
'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    OpenAIGPTConfig,
					    OpenAIGPTDoubleHeadsModel,
					    OpenAIGPTForSequenceClassification,
					    OpenAIGPTLMHeadModel,
					    OpenAIGPTModel,
					)


class 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_=13					,     lowerCAmelCase_=7					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=True					,     lowerCAmelCase_=99					,     lowerCAmelCase_=32					,     lowerCAmelCase_=5					,     lowerCAmelCase_=4					,     lowerCAmelCase_=37					,     lowerCAmelCase_="gelu"					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_=5_12					,     lowerCAmelCase_=16					,     lowerCAmelCase_=2					,     lowerCAmelCase_=0.02					,     lowerCAmelCase_=3					,     lowerCAmelCase_=4					,     lowerCAmelCase_=None					,     ):





								"""simple docstring"""






								_snake_case     		=	parent
								_snake_case     		=	batch_size
								_snake_case     		=	seq_length
								_snake_case     		=	is_training
								_snake_case     		=	use_token_type_ids
								_snake_case     		=	use_labels
								_snake_case     		=	vocab_size
								_snake_case     		=	hidden_size
								_snake_case     		=	num_hidden_layers
								_snake_case     		=	num_attention_heads
								_snake_case     		=	intermediate_size
								_snake_case     		=	hidden_act
								_snake_case     		=	hidden_dropout_prob
								_snake_case     		=	attention_probs_dropout_prob
								_snake_case     		=	max_position_embeddings
								_snake_case     		=	type_vocab_size
								_snake_case     		=	type_sequence_label_size
								_snake_case     		=	initializer_range
								_snake_case     		=	num_labels
								_snake_case     		=	num_choices
								_snake_case     		=	scope
								_snake_case     		=	self.vocab_size - 1


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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

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

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

								_snake_case     		=	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					,     )

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

								return (
								    config,
								    input_ids,
								    head_mask,
								    token_type_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     *lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTModel(config=lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()

								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     head_mask=lowerCAmelCase_						)
								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_						)
								_snake_case     		=	model(lowerCAmelCase_						)

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


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     *lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTLMHeadModel(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()

								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     labels=lowerCAmelCase_						)
								self.parent.assertEqual(result.loss.shape					,     ()						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.vocab_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     *lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTDoubleHeadsModel(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()

								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     labels=lowerCAmelCase_						)
								self.parent.assertEqual(result.loss.shape					,     ()						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.vocab_size)						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     *lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	self.num_labels
								_snake_case     		=	OpenAIGPTForSequenceClassification(lowerCAmelCase_						)
								model.to(lowerCAmelCase_						)
								model.eval()

								_snake_case     		=	ids_tensor([self.batch_size]					,     self.type_sequence_label_size						)
								_snake_case     		=	model(lowerCAmelCase_					,     token_type_ids=lowerCAmelCase_					,     labels=lowerCAmelCase_						)
								self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_labels)						)




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.prepare_config_and_inputs()
								(
								    (
								    _snake_case				
								)		,				(
								    _snake_case				
								)		,				(
								    _snake_case				
								)		,				(
								    _snake_case				
								)		,				(
								    _snake_case				
								)		,				(
								    _snake_case				
								)		,				(
								    _snake_case				
								)		,								
								)     		=	config_and_inputs
								_snake_case     		=	{
								    'input_ids': input_ids,
								    'token_type_ids': token_type_ids,
								    'head_mask': head_mask,
								}

								return config, inputs_dict


@require_torch
class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		,     _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= (
							    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
							    if is_torch_available()
							    else ()
							)
							__lowercase   	= (
							    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
							)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
							__lowercase   	= (
							    {
							        """feature-extraction""": OpenAIGPTModel,
							        """text-classification""": OpenAIGPTForSequenceClassification,
							        """text-generation""": OpenAIGPTLMHeadModel,
							        """zero-shot""": OpenAIGPTForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								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					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_=False						):





								"""simple docstring"""






								_snake_case     		=	super()._prepare_for_class(lowerCAmelCase_					,     lowerCAmelCase_					,     return_labels=lowerCAmelCase_						)

								if return_labels:
									if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
										_snake_case     		=	torch.zeros(
										    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)					,     dtype=torch.long					,     device=lowerCAmelCase_					,     )
										_snake_case     		=	inputs_dict['labels']
										_snake_case     		=	inputs_dict['labels']
										_snake_case     		=	torch.zeros(
										    (self.model_tester.batch_size, self.model_tester.num_choices)					,     dtype=torch.long					,     device=lowerCAmelCase_					,     )
										_snake_case     		=	torch.zeros(
										    self.model_tester.batch_size					,     dtype=torch.long					,     device=lowerCAmelCase_						)
								return inputs_dict


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTModelTester(self						)
								_snake_case     		=	ConfigTester(self					,     config_class=lowerCAmelCase_					,     n_embd=37						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								self.config_tester.run_common_tests()


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_double_lm_head_model(*lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase_						)




							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_snake_case     		=	OpenAIGPTModel.from_pretrained(lowerCAmelCase_						)
									self.assertIsNotNone(lowerCAmelCase_						)


@require_torch
class 		__UpperCAmelCase   (      unittest.TestCase		):


							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'						)
								model.to(lowerCAmelCase_						)
								_snake_case     		=	torch.tensor([[4_81, 47_35, 5_44]]					,     dtype=torch.long					,     device=lowerCAmelCase_						)  # the president is
								_snake_case     		=	[
								    4_81,
								    47_35,
								    5_44,
								    2_46,
								    9_63,
								    8_70,
								    7_62,
								    2_39,
								    2_44,
								    4_04_77,
								    2_44,
								    2_49,
								    7_19,
								    8_81,
								    4_87,
								    5_44,
								    2_40,
								    2_44,
								    6_03,
								    4_81,
								]  # the president is a very good man. " \n " i\'m sure he is, " said the

								_snake_case     		=	model.generate(lowerCAmelCase_					,     do_sample=lowerCAmelCase_						)
								self.assertListEqual(output_ids[0].tolist()					,     lowerCAmelCase_						)



'''simple docstring'''







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowercase					:							str   =      version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
					from nltk import word_tokenize


lowercase					:							int   =      "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

lowercase					:							Any   =      "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowercase					:							Optional[Any]   =      "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,     _KWARGS_DESCRIPTION		)
class 		__UpperCAmelCase   (      datasets.Metric		):


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return datasets.MetricInfo(
								    description=_DESCRIPTION					,     citation=_CITATION					,     inputs_description=_KWARGS_DESCRIPTION					,     features=datasets.Features(
								        {
								            'predictions': datasets.Value('string'					,     id='sequence'						),
								            'references': datasets.Value('string'					,     id='sequence'						),
								        }						)					,     codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']					,     reference_urls=[
								        'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
								        'https://en.wikipedia.org/wiki/METEOR',
								    ]					,     )


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								import nltk

								nltk.download('wordnet'						)
								if NLTK_VERSION >= version.Version('3.6.5'						):
									nltk.download('punkt'						)
								if NLTK_VERSION >= version.Version('3.6.6'						):
									nltk.download('omw-1.4'						)




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_=0.9					,     lowerCAmelCase_=3					,     lowerCAmelCase_=0.5						):





								"""simple docstring"""






								if NLTK_VERSION >= version.Version('3.6.5'						):
									_snake_case     		=	[
									    meteor_score.single_meteor_score(
									        word_tokenize(lowerCAmelCase_						)					,     word_tokenize(lowerCAmelCase_						)					,     alpha=lowerCAmelCase_					,     beta=lowerCAmelCase_					,     gamma=lowerCAmelCase_						)
									    for ref, pred in zip(lowerCAmelCase_					,     lowerCAmelCase_						)
									]
								else:
									_snake_case     		=	[
									    meteor_score.single_meteor_score(lowerCAmelCase_					,     lowerCAmelCase_					,     alpha=lowerCAmelCase_					,     beta=lowerCAmelCase_					,     gamma=lowerCAmelCase_						)
									    for ref, pred in zip(lowerCAmelCase_					,     lowerCAmelCase_						)
									]

								return {"meteor": np.mean(lowerCAmelCase_						)}

