


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_							:



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_=1_3			,							snake_case_=1_0			,							snake_case_=3			,							snake_case_=2			,							snake_case_=2			,							snake_case_=True			,							snake_case_=True			,							snake_case_=3_2			,							snake_case_=5			,							snake_case_=4			,							snake_case_=3_7			,							snake_case_="gelu"			,							snake_case_=0.1			,							snake_case_=0.1			,							snake_case_=1_0			,							snake_case_=0.0_2			,							snake_case_="divided_space_time"			,							snake_case_=None			,							) ->      str:







								'''simple docstring'''

								__lowercase			       = parent
								__lowercase			       = batch_size
								__lowercase			       = image_size
								__lowercase			       = num_channels
								__lowercase			       = patch_size
								__lowercase			       = num_frames
								__lowercase			       = is_training
								__lowercase			       = use_labels
								__lowercase			       = hidden_size
								__lowercase			       = num_hidden_layers
								__lowercase			       = num_attention_heads
								__lowercase			       = intermediate_size
								__lowercase			       = hidden_act
								__lowercase			       = hidden_dropout_prob
								__lowercase			       = attention_probs_dropout_prob
								__lowercase			       = attention_type
								__lowercase			       = initializer_range
								__lowercase			       = scope
								__lowercase			       = num_labels

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






					def A   (					self    ) ->      int:







								'''simple docstring'''

								__lowercase			       = floats_tensor(
								    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]    )

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

								__lowercase			       = self.get_config()

								return config, pixel_values, labels






					def A   (					self    ) ->      int:







								'''simple docstring'''

								__lowercase			       = 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			       = self.num_labels
								return config






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_    ) ->      int:







								'''simple docstring'''

								__lowercase			       = TimesformerModel(config=snake_case_    )
								model.to(snake_case_    )
								model.eval()
								__lowercase			       = model(snake_case_    )
								self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_    ) ->      str:







								'''simple docstring'''

								__lowercase			       = TimesformerForVideoClassification(snake_case_    )
								model.to(snake_case_    )
								model.eval()

								__lowercase			       = model(snake_case_    )

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






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = self.prepare_config_and_inputs()
								__lowercase       ,			__lowercase       ,			__lowercase			       = config_and_inputs
								__lowercase			       = {'''pixel_values''': pixel_values}
								return config, inputs_dict




@require_torch
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
					__UpperCAmelCase							  =					(
					    {"feature-extraction": TimesformerModel, "video-classification": TimesformerForVideoClassification}
					    if is_torch_available()
					    else {}
					)

					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					False






					def A   (					self    ) ->      str:







								'''simple docstring'''

								__lowercase			       = TimesformerModelTester(self    )
								__lowercase			       = ConfigTester(
								    self			,							config_class=snake_case_			,							has_text_modality=snake_case_			,							hidden_size=3_7    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_=False    ) ->      List[Any]:







								'''simple docstring'''

								__lowercase			       = copy.deepcopy(snake_case_    )

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

								return inputs_dict






					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								self.config_tester.run_common_tests()






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







								'''simple docstring'''

								pass






					def A   (					self    ) ->      int:







								'''simple docstring'''

								__lowercase       ,			__lowercase			       = self.model_tester.prepare_config_and_inputs_for_common()

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






					def A   (					self    ) ->      Any:







								'''simple docstring'''

								__lowercase       ,			__lowercase			       = self.model_tester.prepare_config_and_inputs_for_common()

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

											__lowercase			       = ['''pixel_values''']
											self.assertListEqual(arg_names[:1]			,							snake_case_    )






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case_    )






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_video_classification(*snake_case_    )






					@slow
					def A   (					self    ) ->      Any:







								'''simple docstring'''

								for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowercase			       = TimesformerModel.from_pretrained(snake_case_    )
											self.assertIsNotNone(snake_case_    )






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								if not self.has_attentions:
											pass

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

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

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

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

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

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

														__lowercase			       = outputs.attentions

														self.assertEqual(len(snake_case_    )			,							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    ) ->      Optional[Any]:







								'''simple docstring'''

								def check_hidden_states_output(snake_case_			,							snake_case_			,							snake_case_    ):
											__lowercase			       = model_class(snake_case_    )
											model.to(snake_case_    )
											model.eval()

											with torch.no_grad():
														__lowercase			       = model(**self._prepare_for_class(snake_case_			,							snake_case_    )    )

											__lowercase			       = outputs.hidden_states
											__lowercase			       = self.model_tester.num_hidden_layers + 1
											self.assertEqual(len(snake_case_    )			,							snake_case_    )

											__lowercase			       = self.model_tester.seq_length

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

								__lowercase       ,			__lowercase			       = self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											__lowercase			       = True
											check_hidden_states_output(snake_case_			,							snake_case_			,							snake_case_    )

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

											check_hidden_states_output(snake_case_			,							snake_case_			,							snake_case_    )




def 		lowercase_	(  ):


			'''simple docstring'''







			__lowercase			       = hf_hub_download(
			    repo_id='''hf-internal-testing/spaghetti-video'''							,      filename='''eating_spaghetti.npy'''							,      repo_type='''dataset'''	)
			__lowercase			       = np.load(_UpperCamelCase	)
			return list(_UpperCamelCase	)





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



					'''simple docstring'''







					@cached_property
					def A   (					self    ) ->      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 A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400'''    ).to(
								    snake_case_    )

								__lowercase			       = self.default_image_processor
								__lowercase			       = prepare_video()
								__lowercase			       = image_processor(video[:8]			,							return_tensors='''pt'''    ).to(snake_case_    )

								# forward pass
								with torch.no_grad():
											__lowercase			       = model(**snake_case_    )

								# verify the logits
								__lowercase			       = torch.Size((1, 4_0_0)    )
								self.assertEqual(outputs.logits.shape			,							snake_case_    )

								__lowercase			       = torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]    ).to(snake_case_    )

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






from __future__ import annotations
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self			,							snake_case_    ) ->      None:







								'''simple docstring'''

								__lowercase			       = order

								# a_{0} ... a_{k}
								__lowercase			       = [1.0] + [0.0] * order
								# b_{0} ... b_{k}
								__lowercase			       = [1.0] + [0.0] * order

								# x[n-1] ... x[n-k]
								__lowercase			       = [0.0] * self.order
								# y[n-1] ... y[n-k]
								__lowercase			       = [0.0] * self.order






					def A   (					self			,							snake_case_			,							snake_case_    ) ->      None:







								'''simple docstring'''

								if len(snake_case_    ) < self.order:
											__lowercase			       = [1.0, *a_coeffs]

								if len(snake_case_    ) != self.order + 1:
											__lowercase			       = (
											    F'Expected a_coeffs to have {self.order + 1} elements '
											    F'for {self.order}-order filter, got {len(snake_case_    )}'
											)
											raise ValueError(snake_case_    )

								if len(snake_case_    ) != self.order + 1:
											__lowercase			       = (
											    F'Expected b_coeffs to have {self.order + 1} elements '
											    F'for {self.order}-order filter, got {len(snake_case_    )}'
											)
											raise ValueError(snake_case_    )

								__lowercase			       = a_coeffs
								__lowercase			       = b_coeffs






					def A   (					self			,							snake_case_    ) ->      float:







								'''simple docstring'''

								__lowercase			       = 0.0

								# Start at index 1 and do index 0 at the end.
								for i in range(1			,							self.order + 1    ):
											result += (
											    self.b_coeffs[i] * self.input_history[i - 1]
											    - self.a_coeffs[i] * self.output_history[i - 1]
											)

								__lowercase			       = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

								__lowercase			       = self.input_history[:-1]
								__lowercase			       = self.output_history[:-1]

								__lowercase			       = sample
								__lowercase			       = result

								return result


