

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 	A_		:
							def __init__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      List[str]=1_3       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=1_0       ,SCREAMING_SNAKE_CASE__		:      str=3       ,SCREAMING_SNAKE_CASE__		:      List[Any]=2       ,SCREAMING_SNAKE_CASE__		:      Any=2       ,SCREAMING_SNAKE_CASE__		:      int=True       ,SCREAMING_SNAKE_CASE__		:      Tuple=True       ,SCREAMING_SNAKE_CASE__		:      Tuple=3_2       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=5       ,SCREAMING_SNAKE_CASE__		:      List[str]=4       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=3_7       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]="gelu"       ,SCREAMING_SNAKE_CASE__		:      str=0.1       ,SCREAMING_SNAKE_CASE__		:      List[Any]=0.1       ,SCREAMING_SNAKE_CASE__		:      Dict=1_0       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=0.02       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]="divided_space_time"       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=None       ,):
								__lowerCamelCase    :    Optional[Any] 	=			parent
								__lowerCamelCase    :    List[Any] 	=			batch_size
								__lowerCamelCase    :    Optional[Any] 	=			image_size
								__lowerCamelCase    :    Any 	=			num_channels
								__lowerCamelCase    :    Dict 	=			patch_size
								__lowerCamelCase    :    List[Any] 	=			num_frames
								__lowerCamelCase    :    List[Any] 	=			is_training
								__lowerCamelCase    :    str 	=			use_labels
								__lowerCamelCase    :    Any 	=			hidden_size
								__lowerCamelCase    :    Union[str, Any] 	=			num_hidden_layers
								__lowerCamelCase    :    Tuple 	=			num_attention_heads
								__lowerCamelCase    :    List[Any] 	=			intermediate_size
								__lowerCamelCase    :    str 	=			hidden_act
								__lowerCamelCase    :    str 	=			hidden_dropout_prob
								__lowerCamelCase    :    Dict 	=			attention_probs_dropout_prob
								__lowerCamelCase    :    Union[str, Any] 	=			attention_type
								__lowerCamelCase    :    Optional[Any] 	=			initializer_range
								__lowerCamelCase    :    List[Any] 	=			scope
								__lowerCamelCase    :    List[Any] 	=			num_labels

								# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
								__lowerCamelCase    :    List[Any] 	=			(image_size // patch_size) ** 2
								__lowerCamelCase    :    Dict 	=			(num_frames) * self.num_patches_per_frame + 1
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    int 	=			floats_tensor(
								    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size])

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

								__lowerCamelCase    :    List[str] 	=			self.get_config()

								return config, pixel_values, labels
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    str 	=			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       ,)
								__lowerCamelCase    :    List[Any] 	=			self.num_labels
								return config
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      List[Any]):
								__lowerCamelCase    :    Dict 	=			TimesformerModel(config=SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()
								__lowerCamelCase    :    Tuple 	=			model(SCREAMING_SNAKE_CASE__)
								self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size))
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple):
								__lowerCamelCase    :    List[Any] 	=			TimesformerForVideoClassification(SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()

								__lowerCamelCase    :    Optional[int] 	=			model(SCREAMING_SNAKE_CASE__)

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


							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    Optional[int] 	=			self.prepare_config_and_inputs()
								__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			config_and_inputs
								__lowerCamelCase    :    int 	=			{'pixel_values': pixel_values}
								return config, inputs_dict






@require_torch
class 	A_		(	SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  Union[str, Any]										=				(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
							_UpperCAmelCase   :  Tuple										=				(
							    {'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase   :  int										=				False
							_UpperCAmelCase   :  int										=				False
							_UpperCAmelCase   :  Dict										=				False
							_UpperCAmelCase   :  int										=				False
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    Optional[Any] 	=			TimesformerModelTester(self)
								__lowerCamelCase    :    Optional[Any] 	=			ConfigTester(
								    self       ,config_class=SCREAMING_SNAKE_CASE__       ,has_text_modality=SCREAMING_SNAKE_CASE__       ,hidden_size=3_7)
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      int=False):
								__lowerCamelCase    :    str 	=			copy.deepcopy(SCREAMING_SNAKE_CASE__)

								if return_labels:
									if model_class in get_values(SCREAMING_SNAKE_CASE__):
										__lowerCamelCase    :    Dict 	=			torch.zeros(
										    self.model_tester.batch_size       ,dtype=torch.long       ,device=SCREAMING_SNAKE_CASE__)

								return inputs_dict
							def    lowerCAmelCase					(		self		:      List[str]):
								self.config_tester.run_common_tests()
							@unittest.skip(reason='TimeSformer does not use inputs_embeds')
							def    lowerCAmelCase					(		self		:      int):
								pass
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    int 	=			model_class(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(model.get_input_embeddings()       ,(nn.Module))
									__lowerCamelCase    :    List[Any] 	=			model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__       ,nn.Linear))
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    Dict 	=			model_class(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    str 	=			inspect.signature(model.forward)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__lowerCamelCase    :    Optional[Any] 	=			[*signature.parameters.keys()]

									__lowerCamelCase    :    str 	=			['pixel_values']
									self.assertListEqual(arg_names[:1]       ,SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    int 	=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_video_classification(*SCREAMING_SNAKE_CASE__)
							@slow
							def    lowerCAmelCase					(		self		:      str):
								for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase    :    Union[str, Any] 	=			TimesformerModel.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Tuple):
								if not self.has_attentions:
									pass

								else:
									__lowerCamelCase	,			__lowerCamelCase    :    int 	=			self.model_tester.prepare_config_and_inputs_for_common()
									__lowerCamelCase    :    List[str] 	=			True

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

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

										# check that output_attentions also work using config
										del inputs_dict["output_attentions"]
										__lowerCamelCase    :    List[Any] 	=			True
										__lowerCamelCase    :    Tuple 	=			model_class(SCREAMING_SNAKE_CASE__)
										model.to(SCREAMING_SNAKE_CASE__)
										model.eval()
										with torch.no_grad():
											__lowerCamelCase    :    Tuple 	=			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__))
										__lowerCamelCase    :    List[Any] 	=			outputs.attentions
										self.assertEqual(len(SCREAMING_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]       ,)
										__lowerCamelCase    :    str 	=			len(SCREAMING_SNAKE_CASE__)

										# Check attention is always last and order is fine
										__lowerCamelCase    :    List[str] 	=			True
										__lowerCamelCase    :    List[str] 	=			True
										__lowerCamelCase    :    List[str] 	=			model_class(SCREAMING_SNAKE_CASE__)
										model.to(SCREAMING_SNAKE_CASE__)
										model.eval()
										with torch.no_grad():
											__lowerCamelCase    :    int 	=			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__))

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

										__lowerCamelCase    :    Tuple 	=			outputs.attentions

										self.assertEqual(len(SCREAMING_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    lowerCAmelCase					(		self		:      Optional[int]):
								def check_hidden_states_output(SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
									__lowerCamelCase    :    Optional[int] 	=			model_class(SCREAMING_SNAKE_CASE__)
									model.to(SCREAMING_SNAKE_CASE__)
									model.eval()

									with torch.no_grad():
										__lowerCamelCase    :    Any 	=			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__))

									__lowerCamelCase    :    Dict 	=			outputs.hidden_states
									__lowerCamelCase    :    Tuple 	=			self.model_tester.num_hidden_layers + 1
									self.assertEqual(len(SCREAMING_SNAKE_CASE__)       ,SCREAMING_SNAKE_CASE__)

									__lowerCamelCase    :    Optional[int] 	=			self.model_tester.seq_length

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

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

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    int 	=			True
									check_hidden_states_output(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									__lowerCamelCase    :    Dict 	=			True

									check_hidden_states_output(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)






def        SCREAMING_SNAKE_CASE__	(  )  ->					List[Any]:
	__lowerCamelCase    :    Dict 	=			hf_hub_download(
	    repo_id='hf-internal-testing/spaghetti-video'     ,			filename='eating_spaghetti.npy'     ,			repo_type='dataset'      )
	__lowerCamelCase    :    Optional[Any] 	=			np.load(lowerCamelCase__      )
	return list(lowerCamelCase__      )






@require_torch
@require_vision
class 	A_		(	unittest.TestCase    ):
							@cached_property
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								# 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    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    List[str] 	=			TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400').to(
								    SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    int 	=			self.default_image_processor
								__lowerCamelCase    :    Dict 	=			prepare_video()
								__lowerCamelCase    :    List[Any] 	=			image_processor(video[:8]       ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)

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

								# verify the logits
								__lowerCamelCase    :    List[Any] 	=			torch.Size((1, 4_0_0))
								self.assertEqual(outputs.logits.shape       ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    str 	=			torch.tensor([-0.3016, -0.7713, -0.4205]).to(SCREAMING_SNAKE_CASE__)

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







import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
					import onnxruntime as ort






@nightly
@require_onnxruntime
@require_torch_gpu
class 	A_		(	unittest.TestCase    ):
							@property
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)
							@property
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			ort.SessionOptions()
								__lowerCamelCase    :    Tuple 	=			False
								return options


							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    Tuple 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo.png')
								__lowerCamelCase    :    List[str] 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png')
								__lowerCamelCase    :    Optional[int] 	=			load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy')

								# using the PNDM scheduler by default
								__lowerCamelCase    :    Dict 	=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'       ,revision='onnx'       ,safety_checker=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__       ,provider=self.gpu_provider       ,sess_options=self.gpu_options       ,)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Union[str, Any] 	=			'A red cat sitting on a park bench'

								__lowerCamelCase    :    Any 	=			np.random.RandomState(0)
								__lowerCamelCase    :    List[Any] 	=			pipe(
								    prompt=SCREAMING_SNAKE_CASE__       ,image=SCREAMING_SNAKE_CASE__       ,mask_image=SCREAMING_SNAKE_CASE__       ,strength=0.75       ,guidance_scale=7.5       ,num_inference_steps=1_5       ,generator=SCREAMING_SNAKE_CASE__       ,output_type='np'       ,)

								__lowerCamelCase    :    Union[str, Any] 	=			output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image).max() < 1E-2




