


"""simple docstring"""


import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image
else:
	class 						_UpperCamelCase						:
								'''simple docstring'''







								@staticmethod
								def     snake_case       (  *__a					,     **__a			):
											pass


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    hashlib.mda(image.tobytes()   )
			return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Union[str, Any]          =MODEL_FOR_DEPTH_ESTIMATION_MAPPING

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    DepthEstimationPipeline(model=_SCREAMING_SNAKE_CASE					,     image_processor=_SCREAMING_SNAKE_CASE			)
										return depth_estimator, [
										    "./tests/fixtures/tests_samples/COCO/000000039769.png",
										    "./tests/fixtures/tests_samples/COCO/000000039769.png",
										]

							def     snake_case       (  self					,     __a					,     __a			):
										__lowerCAmelCase       					=    depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
										self.assertEqual({"predicted_depth": ANY(torch.Tensor			), "depth": ANY(Image.Image			)}					,     _SCREAMING_SNAKE_CASE			)
										import datasets

										__lowerCAmelCase       					=    datasets.load_dataset("hf-internal-testing/fixtures_image_utils"					,     "image"					,     split="test"			)
										__lowerCAmelCase       					=    depth_estimator(
										    [
										        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			),
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										        # RGBA
										        dataset[0]["file"],
										        # LA
										        dataset[1]["file"],
										        # L
										        dataset[2]["file"],
										    ]			)
										self.assertEqual(
										    [
										        {"predicted_depth": ANY(torch.Tensor			), "depth": ANY(Image.Image			)},
										        {"predicted_depth": ANY(torch.Tensor			), "depth": ANY(Image.Image			)},
										        {"predicted_depth": ANY(torch.Tensor			), "depth": ANY(Image.Image			)},
										        {"predicted_depth": ANY(torch.Tensor			), "depth": ANY(Image.Image			)},
										        {"predicted_depth": ANY(torch.Tensor			), "depth": ANY(Image.Image			)},
										    ]					,     _SCREAMING_SNAKE_CASE					,     )

							@require_tf
							@unittest.skip("Depth estimation is not implemented in TF"			)
							def     snake_case       (  self			):
										pass

							@slow
							@require_torch
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    "Intel/dpt-large"
										__lowerCAmelCase       					=    pipeline("depth-estimation"					,     model=_SCREAMING_SNAKE_CASE			)
										__lowerCAmelCase       					=    depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"			)
										__lowerCAmelCase       					=    hashimage(outputs["depth"]			)

										# This seems flaky.
										# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
										self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()			)					,     29.3_04			)
										self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()			)					,     2.6_6_2			)



							@require_torch
							def     snake_case       (  self			):
										self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"			)








"""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 						_UpperCamelCase						:
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=13					,     __a=10					,     __a=3					,     __a=2					,     __a=2					,     __a=True					,     __a=True					,     __a=32					,     __a=5					,     __a=4					,     __a=37					,     __a="gelu"					,     __a=0.1					,     __a=0.1					,     __a=10					,     __a=0.0_2					,     __a="divided_space_time"					,     __a=None					,     ):
										__lowerCAmelCase       					=    parent
										__lowerCAmelCase       					=    batch_size
										__lowerCAmelCase       					=    image_size
										__lowerCAmelCase       					=    num_channels
										__lowerCAmelCase       					=    patch_size
										__lowerCAmelCase       					=    num_frames
										__lowerCAmelCase       					=    is_training
										__lowerCAmelCase       					=    use_labels
										__lowerCAmelCase       					=    hidden_size
										__lowerCAmelCase       					=    num_hidden_layers
										__lowerCAmelCase       					=    num_attention_heads
										__lowerCAmelCase       					=    intermediate_size
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    hidden_dropout_prob
										__lowerCAmelCase       					=    attention_probs_dropout_prob
										__lowerCAmelCase       					=    attention_type
										__lowerCAmelCase       					=    initializer_range
										__lowerCAmelCase       					=    scope
										__lowerCAmelCase       					=    num_labels

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

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    floats_tensor(
										    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]			)

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

										__lowerCAmelCase       					=    self.get_config()

										return config, pixel_values, labels

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    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       					=    self.num_labels
										return config

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    TimesformerModel(config=__a			)
										model.to(__a			)
										model.eval()
										__lowerCAmelCase       					=    model(__a			)
										self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)			)

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    TimesformerForVideoClassification(__a			)
										model.to(__a			)
										model.eval()

										__lowerCAmelCase       					=    model(__a			)

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



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    config_and_inputs
										__lowerCAmelCase       					=    {"pixel_values": pixel_values}
										return config, inputs_dict
@require_torch
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Optional[int]          =(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
							__UpperCAmelCase		:					Tuple          =(
							    {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
							    if is_torch_available()
							    else {}
							)

							__UpperCAmelCase		:					Any          =False
							__UpperCAmelCase		:					Optional[int]          =False
							__UpperCAmelCase		:					Union[str, Any]          =False
							__UpperCAmelCase		:					Any          =False

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    TimesformerModelTester(self			)
										__lowerCAmelCase       					=    ConfigTester(
										    self					,     config_class=__a					,     has_text_modality=__a					,     hidden_size=37			)

							def     snake_case       (  self					,     __a					,     __a					,     __a=False			):
										__lowerCAmelCase       					=    copy.deepcopy(__a			)

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

										return inputs_dict

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

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

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()

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

							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()

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

													__lowerCAmelCase       					=    ["pixel_values"]
													self.assertListEqual(arg_names[:1]					,     __a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_video_classification(*__a			)

							@slow
							def     snake_case       (  self			):
										for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowerCAmelCase       					=    TimesformerModel.from_pretrained(__a			)
													self.assertIsNotNone(__a			)

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

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

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

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

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

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

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

																__lowerCAmelCase       					=    outputs.attentions

																self.assertEqual(len(__a			)					,     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     snake_case       (  self			):
										def check_hidden_states_output(__a					,     __a					,     __a			):
													__lowerCAmelCase       					=    model_class(__a			)
													model.to(__a			)
													model.eval()

													with torch.no_grad():
																__lowerCAmelCase       					=    model(**self._prepare_for_class(__a					,     __a			)			)

													__lowerCAmelCase       					=    outputs.hidden_states
													__lowerCAmelCase       					=    self.model_tester.num_hidden_layers + 1
													self.assertEqual(len(__a			)					,     __a			)

													__lowerCAmelCase       					=    self.model_tester.seq_length

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

										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													__lowerCAmelCase       					=    True
													check_hidden_states_output(__a					,     __a					,     __a			)

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

													check_hidden_states_output(__a					,     __a					,     __a			)


def _lowerCamelCase			(	):

			'''simple docstring'''




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


@require_torch
@require_vision
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							@cached_property
							def     snake_case       (  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     snake_case       (  self			):
										__lowerCAmelCase       					=    TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400"			).to(
										    __a			)

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

										# forward pass
										with torch.no_grad():
													__lowerCAmelCase       					=    model(**__a			)

										# verify the logits
										__lowerCAmelCase       					=    torch.Size((1, 4_00)			)
										self.assertEqual(outputs.logits.shape					,     __a			)

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

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




