






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 						SCREAMING_SNAKE_CASE				:





											"""simple docstring"""

											@staticmethod
											def       __A (  *__A:   int		,     **__A:   List[Any]   )    ->				str:
																pass




def      __A					(     _lowercase ):

					'''simple docstring'''







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





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							A_						  =					MODEL_FOR_DEPTH_ESTIMATION_MAPPING

							def       __A (  self:   Optional[Any]		,     __A:   str		,     __A:   Dict		,     __A:   Tuple   )    ->				Tuple:
												_A								= DepthEstimationPipeline(model=__A		,     image_processor=__A   )
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]

							def       __A (  self:   Tuple		,     __A:   Tuple		,     __A:   Union[str, Any]   )    ->				str:
												_A								= depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
												self.assertEqual({'''predicted_depth''': ANY(torch.Tensor   ), '''depth''': ANY(Image.Image   )}		,     __A   )
												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   )},
												    ]		,     __A		,     )

							@require_tf
							@unittest.skip('''Depth estimation is not implemented in TF'''   )
							def       __A (  self:   Any   )    ->				Optional[Any]:
												pass

							@slow
							@require_torch
							def       __A (  self:   List[Any]   )    ->				Tuple:
												_A								= '''Intel/dpt-large'''
												_A								= pipeline('''depth-estimation'''		,     model=__A   )
												_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       __A (  self:   List[Any]   )    ->				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'''   )











from collections import OrderedDict
from typing import Mapping

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


__A      =				logging.get_logger(__name__)

__A      =				{
    'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
    'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
    'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
    'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
    'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
    'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"roberta"

							def __init__(  self:   Dict		,     __A:   int=5_02_65		,     __A:   Union[str, Any]=7_68		,     __A:   Union[str, Any]=12		,     __A:   str=12		,     __A:   int=30_72		,     __A:   str="gelu"		,     __A:   Union[str, Any]=0.1		,     __A:   int=0.1		,     __A:   Optional[int]=5_12		,     __A:   Union[str, Any]=2		,     __A:   str=0.02		,     __A:   str=1e-12		,     __A:   Any=1		,     __A:   str=0		,     __A:   Any=2		,     __A:   Optional[int]="absolute"		,     __A:   Optional[Any]=True		,     __A:   Union[str, Any]=None		,     **__A:   List[str]		,     )    ->				Dict:
												super().__init__(pad_token_id=__A		,     bos_token_id=__A		,     eos_token_id=__A		,     **__A   )

												_A								= vocab_size
												_A								= hidden_size
												_A								= num_hidden_layers
												_A								= num_attention_heads
												_A								= hidden_act
												_A								= intermediate_size
												_A								= hidden_dropout_prob
												_A								= attention_probs_dropout_prob
												_A								= max_position_embeddings
												_A								= type_vocab_size
												_A								= initializer_range
												_A								= layer_norm_eps
												_A								= position_embedding_type
												_A								= use_cache
												_A								= classifier_dropout





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@property
							def       __A (  self:   Dict   )    ->				Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_A								= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_A								= {0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ]   )



