
from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				return {key.lstrip("-"    ): value for key, value in zip(unknown_args[::2]	, unknown_args[1::2]    )}





def 			lowerCamelCase_ (				):
				lowerCamelCase_						=			ArgumentParser(
				    "HuggingFace Datasets CLI tool"	, usage="datasets-cli <command> [<args>]"	, allow_abbrev=lowerCamelCase__    )
				lowerCamelCase_						=			parser.add_subparsers(help="datasets-cli command helpers"    )
				set_verbosity_info()

				# Register commands
				ConvertCommand.register_subcommand(lowerCamelCase__    )
				EnvironmentCommand.register_subcommand(lowerCamelCase__    )
				TestCommand.register_subcommand(lowerCamelCase__    )
				RunBeamCommand.register_subcommand(lowerCamelCase__    )
				DummyDataCommand.register_subcommand(lowerCamelCase__    )

				# Parse args
				lowerCamelCase_	,    lowerCamelCase_						=			parser.parse_known_args()
				if not hasattr(lowerCamelCase__	, "func"    ):
								parser.print_help()
								exit(1    )
				lowerCamelCase_						=			parse_unknown_args(lowerCamelCase__    )

				# Run
				lowerCamelCase_						=			args.func(lowerCamelCase__	, **lowerCamelCase__    )
				service.run()


if __name__ == "__main__":
			main()




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     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase						,  lowercase=13						,  lowercase=7						,  lowercase=True						,  lowercase=True						,  lowercase=True						,  lowercase=99						,  lowercase=32						,  lowercase=5						,  lowercase=4						,  lowercase=37						,  lowercase="gelu"						,  lowercase=0.1						,  lowercase=0.1						,  lowercase=512						,  lowercase=16						,  lowercase=2						,  lowercase=0.0_2						,  lowercase=3						,  lowercase=4						,  lowercase=None						,  )				->       List[str]:
					lowerCamelCase_						=			parent
					lowerCamelCase_						=			batch_size
					lowerCamelCase_						=			seq_length
					lowerCamelCase_						=			is_training
					lowerCamelCase_						=			use_token_type_ids
					lowerCamelCase_						=			use_labels
					lowerCamelCase_						=			vocab_size
					lowerCamelCase_						=			hidden_size
					lowerCamelCase_						=			num_hidden_layers
					lowerCamelCase_						=			num_attention_heads
					lowerCamelCase_						=			intermediate_size
					lowerCamelCase_						=			hidden_act
					lowerCamelCase_						=			hidden_dropout_prob
					lowerCamelCase_						=			attention_probs_dropout_prob
					lowerCamelCase_						=			max_position_embeddings
					lowerCamelCase_						=			type_vocab_size
					lowerCamelCase_						=			type_sequence_label_size
					lowerCamelCase_						=			initializer_range
					lowerCamelCase_						=			num_labels
					lowerCamelCase_						=			num_choices
					lowerCamelCase_						=			scope
					lowerCamelCase_						=			self.vocab_size - 1



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					lowerCamelCase_						=			ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size )

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

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

					lowerCamelCase_						=			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						,  )

					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  *lowercase )				->       Dict:
					lowerCamelCase_						=			OpenAIGPTModel(config=lowercase )
					model.to(lowercase )
					model.eval()

					lowerCamelCase_						=			model(lowercase						,  token_type_ids=lowercase						,  head_mask=lowercase )
					lowerCamelCase_						=			model(lowercase						,  token_type_ids=lowercase )
					lowerCamelCase_						=			model(lowercase )

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  *lowercase )				->       int:
					lowerCamelCase_						=			OpenAIGPTLMHeadModel(lowercase )
					model.to(lowercase )
					model.eval()

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  *lowercase )				->       Dict:
					lowerCamelCase_						=			OpenAIGPTDoubleHeadsModel(lowercase )
					model.to(lowercase )
					model.eval()

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  *lowercase )				->       int:
					lowerCamelCase_						=			self.num_labels
					lowerCamelCase_						=			OpenAIGPTForSequenceClassification(lowercase )
					model.to(lowercase )
					model.eval()

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			self.prepare_config_and_inputs()
					(
					    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    
					)						=			config_and_inputs
					lowerCamelCase_						=			{
					    "input_ids": input_ids,
					    "token_type_ids": token_type_ids,
					    "head_mask": head_mask,
					}

					return config, inputs_dict




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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       int:
					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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase=False )				->       Any:
					lowerCamelCase_						=			super()._prepare_for_class(lowercase						,  lowercase						,  return_labels=lowercase )

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			OpenAIGPTModelTester(self )
					lowerCamelCase_						=			ConfigTester(self						,  config_class=lowercase						,  n_embd=37 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					self.config_tester.run_common_tests()



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_openai_gpt_model(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_lm_head_model(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_double_lm_head_model(*lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase )



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_						=			OpenAIGPTModel.from_pretrained(lowercase )
									self.assertIsNotNone(lowercase )




@require_torch
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			OpenAIGPTLMHeadModel.from_pretrained("openai-gpt" )
					model.to(lowercase )
					lowerCamelCase_						=			torch.tensor([[481, 4735, 544]]						,  dtype=torch.long						,  device=lowercase )  # the president is
					lowerCamelCase_						=			[
					    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

					lowerCamelCase_						=			model.generate(lowercase						,  do_sample=lowercase )
					self.assertListEqual(output_ids[0].tolist()						,  lowercase )


