"""simple docstring"""







import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
						    TimesformerForVideoClassification,
						    TimesformerModel,
						)
						from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from transformers import VideoMAEImageProcessor



class  _lowerCamelCase			:







				def __init__( self    :       Any			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       Optional[int]=1_3			,					lowerCamelCase_    :       str=1_0			,					lowerCamelCase_    :       Dict=3			,					lowerCamelCase_    :       Dict=2			,					lowerCamelCase_    :       Any=2			,					lowerCamelCase_    :       List[Any]=True			,					lowerCamelCase_    :       int=True			,					lowerCamelCase_    :       str=3_2			,					lowerCamelCase_    :       str=5			,					lowerCamelCase_    :       int=4			,					lowerCamelCase_    :       str=3_7			,					lowerCamelCase_    :       List[Any]="gelu"			,					lowerCamelCase_    :       List[Any]=0.1			,					lowerCamelCase_    :       List[str]=0.1			,					lowerCamelCase_    :       Any=1_0			,					lowerCamelCase_    :       str=0.02			,					lowerCamelCase_    :       Optional[Any]="divided_space_time"			,					lowerCamelCase_    :       Optional[int]=None			,					):

					"""simple docstring"""






					_lowercase :    str							=							parent
					_lowercase :    str							=							batch_size
					_lowercase :    List[Any]							=							image_size
					_lowercase :    Optional[Any]							=							num_channels
					_lowercase :    Union[str, Any]							=							patch_size
					_lowercase :    Dict							=							num_frames
					_lowercase :    List[str]							=							is_training
					_lowercase :    str							=							use_labels
					_lowercase :    List[str]							=							hidden_size
					_lowercase :    int							=							num_hidden_layers
					_lowercase :    Union[str, Any]							=							num_attention_heads
					_lowercase :    Dict							=							intermediate_size
					_lowercase :    int							=							hidden_act
					_lowercase :    Optional[Any]							=							hidden_dropout_prob
					_lowercase :    List[str]							=							attention_probs_dropout_prob
					_lowercase :    Optional[Any]							=							attention_type
					_lowercase :    List[str]							=							initializer_range
					_lowercase :    Optional[int]							=							scope
					_lowercase :    Dict							=							num_labels

					# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
					_lowercase :    List[Any]							=							(image_size // patch_size) ** 2
					_lowercase :    Dict							=							(num_frames) * self.num_patches_per_frame + 1







				def 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							floats_tensor(
					    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]						)

					_lowercase :    Optional[int]							=							None
					if self.use_labels:
						_lowercase :    Union[str, Any]							=							ids_tensor([self.batch_size]			,					self.num_labels						)

					_lowercase :    List[Any]							=							self.get_config()

					return config, pixel_values, labels







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					_lowercase :    Any							=							TimesformerConfig(
					    image_size=self.image_size			,					patch_size=self.patch_size			,					num_channels=self.num_channels			,					num_frames=self.num_frames			,					hidden_size=self.hidden_size			,					num_hidden_layers=self.num_hidden_layers			,					num_attention_heads=self.num_attention_heads			,					intermediate_size=self.intermediate_size			,					hidden_act=self.hidden_act			,					hidden_dropout_prob=self.hidden_dropout_prob			,					attention_probs_dropout_prob=self.attention_probs_dropout_prob			,					initializer_range=self.initializer_range			,					attention_type=self.attention_type			,					)
					_lowercase :    str							=							self.num_labels
					return config







				def 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Optional[int]						):

					"""simple docstring"""






					_lowercase :    int							=							TimesformerModel(config=lowerCamelCase_						)
					model.to(lowerCamelCase_						)
					model.eval()
					_lowercase :    List[str]							=							model(lowerCamelCase_						)
					self.parent.assertEqual(result.last_hidden_state.shape			,					(self.batch_size, self.seq_length, self.hidden_size)						)







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Union[str, Any]			,					lowerCamelCase_    :       str						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							TimesformerForVideoClassification(lowerCamelCase_						)
					model.to(lowerCamelCase_						)
					model.eval()

					_lowercase :    List[Any]							=							model(lowerCamelCase_						)

					# verify the logits shape
					_lowercase :    Tuple							=							torch.Size((self.batch_size, self.num_labels)						)
					self.parent.assertEqual(result.logits.shape			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					_lowercase :    List[str]							=							self.prepare_config_and_inputs()
					_lowercase				,      _lowercase				,      _lowercase :    Any							=							config_and_inputs
					_lowercase :    Tuple							=							{'pixel_values': pixel_values}
					return config, inputs_dict



@require_torch
class  _lowerCamelCase			(__lowerCamelCase						,							__lowerCamelCase						,							unittest.TestCase     ):
				_snake_case     =  (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
				_snake_case     =  (
				    {"feature-extraction": TimesformerModel, "video-classification": TimesformerForVideoClassification}
				    if is_torch_available()
				    else {}
				)

				_snake_case     =  False
				_snake_case     =  False
				_snake_case     =  False
				_snake_case     =  False







				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					_lowercase :    Tuple							=							TimesformerModelTester(self						)
					_lowercase :    int							=							ConfigTester(
					    self			,					config_class=lowerCamelCase_			,					has_text_modality=lowerCamelCase_			,					hidden_size=3_7						)







				def 							__UpperCAmelCase					( self    :       Dict			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       str=False						):

					"""simple docstring"""






					_lowercase :    int							=							copy.deepcopy(lowerCamelCase_						)

					if return_labels:
						if model_class in get_values(lowerCamelCase_						):
							_lowercase :    Optional[Any]							=							torch.zeros(
							    self.model_tester.batch_size			,					dtype=torch.long			,					device=lowerCamelCase_						)

					return inputs_dict







				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					self.config_tester.run_common_tests()







				@unittest.skip(reason='TimeSformer does not use inputs_embeds'						)
				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					pass







				def 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""simple docstring"""






					_lowercase				,      _lowercase :    Optional[Any]							=							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						_lowercase :    List[str]							=							model_class(lowerCamelCase_						)
						self.assertIsInstance(model.get_input_embeddings()			,					(nn.Module)						)
						_lowercase :    List[Any]							=							model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(lowerCamelCase_			,					nn.Linear						)						)







				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					_lowercase				,      _lowercase :    List[str]							=							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						_lowercase :    int							=							model_class(lowerCamelCase_						)
						_lowercase :    Any							=							inspect.signature(model.forward						)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						_lowercase :    List[str]							=							[*signature.parameters.keys()]

						_lowercase :    Tuple							=							['pixel_values']
						self.assertListEqual(arg_names[:1]			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






					_lowercase :    Dict							=							self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_video_classification(*lowerCamelCase_						)







				@slow
				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_lowercase :    int							=							TimesformerModel.from_pretrained(lowerCamelCase_						)
						self.assertIsNotNone(lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""simple docstring"""






					if not self.has_attentions:
						pass

					else:
						_lowercase				,      _lowercase :    Optional[Any]							=							self.model_tester.prepare_config_and_inputs_for_common()
						_lowercase :    Optional[int]							=							True

						for model_class in self.all_model_classes:
							_lowercase :    Optional[Any]							=							self.model_tester.seq_length
							_lowercase :    Union[str, Any]							=							self.model_tester.num_frames

							_lowercase :    List[str]							=							True
							_lowercase :    Dict							=							False
							_lowercase :    Optional[Any]							=							True
							_lowercase :    Any							=							model_class(lowerCamelCase_						)
							model.to(lowerCamelCase_						)
							model.eval()
							with torch.no_grad():
								_lowercase :    int							=							model(**self._prepare_for_class(lowerCamelCase_			,					lowerCamelCase_						)						)
							_lowercase :    List[str]							=							outputs.attentions
							self.assertEqual(len(lowerCamelCase_						)			,					self.model_tester.num_hidden_layers						)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							_lowercase :    int							=							True
							_lowercase :    str							=							model_class(lowerCamelCase_						)
							model.to(lowerCamelCase_						)
							model.eval()
							with torch.no_grad():
								_lowercase :    Optional[Any]							=							model(**self._prepare_for_class(lowerCamelCase_			,					lowerCamelCase_						)						)
							_lowercase :    Optional[Any]							=							outputs.attentions
							self.assertEqual(len(lowerCamelCase_						)			,					self.model_tester.num_hidden_layers						)

							# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
							self.assertListEqual(
							    list(attentions[0].shape[-3:]						)			,					[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]			,					)
							_lowercase :    Tuple							=							len(lowerCamelCase_						)

							# Check attention is always last and order is fine
							_lowercase :    Dict							=							True
							_lowercase :    List[Any]							=							True
							_lowercase :    str							=							model_class(lowerCamelCase_						)
							model.to(lowerCamelCase_						)
							model.eval()
							with torch.no_grad():
								_lowercase :    Optional[Any]							=							model(**self._prepare_for_class(lowerCamelCase_			,					lowerCamelCase_						)						)

							self.assertEqual(out_len + 1			,					len(lowerCamelCase_						)						)

							_lowercase :    str							=							outputs.attentions

							self.assertEqual(len(lowerCamelCase_						)			,					self.model_tester.num_hidden_layers						)

							# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
							self.assertListEqual(
							    list(self_attentions[0].shape[-3:]						)			,					[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]			,					)







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					def check_hidden_states_output(lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       List[str]						):
						_lowercase :    Tuple							=							model_class(lowerCamelCase_						)
						model.to(lowerCamelCase_						)
						model.eval()

						with torch.no_grad():
							_lowercase :    Dict							=							model(**self._prepare_for_class(lowerCamelCase_			,					lowerCamelCase_						)						)

						_lowercase :    Optional[Any]							=							outputs.hidden_states
						_lowercase :    Any							=							self.model_tester.num_hidden_layers + 1
						self.assertEqual(len(lowerCamelCase_						)			,					lowerCamelCase_						)

						_lowercase :    List[str]							=							self.model_tester.seq_length

						self.assertListEqual(
						    list(hidden_states[0].shape[-2:]						)			,					[seq_length, self.model_tester.hidden_size]			,					)

					_lowercase				,      _lowercase :    List[str]							=							self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						_lowercase :    List[str]							=							True
						check_hidden_states_output(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_						)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						_lowercase :    Optional[int]							=							True

						check_hidden_states_output(lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_						)



def      __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase :    Dict							=							hf_hub_download(
	    repo_id='hf-internal-testing/spaghetti-video'     ,filename='eating_spaghetti.npy'     ,repo_type='dataset'			)
	_lowercase :    int							=							np.load(__UpperCAmelCase			)
	return list(__UpperCAmelCase			)







@require_torch
@require_vision
class  _lowerCamelCase			(unittest.TestCase     ):







				@cached_property
				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					return (
					    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]			,					image_std=[0.5, 0.5, 0.5]						)
					    if is_vision_available()
					    else None
					)







				@slow
				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					_lowercase :    Any							=							TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400'						).to(
					    lowerCamelCase_						)

					_lowercase :    Dict							=							self.default_image_processor
					_lowercase :    Dict							=							prepare_video()
					_lowercase :    Any							=							image_processor(video[:8]			,					return_tensors='pt'						).to(lowerCamelCase_						)

					# forward pass
					with torch.no_grad():
						_lowercase :    Union[str, Any]							=							model(**lowerCamelCase_						)

					# verify the logits
					_lowercase :    List[str]							=							torch.Size((1, 4_0_0)						)
					self.assertEqual(outputs.logits.shape			,					lowerCamelCase_						)

					_lowercase :    int							=							torch.tensor([-0.3016, -0.7713, -0.4205]						).to(lowerCamelCase_						)

					self.assertTrue(torch.allclose(outputs.logits[0, :3]			,					lowerCamelCase_			,					atol=1E-4						)						)




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE									=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE									=						[
						    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BioGptForCausalLM',
						    'BioGptForTokenClassification',
						    'BioGptForSequenceClassification',
						    'BioGptModel',
						    'BioGptPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
						from .tokenization_biogpt import BioGptTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_biogpt import (
												    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BioGptForCausalLM,
												    BioGptForSequenceClassification,
												    BioGptForTokenClassification,
												    BioGptModel,
												    BioGptPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE									=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



