






from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
					import tensorflow as tf

					from transformers.models.openai.modeling_tf_openai import (
					    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFOpenAIGPTDoubleHeadsModel,
					    TFOpenAIGPTForSequenceClassification,
					    TFOpenAIGPTLMHeadModel,
					    TFOpenAIGPTModel,
					)





class        a__      :






		def __init__(      self							, lowercase__							, )				->				Optional[Any]:
				__A    =							parent
				__A    =							13
				__A    =							7
				__A    =							True
				__A    =							True
				__A    =							True
				__A    =							True
				__A    =							True
				__A    =							99
				__A    =							32
				__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






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__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    =							OpenAIGPTConfig(
				    vocab_size=self.vocab_size							, n_embd=self.hidden_size							, n_layer=self.num_hidden_layers							, n_head=self.num_attention_heads							, n_positions=self.max_position_embeddings							, pad_token_id=self.pad_token_id							, )

				__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							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, *lowercase__						)				->				Dict:
				__A    =							TFOpenAIGPTModel(config=lowercase__						)
				__A    =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
				__A    =							model(lowercase__						)

				__A    =							[input_ids, input_mask]
				__A    =							model(lowercase__						)

				__A    =							model(lowercase__						)

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, *lowercase__						)				->				Dict:
				__A    =							TFOpenAIGPTLMHeadModel(config=lowercase__						)
				__A    =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
				__A    =							model(lowercase__						)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, *lowercase__						)				->				Optional[int]:
				__A    =							TFOpenAIGPTDoubleHeadsModel(config=lowercase__						)

				__A    =							tf.tile(tf.expand_dims(lowercase__							, 1						)							, (1, self.num_choices, 1)						)
				__A    =							tf.tile(tf.expand_dims(lowercase__							, 1						)							, (1, self.num_choices, 1)						)
				__A    =							tf.tile(tf.expand_dims(lowercase__							, 1						)							, (1, self.num_choices, 1)						)

				__A    =							{
				    "input_ids": multiple_choice_inputs_ids,
				    "mc_token_ids": mc_token_ids,
				    "attention_mask": multiple_choice_input_mask,
				    "token_type_ids": multiple_choice_token_type_ids,
				}
				__A    =							model(lowercase__						)
				self.parent.assertEqual(
				    result.logits.shape							, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size)						)
				self.parent.assertEqual(result.mc_logits.shape							, (self.batch_size, self.num_choices)						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, *lowercase__						)				->				Union[str, Any]:
				__A    =							self.num_labels
				__A    =							ids_tensor([self.batch_size]							, self.type_sequence_label_size						)
				__A    =							{
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				    "labels": sequence_labels,
				}
				__A    =							TFOpenAIGPTForSequenceClassification(lowercase__						)
				__A    =							model(lowercase__						)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_labels)						)






		def 					_lowerCamelCase					(      self						)				->				str:
				__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        a__      (	lowerCAmelCase__	,       lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Union[str, Any] 					= (
		    (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
		    if is_tf_available()
		    else ()
		)
		UpperCAmelCase_							:       Optional[int] 					= (
		    (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
		)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
		UpperCAmelCase_							:       List[Any] 					= (
		    {
		        'feature-extraction': TFOpenAIGPTModel,
		        'text-classification': TFOpenAIGPTForSequenceClassification,
		        'text-generation': TFOpenAIGPTLMHeadModel,
		        'zero-shot': TFOpenAIGPTForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		UpperCAmelCase_							:       List[Any] 					= False
		UpperCAmelCase_							:       List[Any] 					= False






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, lowercase__						)				->				Dict:
				if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
						# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
						# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
						# tiny config could not be created.
						return True

				return False






		def 					_lowerCamelCase					(      self						)				->				Dict:
				__A    =							TFOpenAIGPTModelTester(self						)
				__A    =							ConfigTester(self							, config_class=lowercase__							, n_embd=37						)






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				self.config_tester.run_common_tests()






		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_openai_gpt_model(*lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_openai_gpt_lm_head(*lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				Dict:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_openai_gpt_double_head(*lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase__						)






		@slow
		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__A    =							TFOpenAIGPTModel.from_pretrained(lowercase__						)
						self.assertIsNotNone(lowercase__						)





@require_tf
class        a__      (	unittest.TestCase		):






		@slow
		def 					_lowerCamelCase					(      self						)				->				Dict:
				__A    =							TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt"						)
				__A    =							tf.convert_to_tensor([[481, 4735, 544]]							, dtype=tf.intaa						)  # the president is
				__A    =							[
				    481,
				    4735,
				    544,
				    246,
				    963,
				    870,
				    762,
				    239,
				    244,
				    40477,
				    244,
				    249,
				    719,
				    881,
				    487,
				    544,
				    240,
				    244,
				    603,
				    481,
				]  # the president is a very good man. " \n " i\'m sure he is, " said the

				__A    =							model.generate(lowercase__							, do_sample=lowercase__						)
				self.assertListEqual(output_ids[0].numpy().tolist()							, lowercase__						)









def __A       ( __lowerCAmelCase = 100					,						__lowerCAmelCase = 100      )->    int:





						"""simple docstring"""



						return max(
						    sum(int(__lowerCAmelCase      ) for x in str(base**power      )      )
						    for base in range(__lowerCAmelCase      )
						    for power in range(__lowerCAmelCase      )      )


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()
