

											# first forward pass
											A__						:				Any         =			model(_A      ,		attention_mask=_A      ,		token_type_ids=_A      ,		use_cache=_A)

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

											# create hypothetical next token and extent to next_input_ids
											A__						:				Union[str, Any]         =			ids_tensor((self.batch_size, 3)      ,		config.vocab_size)
											A__						:				Any         =			ids_tensor((self.batch_size, 3)      ,		2)
											A__						:				int         =			ids_tensor((self.batch_size, 3)      ,		self.type_vocab_size)

											# append to next input_ids and token_type_ids
											A__						:				Tuple         =			tf.concat([input_ids, next_tokens]      ,		axis=-1)
											A__						:				Union[str, Any]         =			tf.concat([input_mask, next_attn_mask]      ,		axis=-1)
											A__						:				Dict         =			tf.concat([token_type_ids, next_token_types]      ,		axis=-1)

											A__						:				int         =			model(
											    _A      ,		token_type_ids=_A      ,		attention_mask=_A)["last_hidden_state"]
											A__						:				Tuple         =			model(
											    _A      ,		token_type_ids=_A      ,		attention_mask=_A      ,		past_key_values=_A      ,		)["last_hidden_state"]
											self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])

											# select random slice
											A__						:				Dict         =			int(ids_tensor((1,)      ,		shape_list(_A)[-1]))
											A__						:				int         =			output_from_no_past[:, -3:, random_slice_idx]
											A__						:				List[str]         =			output_from_past[:, :, random_slice_idx]

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







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







					def 			_lowercase      (							self   :  str):
											A__						:				List[str]         =			self.prepare_config_and_inputs()

											(
											    (
											    A__
											)     ,							(
											    A__
											)     ,							(
											    A__
											)     ,							(
											    A__
											)     ,							(
											    A__
											)     ,							(
											    A__
											)     ,							(
											    A__
											)     ,							(
											    A__
											)     ,							(
											    A__
											)     ,							
											)						:				Dict         =			config_and_inputs

											A__						:				List[str]         =			{
											    "input_ids": input_ids,
											    "token_type_ids": token_type_ids,
											    "attention_mask": input_mask,
											}
											return config, inputs_dict




@require_tf
class 				lowerCAmelCase__	(						UpperCamelCase       ,				UpperCamelCase       ,				UpperCamelCase       ,				unittest.TestCase    ):
					__A  :     Optional[Any]     =    (
					    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
					    if is_tf_available()
					    else ()
					)

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







					def 			_lowercase      (							self   :  str      ,		_A   :  List[Any]      ,		_A   :  Any      ,		_A   :  Any      ,		_A   :  Any      ,		_A   :  str):
											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 			_lowercase      (							self   :  int):
											A__						:				Optional[int]         =			TFGPTJModelTester(self)
											A__						:				Optional[Any]         =			ConfigTester(self      ,		config_class=_A      ,		n_embd=37)







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







					def 			_lowercase      (							self   :  Union[str, Any]):
											A__						:				str         =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_gptj_model(*_A)







					def 			_lowercase      (							self   :  List[str]):
											A__						:				str         =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_gptj_model_past(*_A)







					def 			_lowercase      (							self   :  Tuple):
											A__						:				Any         =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_gptj_model_attention_mask_past(*_A)







					def 			_lowercase      (							self   :  Any):
											A__						:				Dict         =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_gptj_model_past_large_inputs(*_A)







					def 			_lowercase      (							self   :  str):
											A__						:				List[Any]         =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_gptj_lm_head_model(*_A)







					@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 			_lowercase      (							self   :  Optional[int]):
											A__						:				Optional[int]         =			TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B"      ,		from_pt=_A)
											self.assertIsNotNone(_A)







					@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor.")
					def 			_lowercase      (							self   :  Optional[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 				lowerCAmelCase__	(						unittest.TestCase    ):







					def 			_lowercase      (							self   :  Union[str, Any]):
											A__						:				Optional[Any]         =			TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"      ,		from_pt=_A)
											A__						:				int         =			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__						:				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
											A__						:				str         =			model.generate(_A      ,		do_sample=_A)
											self.assertListEqual(output_ids[0].numpy().tolist()      ,		_A)







					def 			_lowercase      (							self   :  str):
											A__						:				Union[str, Any]         =			AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"      ,		revision="float16")
											A__						:				int         =			TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"      ,		revision="float16"      ,		from_pt=_A)

											A__						:				List[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"):
																	A__						:				List[Any]         =			model.generate(**_A      ,		do_sample=_A      ,		seed=[42, 0])
											A__						:				List[str]         =			tokenizer.decode(output_ids[0]      ,		skip_special_tokens=_A)

											A__						:				Union[str, Any]         =			"Today is a nice day and I’m going to go for a walk. I’"
											self.assertEqual(_A      ,		_A)







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

											A__						:				Tuple         =			"left"

											# Define PAD Token = EOS Token = 50256
											A__						:				Optional[int]         =			tokenizer.eos_token
											A__						:				Any         =			model.config.eos_token_id

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

											A__						:				List[Any]         =			tokenizer(_A      ,		return_tensors="tf"      ,		padding=_A)
											A__						:				Tuple         =			model.generate(**_A      ,		do_sample=_A      ,		num_beams=2)
											A__						:				Union[str, Any]         =			tokenizer.batch_decode(_A      ,		skip_special_tokens=_A)
											self.assertListEqual(_A      ,		_A)







					def 			_lowercase      (							self   :  str):
											# Confirms that left-padding is working properly
											A__     ,							A__     ,							A__     ,							A__						:				Dict         =			self._get_beam_search_test_objects()

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







					def 			_lowercase      (							self   :  List[str]):
											# Confirms that XLA is working properly
											A__     ,							A__     ,							A__     ,							A__						:				Optional[Any]         =			self._get_beam_search_test_objects()

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




def 						snake_case__							(       __lowercase = 1_0_0_0							)		->		int:







						"""simple docstring"""

						A__						:				int         =			0
						A__						:				Optional[int]         =			0
						while 1:
												num += 3
												if num >= n:
																		break
												total += num
												num += 2
												if num >= n:
																		break
												total += num
												num += 1
												if num >= n:
																		break
												total += num
												num += 3
												if num >= n:
																		break
												total += num
												num += 1
												if num >= n:
																		break
												total += num
												num += 2
												if num >= n:
																		break
												total += num
												num += 3
												if num >= n:
																		break
												total += num
						return total


if __name__ == "__main__":
		print(f"""{solution() = }""")