


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       lowerCamelCase_ (							*UpperCamelCase_:	Optional[int]    ,						**UpperCamelCase_:	Optional[int]							)			->   Dict:





												"""simple docstring"""







												pass
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




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


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class        _a							(			unittest.TestCase			):
							_lowercase	:			Dict        = MODEL_FOR_DEPTH_ESTIMATION_MAPPING

							def       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	Union[str, Any]    ,						UpperCamelCase_:	Any							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      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:	Any    ,						UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	Optional[int]							)			->   Any:





									"""simple docstring"""







									lowercase__            =      depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
									self.assertEqual({'''predicted_depth''': ANY(torch.Tensor							), '''depth''': ANY(Image.Image							)}    ,						UpperCamelCase_							)
									import datasets

									lowercase__            =      datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''    ,						'''image'''    ,						split='''test'''							)
									lowercase__            =      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:	int							)			->   Dict:





									"""simple docstring"""







									pass

							@slow
							@require_torch
							def       lowerCamelCase_ (							self:	str							)			->   Union[str, Any]:





									"""simple docstring"""







									lowercase__            =      '''Intel/dpt-large'''
									lowercase__            =      pipeline('''depth-estimation'''    ,						model=UpperCamelCase_							)
									lowercase__            =      depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''							)
									lowercase__            =      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:	Dict							)			->   Dict:





									"""simple docstring"""







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







"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
							from transformers import (
							    AutoConfig,
							    BertConfig,
							    GPTaConfig,
							    TaConfig,
							    TFAutoModel,
							    TFAutoModelForCausalLM,
							    TFAutoModelForMaskedLM,
							    TFAutoModelForPreTraining,
							    TFAutoModelForQuestionAnswering,
							    TFAutoModelForSeqaSeqLM,
							    TFAutoModelForSequenceClassification,
							    TFAutoModelWithLMHead,
							    TFBertForMaskedLM,
							    TFBertForPreTraining,
							    TFBertForQuestionAnswering,
							    TFBertForSequenceClassification,
							    TFBertModel,
							    TFGPTaLMHeadModel,
							    TFRobertaForMaskedLM,
							    TFTaForConditionalGeneration,
							)
							from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
							from transformers import (
							    AutoModel,
							    AutoModelForCausalLM,
							    AutoModelForMaskedLM,
							    AutoModelForPreTraining,
							    AutoModelForQuestionAnswering,
							    AutoModelForSeqaSeqLM,
							    AutoModelForSequenceClassification,
							    AutoModelWithLMHead,
							    BertForMaskedLM,
							    BertForPreTraining,
							    BertForQuestionAnswering,
							    BertForSequenceClassification,
							    BertModel,
							    GPTaLMHeadModel,
							    RobertaForMaskedLM,
							    TaForConditionalGeneration,
							)
@is_pt_tf_cross_test
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		@slow
		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		Union[str, Any]:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    Tuple				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Dict				=			TFAutoModel.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			AutoModel.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Tuple:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    Dict				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    str				=			TFAutoModelForPreTraining.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Optional[Any]				=			AutoModelForPreTraining.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Dict:






				"""simple docstring"""



				for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Any				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			TFAutoModelForCausalLM.from_pretrained(_snake_case			,from_pt=_snake_case						)
						lowercase__		,			lowercase__   :    Optional[Any]				=			TFAutoModelForCausalLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Union[str, Any]				=			AutoModelForCausalLM.from_pretrained(_snake_case			,from_tf=_snake_case						)
						lowercase__		,			lowercase__   :    Optional[Any]				=			AutoModelForCausalLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Any						)					->		Tuple:






				"""simple docstring"""



				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Any				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Optional[Any]				=			TFAutoModelWithLMHead.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			AutoModelWithLMHead.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    str				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Union[str, Any]				=			TFAutoModelForMaskedLM.from_pretrained(_snake_case			,from_pt=_snake_case						)
						lowercase__		,			lowercase__   :    str				=			TFAutoModelForMaskedLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			AutoModelForMaskedLM.from_pretrained(_snake_case			,from_tf=_snake_case						)
						lowercase__		,			lowercase__   :    Any				=			AutoModelForMaskedLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Dict:






				"""simple docstring"""



				for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Union[str, Any]				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			TFAutoModelForSeqaSeqLM.from_pretrained(_snake_case			,from_pt=_snake_case						)
						lowercase__		,			lowercase__   :    List[str]				=			TFAutoModelForSeqaSeqLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			AutoModelForSeqaSeqLM.from_pretrained(_snake_case			,from_tf=_snake_case						)
						lowercase__		,			lowercase__   :    Optional[int]				=			AutoModelForSeqaSeqLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Union[str, Any]:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    Tuple				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			TFAutoModelForSequenceClassification.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Union[str, Any]				=			AutoModelForSequenceClassification.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[Any]:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    List[Any]				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    str				=			TFAutoModelForQuestionAnswering.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			AutoModelForQuestionAnswering.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			TFAutoModelWithLMHead.from_pretrained(_snake_case			,from_pt=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)

				lowercase__   :    Union[str, Any]				=			AutoModelWithLMHead.from_pretrained(_snake_case			,from_tf=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)







		def 					UpperCAmelCase    (	self						:  int						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			TFAutoModelWithLMHead.from_pretrained(_snake_case			,from_pt=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)

				lowercase__   :    int				=			AutoModelWithLMHead.from_pretrained(_snake_case			,from_tf=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)
