



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      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=13			,	UpperCAmelCase__=10			,	UpperCAmelCase__=3			,	UpperCAmelCase__=2			,	UpperCAmelCase__=2			,	UpperCAmelCase__=True			,	UpperCAmelCase__=True			,	UpperCAmelCase__=32			,	UpperCAmelCase__=5			,	UpperCAmelCase__=4			,	UpperCAmelCase__=37			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=10			,	UpperCAmelCase__=0.02			,	UpperCAmelCase__="divided_space_time"			,	UpperCAmelCase__=None			,	):
							A__							=       parent
							A__							=       batch_size
							A__							=       image_size
							A__							=       num_channels
							A__							=       patch_size
							A__							=       num_frames
							A__							=       is_training
							A__							=       use_labels
							A__							=       hidden_size
							A__							=       num_hidden_layers
							A__							=       num_attention_heads
							A__							=       intermediate_size
							A__							=       hidden_act
							A__							=       hidden_dropout_prob
							A__							=       attention_probs_dropout_prob
							A__							=       attention_type
							A__							=       initializer_range
							A__							=       scope
							A__							=       num_labels

							# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
							A__							=       (image_size // patch_size) ** 2
							A__							=       (num_frames) * self.num_patches_per_frame + 1



	def 			__A							(       self		):
							A__							=       floats_tensor(
							    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]		)

							A__							=       None
							if self.use_labels:
													A__							=       ids_tensor([self.batch_size]			,	self.num_labels		)

							A__							=       self.get_config()

							return config, pixel_values, labels



	def 			__A							(       self		):
							A__							=       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			,	)
							A__							=       self.num_labels
							return config



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       TimesformerModel(config=UpperCAmelCase__		)
							model.to(UpperCAmelCase__		)
							model.eval()
							A__							=       model(UpperCAmelCase__		)
							self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size)		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       TimesformerForVideoClassification(UpperCAmelCase__		)
							model.to(UpperCAmelCase__		)
							model.eval()

							A__							=       model(UpperCAmelCase__		)

							# verify the logits shape
							A__							=       torch.Size((self.batch_size, self.num_labels)		)
							self.parent.assertEqual(result.logits.shape			,	UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       self.prepare_config_and_inputs()
							A__						,       A__						,       A__							=       config_and_inputs
							A__							=       {"pixel_values": pixel_values}
							return config, inputs_dict






@require_torch
class      UpperCamelCase    (						_UpperCAmelCase       ,    _UpperCAmelCase       ,    unittest.TestCase     ):
	lowerCAmelCase	:   Optional[Any]				=	(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
	lowerCAmelCase	:   Dict				=	(
	    {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
	    if is_torch_available()
	    else {}
	)

	lowerCAmelCase	:   List[str]				=	False
	lowerCAmelCase	:   Optional[int]				=	False
	lowerCAmelCase	:   Optional[Any]				=	False
	lowerCAmelCase	:   Any				=	False



	def 			__A							(       self		):
							A__							=       TimesformerModelTester(self		)
							A__							=       ConfigTester(
							    self			,	config_class=UpperCAmelCase__			,	has_text_modality=UpperCAmelCase__			,	hidden_size=37		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__=False		):
							A__							=       copy.deepcopy(UpperCAmelCase__		)

							if return_labels:
													if model_class in get_values(UpperCAmelCase__		):
																			A__							=       torch.zeros(
																			    self.model_tester.batch_size			,	dtype=torch.long			,	device=UpperCAmelCase__		)

							return inputs_dict



	def 			__A							(       self		):
							self.config_tester.run_common_tests()



	@unittest.skip(reason="TimeSformer does not use inputs_embeds"		)
	def 			__A							(       self		):
							pass



	def 			__A							(       self		):
							A__						,       A__							=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													A__							=       model_class(UpperCAmelCase__		)
													self.assertIsInstance(model.get_input_embeddings()			,	(nn.Module)		)
													A__							=       model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(UpperCAmelCase__			,	nn.Linear		)		)



	def 			__A							(       self		):
							A__						,       A__							=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													A__							=       model_class(UpperCAmelCase__		)
													A__							=       inspect.signature(model.forward		)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													A__							=       [*signature.parameters.keys()]

													A__							=       ["pixel_values"]
													self.assertListEqual(arg_names[:1]			,	UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_video_classification(*UpperCAmelCase__		)



	@slow
	def 			__A							(       self		):
							for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A__							=       TimesformerModel.from_pretrained(UpperCAmelCase__		)
													self.assertIsNotNone(UpperCAmelCase__		)



	def 			__A							(       self		):
							if not self.has_attentions:
													pass

							else:
													A__						,       A__							=       self.model_tester.prepare_config_and_inputs_for_common()
													A__							=       True

													for model_class in self.all_model_classes:
																			A__							=       self.model_tester.seq_length
																			A__							=       self.model_tester.num_frames

																			A__							=       True
																			A__							=       False
																			A__							=       True
																			A__							=       model_class(UpperCAmelCase__		)
																			model.to(UpperCAmelCase__		)
																			model.eval()
																			with torch.no_grad():
																									A__							=       model(**self._prepare_for_class(UpperCAmelCase__			,	UpperCAmelCase__		)		)
																			A__							=       outputs.attentions
																			self.assertEqual(len(UpperCAmelCase__		)			,	self.model_tester.num_hidden_layers		)

																			# check that output_attentions also work using config
																			del inputs_dict["output_attentions"]
																			A__							=       True
																			A__							=       model_class(UpperCAmelCase__		)
																			model.to(UpperCAmelCase__		)
																			model.eval()
																			with torch.no_grad():
																									A__							=       model(**self._prepare_for_class(UpperCAmelCase__			,	UpperCAmelCase__		)		)
																			A__							=       outputs.attentions
																			self.assertEqual(len(UpperCAmelCase__		)			,	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]			,	)
																			A__							=       len(UpperCAmelCase__		)

																			# Check attention is always last and order is fine
																			A__							=       True
																			A__							=       True
																			A__							=       model_class(UpperCAmelCase__		)
																			model.to(UpperCAmelCase__		)
																			model.eval()
																			with torch.no_grad():
																									A__							=       model(**self._prepare_for_class(UpperCAmelCase__			,	UpperCAmelCase__		)		)

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

																			A__							=       outputs.attentions

																			self.assertEqual(len(UpperCAmelCase__		)			,	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 			__A							(       self		):
							def check_hidden_states_output(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
													A__							=       model_class(UpperCAmelCase__		)
													model.to(UpperCAmelCase__		)
													model.eval()

													with torch.no_grad():
																			A__							=       model(**self._prepare_for_class(UpperCAmelCase__			,	UpperCAmelCase__		)		)

													A__							=       outputs.hidden_states
													A__							=       self.model_tester.num_hidden_layers + 1
													self.assertEqual(len(UpperCAmelCase__		)			,	UpperCAmelCase__		)

													A__							=       self.model_tester.seq_length

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

							A__						,       A__							=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													A__							=       True
													check_hidden_states_output(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													A__							=       True

													check_hidden_states_output(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)





def  UpperCamelCase						(       )->       Dict:





						"""simple docstring"""





						A__							=       hf_hub_download(
						    repo_id="hf-internal-testing/spaghetti-video"			,							filename="eating_spaghetti.npy"			,							repo_type="dataset"    )
						A__							=       np.load(_A    )
						return list(_A    )






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



	@cached_property
	def 			__A							(       self		):
							# logits were tested with a different mean and std, so we use the same here
							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 			__A							(       self		):
							A__							=       TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400"		).to(
							    UpperCAmelCase__		)

							A__							=       self.default_image_processor
							A__							=       prepare_video()
							A__							=       image_processor(video[:8]			,	return_tensors="pt"		).to(UpperCAmelCase__		)

							# forward pass
							with torch.no_grad():
													A__							=       model(**UpperCAmelCase__		)

							# verify the logits
							A__							=       torch.Size((1, 400)		)
							self.assertEqual(outputs.logits.shape			,	UpperCAmelCase__		)

							A__							=       torch.tensor([-0.3_016, -0.7_713, -0.4_205]		).to(UpperCAmelCase__		)

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










def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       str:





						"""simple docstring"""





						A__							=       len(_A    )
						A__							=       len(_A    )
						A__							=       (
						    first_str_length if first_str_length > second_str_length else second_str_length
						)
						A__							=       []
						for char_count in range(_A    ):
												if char_count < first_str_length:
																		output_list.append(first_str[char_count]    )
												if char_count < second_str_length:
																		output_list.append(second_str[char_count]    )
						return "".join(_A    )


if __name__ == "__main__":
						print(alternative_string_arrange("AB", "XYZ"), end=" ")





