
'''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_	:






						'''simple docstring'''






						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=0.02       ,  _lowercase=3       ,  _lowercase=4       ,  _lowercase=None       ,  ):

												"""simple docstring"""



												_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    _lowercase      (   self ):

												"""simple docstring"""



												_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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  *_lowercase ):

												"""simple docstring"""



												_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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  *_lowercase ):

												"""simple docstring"""



												_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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  *_lowercase ):

												"""simple docstring"""



												_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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  *_lowercase ):

												"""simple docstring"""



												_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    _lowercase      (   self ):

												"""simple docstring"""



												_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     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: Any										=  (
						    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
						    if is_torch_available()
						    else ()
						)
						_lowercase						: Optional[Any]										=  (
						    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
						)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
						_lowercase						: Any										=  (
						    {
						        '''feature-extraction''': OpenAIGPTModel,
						        '''text-classification''': OpenAIGPTForSequenceClassification,
						        '''text-generation''': OpenAIGPTLMHeadModel,
						        '''zero-shot''': OpenAIGPTForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase=False ):

												"""simple docstring"""



												_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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					OpenAIGPTModelTester(self )
												_lowerCAmelCase    		=					ConfigTester(self       ,  config_class=_lowercase       ,  n_embd=37 )




						def    _lowercase      (   self ):

												"""simple docstring"""



												self.config_tester.run_common_tests()




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_openai_gpt_model(*_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_lm_head_model(*_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_double_lm_head_model(*_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_lowercase )




						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



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






@require_torch
class     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt""" )
												model.to(_lowercase )
												_lowerCAmelCase    		=					torch.tensor([[481, 4_735, 544]]       ,  dtype=torch.long       ,  device=_lowercase )  # the president is
												_lowerCAmelCase    		=					[
												    481,
												    4_735,
												    544,
												    246,
												    963,
												    870,
												    762,
												    239,
												    244,
												    40_477,
												    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 )


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase          =						{"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """ViTMAEForPreTraining""",
						    """ViTMAELayer""",
						    """ViTMAEModel""",
						    """ViTMAEPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """TFViTMAEForPreTraining""",
						    """TFViTMAEModel""",
						    """TFViTMAEPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_mae import (
												    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMAEForPreTraining,
												    ViTMAELayer,
												    ViTMAEModel,
												    ViTMAEPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
						import sys

						_lowercase          =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
