





"""simple docstring"""
from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    LayoutLMvaConfig,
		    TFLayoutLMvaForQuestionAnswering,
		    TFLayoutLMvaForSequenceClassification,
		    TFLayoutLMvaForTokenClassification,
		    TFLayoutLMvaModel,
		)

if is_vision_available():
		from PIL import Image

		from transformers import LayoutLMvaImageProcessor






class 					_UpperCAmelCase   :


					def __init__(					self			:Union[str, Any]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:int=2     ,       __UpperCamelCase			:str=3     ,       __UpperCamelCase			:Any=4     ,       __UpperCamelCase			:Optional[Any]=2     ,       __UpperCamelCase			:Optional[Any]=7     ,       __UpperCamelCase			:List[Any]=True     ,       __UpperCamelCase			:Union[str, Any]=True     ,       __UpperCamelCase			:List[str]=True     ,       __UpperCamelCase			:Any=True     ,       __UpperCamelCase			:Optional[Any]=99     ,       __UpperCamelCase			:Any=36     ,       __UpperCamelCase			:List[str]=2     ,       __UpperCamelCase			:Union[str, Any]=4     ,       __UpperCamelCase			:Dict=37     ,       __UpperCamelCase			:int="gelu"     ,       __UpperCamelCase			:List[Any]=0.1     ,       __UpperCamelCase			:Optional[int]=0.1     ,       __UpperCamelCase			:Tuple=5_12     ,       __UpperCamelCase			:Optional[int]=16     ,       __UpperCamelCase			:Optional[int]=2     ,       __UpperCamelCase			:Optional[Any]=0.02     ,       __UpperCamelCase			:int=6     ,       __UpperCamelCase			:Any=6     ,       __UpperCamelCase			:Optional[Any]=3     ,       __UpperCamelCase			:Tuple=4     ,       __UpperCamelCase			:str=None     ,       __UpperCamelCase			:List[str]=10_00     ,       ):
												A		=  parent
												A		=  batch_size
												A		=  num_channels
												A		=  image_size
												A		=  patch_size
												A		=  is_training
												A		=  use_input_mask
												A		=  use_token_type_ids
												A		=  use_labels
												A		=  vocab_size
												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		=  max_position_embeddings
												A		=  type_vocab_size
												A		=  type_sequence_label_size
												A		=  initializer_range
												A		=  coordinate_size
												A		=  shape_size
												A		=  num_labels
												A		=  num_choices
												A		=  scope
												A		=  range_bbox

												# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
												A		=  text_seq_length
												A		=  (image_size // patch_size) ** 2 + 1
												A		=  self.text_seq_length + self.image_seq_length


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  ids_tensor([self.batch_size, self.text_seq_length]     ,       self.vocab_size		)

												A		=  ids_tensor([self.batch_size, self.text_seq_length, 4]     ,       self.range_bbox		)
												A		=  bbox.numpy()
												# Ensure that bbox is legal
												for i in range(bbox.shape[0]		):
																			for j in range(bbox.shape[1]		):
																										if bbox[i, j, 3] < bbox[i, j, 1]:
																																	A		=  bbox[i, j, 3]
																																	A		=  bbox[i, j, 1]
																																	A		=  tmp_coordinate
																										if bbox[i, j, 2] < bbox[i, j, 0]:
																																	A		=  bbox[i, j, 2]
																																	A		=  bbox[i, j, 0]
																																	A		=  tmp_coordinate
												A		=  tf.constant(__UpperCamelCase		)

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

												A		=  None
												if self.use_input_mask:
																			A		=  random_attention_mask([self.batch_size, self.text_seq_length]		)

												A		=  None
												if self.use_token_type_ids:
																			A		=  ids_tensor([self.batch_size, self.text_seq_length]     ,       self.type_vocab_size		)

												A		=  None
												A		=  None
												if self.use_labels:
																			A		=  ids_tensor([self.batch_size]     ,       self.type_sequence_label_size		)
																			A		=  ids_tensor([self.batch_size, self.text_seq_length]     ,       self.num_labels		)

												A		=  LayoutLMvaConfig(
												    vocab_size=self.vocab_size     ,       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     ,       max_position_embeddings=self.max_position_embeddings     ,       type_vocab_size=self.type_vocab_size     ,       initializer_range=self.initializer_range     ,       coordinate_size=self.coordinate_size     ,       shape_size=self.shape_size     ,       input_size=self.image_size     ,       patch_size=self.patch_size     ,       )

												return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:List[Any]     ,       __UpperCamelCase			:List[Any]     ,       __UpperCamelCase			:List[Any]     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:List[Any]		):
												A		=  TFLayoutLMvaModel(config=__UpperCamelCase		)

												# text + image
												A		=  model(__UpperCamelCase     ,       pixel_values=__UpperCamelCase     ,       training=__UpperCamelCase		)
												A		=  model(
												    __UpperCamelCase     ,       bbox=__UpperCamelCase     ,       pixel_values=__UpperCamelCase     ,       attention_mask=__UpperCamelCase     ,       token_type_ids=__UpperCamelCase     ,       training=__UpperCamelCase     ,       )
												A		=  model(__UpperCamelCase     ,       bbox=__UpperCamelCase     ,       pixel_values=__UpperCamelCase     ,       training=__UpperCamelCase		)

												self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)		)

												# text only
												A		=  model(__UpperCamelCase     ,       training=__UpperCamelCase		)
												self.parent.assertEqual(
												    result.last_hidden_state.shape     ,       (self.batch_size, self.text_seq_length, self.hidden_size)		)

												# image only
												A		=  model({"pixel_values": pixel_values}     ,       training=__UpperCamelCase		)
												self.parent.assertEqual(
												    result.last_hidden_state.shape     ,       (self.batch_size, self.image_seq_length, self.hidden_size)		)


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:List[Any]     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:Union[str, Any]		):
												A		=  self.num_labels
												A		=  TFLayoutLMvaForSequenceClassification(config=__UpperCamelCase		)
												A		=  model(
												    __UpperCamelCase     ,       bbox=__UpperCamelCase     ,       pixel_values=__UpperCamelCase     ,       attention_mask=__UpperCamelCase     ,       token_type_ids=__UpperCamelCase     ,       labels=__UpperCamelCase     ,       training=__UpperCamelCase     ,       )
												self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels)		)


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:List[str]		):
												A		=  self.num_labels
												A		=  TFLayoutLMvaForTokenClassification(config=__UpperCamelCase		)
												A		=  model(
												    __UpperCamelCase     ,       bbox=__UpperCamelCase     ,       pixel_values=__UpperCamelCase     ,       attention_mask=__UpperCamelCase     ,       token_type_ids=__UpperCamelCase     ,       labels=__UpperCamelCase     ,       training=__UpperCamelCase     ,       )
												self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.text_seq_length, self.num_labels)		)


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:List[Any]     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:Union[str, Any]		):
												A		=  2
												A		=  TFLayoutLMvaForQuestionAnswering(config=__UpperCamelCase		)
												A		=  model(
												    __UpperCamelCase     ,       bbox=__UpperCamelCase     ,       pixel_values=__UpperCamelCase     ,       attention_mask=__UpperCamelCase     ,       token_type_ids=__UpperCamelCase     ,       start_positions=__UpperCamelCase     ,       end_positions=__UpperCamelCase     ,       training=__UpperCamelCase     ,       )
												self.parent.assertEqual(result.start_logits.shape     ,       (self.batch_size, self.seq_length)		)
												self.parent.assertEqual(result.end_logits.shape     ,       (self.batch_size, self.seq_length)		)


					def 	lowerCamelCase       (					self			:List[str]		):
												A		=  self.prepare_config_and_inputs()
												((A),	(A),	(A),	(A),	(A),	(A),	(A),	(A))		=  config_and_inputs
												A		=  {
												    "input_ids": input_ids,
												    "bbox": bbox,
												    "pixel_values": pixel_values,
												    "token_type_ids": token_type_ids,
												    "attention_mask": input_mask,
												}
												return config, inputs_dict







@require_tf
class 					_UpperCAmelCase   (	lowercase_     ,							lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							(
					    (
					        TFLayoutLMvaModel,
					        TFLayoutLMvaForQuestionAnswering,
					        TFLayoutLMvaForSequenceClassification,
					        TFLayoutLMvaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCamelCase      =							(
					    {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
					    if is_tf_available()
					    else {}
					)

					UpperCamelCase      =							False
					UpperCamelCase      =							False
					UpperCamelCase      =							False


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:Optional[Any]		):
												return True


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Optional[int]=False		):
												A		=  copy.deepcopy(__UpperCamelCase		)

												if model_class in get_values(__UpperCamelCase		):
																			A		=  {
																			    k: tf.tile(tf.expand_dims(__UpperCamelCase     ,       1		)     ,       (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)		)
																			    if isinstance(__UpperCamelCase     ,       tf.Tensor		) and v.ndim > 0
																			    else v
																			    for k, v in inputs_dict.items()
																			}

												if return_labels:
																			if model_class in get_values(__UpperCamelCase		):
																										A		=  tf.ones(self.model_tester.batch_size     ,       dtype=tf.intaa		)
																			elif model_class in get_values(__UpperCamelCase		):
																										A		=  tf.zeros(self.model_tester.batch_size     ,       dtype=tf.intaa		)
																										A		=  tf.zeros(self.model_tester.batch_size     ,       dtype=tf.intaa		)
																			elif model_class in get_values(__UpperCamelCase		):
																										A		=  tf.zeros(self.model_tester.batch_size     ,       dtype=tf.intaa		)
																			elif model_class in get_values(__UpperCamelCase		):
																										A		=  tf.zeros(
																										    (self.model_tester.batch_size, self.model_tester.text_seq_length)     ,       dtype=tf.intaa		)

												return inputs_dict


					def 	lowerCamelCase       (					self			:List[Any]		):
												A		=  TFLayoutLMvaModelTester(self		)
												A		=  ConfigTester(self     ,       config_class=__UpperCamelCase     ,       hidden_size=37		)


					def 	lowerCamelCase       (					self			:Tuple		):
												self.config_tester.run_common_tests()


					def 	lowerCamelCase       (					self			:Dict		):
												A,	A		=  self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes:
																			A		=  model_class(__UpperCamelCase		)
																			if getattr(__UpperCamelCase     ,       "hf_compute_loss"     ,       __UpperCamelCase		):
																										# The number of elements in the loss should be the same as the number of elements in the label
																										A		=  self._prepare_for_class(inputs_dict.copy()     ,       __UpperCamelCase     ,       return_labels=__UpperCamelCase		)
																										A		=  prepared_for_class[
																										    sorted(prepared_for_class.keys() - inputs_dict.keys()     ,       reverse=__UpperCamelCase		)[0]
																										]
																										A		=  added_label.shape.as_list()[:1]

																										# Test that model correctly compute the loss with kwargs
																										A		=  self._prepare_for_class(inputs_dict.copy()     ,       __UpperCamelCase     ,       return_labels=__UpperCamelCase		)
																										A		=  prepared_for_class.pop("input_ids"		)

																										A		=  model(__UpperCamelCase     ,       **__UpperCamelCase		)[0]
																										self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)

																										# Test that model correctly compute the loss when we mask some positions
																										A		=  self._prepare_for_class(inputs_dict.copy()     ,       __UpperCamelCase     ,       return_labels=__UpperCamelCase		)
																										A		=  prepared_for_class.pop("input_ids"		)
																										if "labels" in prepared_for_class:
																																	A		=  prepared_for_class["labels"].numpy()
																																	if len(labels.shape		) > 1 and labels.shape[1] != 1:
																																								A		=  -1_00
																																								A		=  tf.convert_to_tensor(__UpperCamelCase		)
																																								A		=  model(__UpperCamelCase     ,       **__UpperCamelCase		)[0]
																																								self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)
																																								self.assertTrue(not np.any(np.isnan(loss.numpy()		)		)		)

                # Test that model correctly compute the loss with a dict
																										A		=  self._prepare_for_class(inputs_dict.copy()     ,       __UpperCamelCase     ,       return_labels=__UpperCamelCase		)
																										A		=  model(__UpperCamelCase		)[0]
																										self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)

																										# Test that model correctly compute the loss with a tuple
																										A		=  self._prepare_for_class(inputs_dict.copy()     ,       __UpperCamelCase     ,       return_labels=__UpperCamelCase		)

																										# Get keys that were added with the _prepare_for_class function
																										A		=  prepared_for_class.keys() - inputs_dict.keys()
																										A		=  inspect.signature(model.call		).parameters
																										A		=  list(signature.keys()		)

																										# Create a dictionary holding the location of the tensors in the tuple
																										A		=  {0: "input_ids"}
																										for label_key in label_keys:
																																	A		=  signature_names.index(__UpperCamelCase		)
																																	A		=  label_key
																										A		=  sorted(tuple_index_mapping.items()		)
																										# Initialize a list with their default values, update the values and convert to a tuple
																										A		=  []

																										for name in signature_names:
																																	if name != "kwargs":
																																								list_input.append(signature[name].default		)

																										for index, value in sorted_tuple_index_mapping:
																																	A		=  prepared_for_class[value]

																										A		=  tuple(__UpperCamelCase		)

																										# Send to model
																										A		=  model(tuple_input[:-1]		)[0]

																										self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)


					def 	lowerCamelCase       (					self			:int		):
												(
												    (
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	
												)		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:int		):
												(
												    (
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	
												)		=  self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																			A		=  type
																			self.model_tester.create_and_check_model(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:str		):
												(
												    (
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	
												)		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(
												    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:str		):
												(
												    (
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	
												)		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(
												    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Optional[int]		):
												(
												    (
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	(
												    A
												),	
												)		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(
												    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)


					@slow
					def 	lowerCamelCase       (					self			:Optional[int]		):
												for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A		=  TFLayoutLMvaModel.from_pretrained(__UpperCamelCase		)
																			self.assertIsNotNone(__UpperCamelCase		)







def 					A__				(			):
							A		=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
							return image






@require_tf
class 					_UpperCAmelCase   (	unittest.TestCase ):


					@cached_property
					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												return LayoutLMvaImageProcessor(apply_ocr=__UpperCamelCase		) if is_vision_available() else None


					@slow
					def 	lowerCamelCase       (					self			:str		):
												A		=  TFLayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"		)

												A		=  self.default_image_processor
												A		=  prepare_img()
												A		=  image_processor(images=__UpperCamelCase     ,       return_tensors="tf"		).pixel_values

												A		=  tf.constant([[1, 2]]		)
												A		=  tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]		)     ,       axis=0		)

												# forward pass
												A		=  model(input_ids=__UpperCamelCase     ,       bbox=__UpperCamelCase     ,       pixel_values=__UpperCamelCase     ,       training=__UpperCamelCase		)

												# verify the logits
												A		=  (1, 1_99, 7_68)
												self.assertEqual(outputs.last_hidden_state.shape     ,       __UpperCamelCase		)

												A		=  tf.constant(
												    [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]		)

												self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]     ,       __UpperCamelCase     ,       atol=1e-4		)		)









"""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   :


							@staticmethod
							def 	lowerCamelCase       (					*__UpperCamelCase			:List[Any]     ,       **__UpperCamelCase			:List[Any]		):
														pass






def 					A__				(			UpperCamelCase       ):
							A		=  hashlib.mda(image.tobytes()       )
							return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					_UpperCAmelCase   (	unittest.TestCase ):
					UpperCamelCase      =							MODEL_FOR_DEPTH_ESTIMATION_MAPPING


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:Optional[int]		):
												A		=  DepthEstimationPipeline(model=__UpperCamelCase     ,       image_processor=__UpperCamelCase		)
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
												self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}     ,       __UpperCamelCase		)
												import datasets

												A		=  datasets.load_dataset("hf-internal-testing/fixtures_image_utils"     ,       "image"     ,       split="test"		)
												A		=  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		)},
												    ]     ,       __UpperCamelCase     ,       )


					@require_tf
					@unittest.skip("Depth estimation is not implemented in TF"		)
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												pass


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

												# This seems flaky.
												# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
												self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()		)     ,       29.304		)
												self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()		)     ,       2.662		)


					@require_torch
					def 	lowerCamelCase       (					self			:Optional[Any]		):
												# This is highly irregular to have no small tests.
												self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)


