




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  A		:



								@staticmethod
								def        SCREAMING_SNAKE_CASE__			(     *UpperCamelCase__,	**UpperCamelCase__   ):







														"""simple docstring"""



														pass





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  hashlib.mda(image.tobytes()  )
						return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class  A		(  unittest.TestCase     ):
		__snake_case									=   MODEL_FOR_DEPTH_ESTIMATION_MAPPING



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  DepthEstimationPipeline(model=UpperCamelCase__,	image_processor=UpperCamelCase__   )
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
								self.assertEqual({'''predicted_depth''': ANY(torch.Tensor   ), '''depth''': ANY(Image.Image   )},	UpperCamelCase__   )
								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   )},
								    ],	UpperCamelCase__,	)



		@require_tf
		@unittest.skip('''Depth estimation is not implemented in TF'''   )
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass



		@slow
		@require_torch
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  '''Intel/dpt-large'''
								lowerCAmelCase_      			=  pipeline('''depth-estimation''',	model=UpperCamelCase__   )
								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()   ),	29.304   )
								self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()   ),	2.662   )







		@require_torch
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''   )










from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A     =			logging.get_logger(__name__)

_A     =			{
    '''google/vit-base-patch16-224''': '''https://huggingface.co/vit-base-patch16-224/resolve/main/config.json''',
    # See all ViT models at https://huggingface.co/models?filter=vit
}


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   'vit'



		def __init__(     self,	UpperCamelCase__=768,	UpperCamelCase__=12,	UpperCamelCase__=12,	UpperCamelCase__=3072,	UpperCamelCase__="gelu",	UpperCamelCase__=0.0,	UpperCamelCase__=0.0,	UpperCamelCase__=0.02,	UpperCamelCase__=1E-12,	UpperCamelCase__=224,	UpperCamelCase__=16,	UpperCamelCase__=3,	UpperCamelCase__=True,	UpperCamelCase__=16,	**UpperCamelCase__,	):







								"""simple docstring"""



								super().__init__(**UpperCamelCase__   )

								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_      			=  initializer_range
								lowerCAmelCase_      			=  layer_norm_eps
								lowerCAmelCase_      			=  image_size
								lowerCAmelCase_      			=  patch_size
								lowerCAmelCase_      			=  num_channels
								lowerCAmelCase_      			=  qkv_bias
								lowerCAmelCase_      			=  encoder_stride







class  A		(  __UpperCAmelCase     ):
		__snake_case									=   version.parse('1.11'     )



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]   )







		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return 1E-4




