




'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase  =			logging.get_logger(__name__)

_lowercase  =			{
    """facebook/timesformer""": """https://huggingface.co/facebook/timesformer/resolve/main/config.json""",
}
class 						a_				(			UpperCAmelCase__						):
	lowercase_       :     List[str]			  =      '''timesformer'''




	def __init__(       self     :  Dict       ,		__lowerCAmelCase     :  Optional[int]=2_2_4       ,		__lowerCAmelCase     :  List[Any]=1_6       ,		__lowerCAmelCase     :  List[Any]=3       ,		__lowerCAmelCase     :  List[str]=8       ,		__lowerCAmelCase     :  Union[str, Any]=7_6_8       ,		__lowerCAmelCase     :  List[str]=1_2       ,		__lowerCAmelCase     :  str=1_2       ,		__lowerCAmelCase     :  Union[str, Any]=3_0_7_2       ,		__lowerCAmelCase     :  Optional[Any]="gelu"       ,		__lowerCAmelCase     :  Tuple=0.0       ,		__lowerCAmelCase     :  str=0.0       ,		__lowerCAmelCase     :  Optional[int]=0.02       ,		__lowerCAmelCase     :  Optional[int]=1E-6       ,		__lowerCAmelCase     :  Any=True       ,		__lowerCAmelCase     :  Tuple="divided_space_time"       ,		__lowerCAmelCase     :  Dict=0       ,		**__lowerCAmelCase     :  Union[str, Any]       ,		):
							super().__init__(**__lowerCAmelCase      )

							__snake_case						 =   image_size
							__snake_case						 =   patch_size
							__snake_case						 =   num_channels
							__snake_case						 =   num_frames

							__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						 =   initializer_range
							__snake_case						 =   layer_norm_eps
							__snake_case						 =   qkv_bias

							__snake_case						 =   attention_type
							__snake_case						 =   drop_path_rate







'''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 						a_				:




	def __init__(       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[int]=1_3       ,		__lowerCAmelCase     :  Tuple=7       ,		__lowerCAmelCase     :  Optional[int]=True       ,		__lowerCAmelCase     :  Any=True       ,		__lowerCAmelCase     :  List[Any]=True       ,		__lowerCAmelCase     :  Union[str, Any]=9_9       ,		__lowerCAmelCase     :  Any=3_2       ,		__lowerCAmelCase     :  Tuple=5       ,		__lowerCAmelCase     :  int=4       ,		__lowerCAmelCase     :  List[str]=3_7       ,		__lowerCAmelCase     :  List[Any]="gelu"       ,		__lowerCAmelCase     :  Union[str, Any]=0.1       ,		__lowerCAmelCase     :  int=0.1       ,		__lowerCAmelCase     :  Any=5_1_2       ,		__lowerCAmelCase     :  Dict=1_6       ,		__lowerCAmelCase     :  Dict=2       ,		__lowerCAmelCase     :  Union[str, Any]=0.02       ,		__lowerCAmelCase     :  Optional[Any]=3       ,		__lowerCAmelCase     :  str=4       ,		__lowerCAmelCase     :  List[str]=None       ,		):
							__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   lowercase__   (       self     :  Union[str, Any]      ):
							__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   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Union[str, Any]       ,		*__lowerCAmelCase     :  List[str]      ):
							__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   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Dict       ,		*__lowerCAmelCase     :  str      ):
							__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   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  List[Any]       ,		*__lowerCAmelCase     :  Tuple      ):
							__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   lowercase__   (       self     :  Optional[int]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  int       ,		*__lowerCAmelCase     :  int      ):
							__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   lowercase__   (       self     :  List[str]      ):
							__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 						a_				(			UpperCAmelCase__     ,						UpperCAmelCase__     ,						UpperCAmelCase__     ,						unittest.TestCase						):
	lowercase_       :     List[str]			  =      (
	    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
	    if is_torch_available()
	    else ()
	)
	lowercase_       :     Union[str, Any]			  =      (
	    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
	lowercase_       :     str			  =      (
	    {
	        '''feature-extraction''': OpenAIGPTModel,
	        '''text-classification''': OpenAIGPTForSequenceClassification,
	        '''text-generation''': OpenAIGPTLMHeadModel,
	        '''zero-shot''': OpenAIGPTForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Any      ):
							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     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  List[str]=False      ):
							__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   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   OpenAIGPTModelTester(self      )
							__snake_case						 =   ConfigTester(self       ,		config_class=__lowerCAmelCase       ,		n_embd=3_7      )




	def   lowercase__   (       self     :  int      ):
							self.config_tester.run_common_tests()




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_openai_gpt_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_lm_head_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_double_lm_head_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__lowerCAmelCase      )




	@slow
	def   lowercase__   (       self     :  str      ):
							for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__snake_case						 =   OpenAIGPTModel.from_pretrained(__lowerCAmelCase      )
													self.assertIsNotNone(__lowerCAmelCase      )





@require_torch
class 						a_				(			unittest.TestCase						):




	@slow
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'      )
							model.to(__lowerCAmelCase      )
							__snake_case						 =   torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]       ,		dtype=torch.long       ,		device=__lowerCAmelCase      )  # the president is
							__snake_case						 =   [
							    4_8_1,
							    4_7_3_5,
							    5_4_4,
							    2_4_6,
							    9_6_3,
							    8_7_0,
							    7_6_2,
							    2_3_9,
							    2_4_4,
							    4_0_4_7_7,
							    2_4_4,
							    2_4_9,
							    7_1_9,
							    8_8_1,
							    4_8_7,
							    5_4_4,
							    2_4_0,
							    2_4_4,
							    6_0_3,
							    4_8_1,
							]  # 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      )

