




'''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      lowerCAmelCase :


            @staticmethod
            def    lowerCAmelCase   ( *__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	Union[str, Any]:



                 '''simple docstring'''




                 pass







def       _UpperCamelCase			(_lowerCamelCase				:  Image						)-> Dict:




     '''simple docstring'''





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







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class      lowerCAmelCase (			unittest.TestCase):
       __lowercase	:   int										=      MODEL_FOR_DEPTH_ESTIMATION_MAPPING


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	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    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	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

            __snake_case            =	datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''     ,	'''image'''     ,	split='''test'''	)
            __snake_case            =	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    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            pass


       @slow
       @require_torch
       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	"""Intel/dpt-large"""
            __snake_case            =	pipeline('''depth-estimation'''     ,	model=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''	)
            __snake_case            =	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	)   ->	List[Any]:



            '''simple docstring'''




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










"""simple docstring"""
from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


UpperCAmelCase				=						logging.get_logger(__name__)


UpperCAmelCase				=						OrderedDict(
    [
        # Base model mapping
        ("""albert""", """FlaxAlbertModel"""),
        ("""bart""", """FlaxBartModel"""),
        ("""beit""", """FlaxBeitModel"""),
        ("""bert""", """FlaxBertModel"""),
        ("""big_bird""", """FlaxBigBirdModel"""),
        ("""blenderbot""", """FlaxBlenderbotModel"""),
        ("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
        ("""clip""", """FlaxCLIPModel"""),
        ("""distilbert""", """FlaxDistilBertModel"""),
        ("""electra""", """FlaxElectraModel"""),
        ("""gpt-sw3""", """FlaxGPT2Model"""),
        ("""gpt2""", """FlaxGPT2Model"""),
        ("""gpt_neo""", """FlaxGPTNeoModel"""),
        ("""gptj""", """FlaxGPTJModel"""),
        ("""longt5""", """FlaxLongT5Model"""),
        ("""marian""", """FlaxMarianModel"""),
        ("""mbart""", """FlaxMBartModel"""),
        ("""mt5""", """FlaxMT5Model"""),
        ("""opt""", """FlaxOPTModel"""),
        ("""pegasus""", """FlaxPegasusModel"""),
        ("""regnet""", """FlaxRegNetModel"""),
        ("""resnet""", """FlaxResNetModel"""),
        ("""roberta""", """FlaxRobertaModel"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
        ("""roformer""", """FlaxRoFormerModel"""),
        ("""t5""", """FlaxT5Model"""),
        ("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
        ("""vit""", """FlaxViTModel"""),
        ("""wav2vec2""", """FlaxWav2Vec2Model"""),
        ("""whisper""", """FlaxWhisperModel"""),
        ("""xglm""", """FlaxXGLMModel"""),
        ("""xlm-roberta""", """FlaxXLMRobertaModel"""),
    ]
)

UpperCAmelCase				=						OrderedDict(
    [
        # Model for pre-training mapping
        ("""albert""", """FlaxAlbertForPreTraining"""),
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""bert""", """FlaxBertForPreTraining"""),
        ("""big_bird""", """FlaxBigBirdForPreTraining"""),
        ("""electra""", """FlaxElectraForPreTraining"""),
        ("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""mt5""", """FlaxMT5ForConditionalGeneration"""),
        ("""roberta""", """FlaxRobertaForMaskedLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
        ("""roformer""", """FlaxRoFormerForMaskedLM"""),
        ("""t5""", """FlaxT5ForConditionalGeneration"""),
        ("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
    ]
)

UpperCAmelCase				=						OrderedDict(
    [
        # Model for Masked LM mapping
        ("""albert""", """FlaxAlbertForMaskedLM"""),
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""bert""", """FlaxBertForMaskedLM"""),
        ("""big_bird""", """FlaxBigBirdForMaskedLM"""),
        ("""distilbert""", """FlaxDistilBertForMaskedLM"""),
        ("""electra""", """FlaxElectraForMaskedLM"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""roberta""", """FlaxRobertaForMaskedLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
        ("""roformer""", """FlaxRoFormerForMaskedLM"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
    ]
)

UpperCAmelCase				=						OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("""bart""", """FlaxBartForConditionalGeneration"""),
        ("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
        ("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
        ("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
        ("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
        ("""marian""", """FlaxMarianMTModel"""),
        ("""mbart""", """FlaxMBartForConditionalGeneration"""),
        ("""mt5""", """FlaxMT5ForConditionalGeneration"""),
        ("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
        ("""t5""", """FlaxT5ForConditionalGeneration"""),
    ]
)

UpperCAmelCase				=						OrderedDict(
    [
        # Model for Image-classsification
        ("""beit""", """FlaxBeitForImageClassification"""),
        ("""regnet""", """FlaxRegNetForImageClassification"""),
        ("""resnet""", """FlaxResNetForImageClassification"""),
        ("""vit""", """FlaxViTForImageClassification"""),
    ]
)

UpperCAmelCase				=						OrderedDict(
    [
        ("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
    ]
)

UpperCAmelCase				=						OrderedDict(
    [
        # Model for Causal LM mapping
        ("""bart""", """FlaxBartForCausalLM"""),
        ("""bert""", """FlaxBertForCausalLM"""),
        ("""big_bird""", """FlaxBigBirdForCausalLM"""),
        ("""electra""", """FlaxElectraForCausalLM"""),
        ("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
        ("""gpt2""", """FlaxGPT2LMHeadModel"""),
        ("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
        ("""gptj""", """FlaxGPTJForCausalLM"""),
        ("""opt""", """FlaxOPTForCausalLM"""),
        ("""roberta""", """FlaxRobertaForCausalLM"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
        ("""xglm""", """FlaxXGLMForCausalLM"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
    ]
)

UpperCAmelCase				=						OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("""albert""", """FlaxAlbertForSequenceClassification"""),
        ("""bart""", """FlaxBartForSequenceClassification"""),
        ("""bert""", """FlaxBertForSequenceClassification"""),
        ("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
        ("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
        ("""electra""", """FlaxElectraForSequenceClassification"""),
        ("""mbart""", """FlaxMBartForSequenceClassification"""),
        ("""roberta""", """FlaxRobertaForSequenceClassification"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
        ("""roformer""", """FlaxRoFormerForSequenceClassification"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
    ]
)

UpperCAmelCase				=						OrderedDict(
    [
        # Model for Question Answering mapping
        ("""albert""", """FlaxAlbertForQuestionAnswering"""),
        ("""bart""", """FlaxBartForQuestionAnswering"""),
        ("""bert""", """FlaxBertForQuestionAnswering"""),
        ("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
        ("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
        ("""electra""", """FlaxElectraForQuestionAnswering"""),
        ("""mbart""", """FlaxMBartForQuestionAnswering"""),
        ("""roberta""", """FlaxRobertaForQuestionAnswering"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
        ("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
    ]
)

UpperCAmelCase				=						OrderedDict(
    [
        # Model for Token Classification mapping
        ("""albert""", """FlaxAlbertForTokenClassification"""),
        ("""bert""", """FlaxBertForTokenClassification"""),
        ("""big_bird""", """FlaxBigBirdForTokenClassification"""),
        ("""distilbert""", """FlaxDistilBertForTokenClassification"""),
        ("""electra""", """FlaxElectraForTokenClassification"""),
        ("""roberta""", """FlaxRobertaForTokenClassification"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
        ("""roformer""", """FlaxRoFormerForTokenClassification"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
    ]
)

UpperCAmelCase				=						OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("""albert""", """FlaxAlbertForMultipleChoice"""),
        ("""bert""", """FlaxBertForMultipleChoice"""),
        ("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
        ("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
        ("""electra""", """FlaxElectraForMultipleChoice"""),
        ("""roberta""", """FlaxRobertaForMultipleChoice"""),
        ("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
        ("""roformer""", """FlaxRoFormerForMultipleChoice"""),
        ("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
    ]
)

UpperCAmelCase				=						OrderedDict(
    [
        ("""bert""", """FlaxBertForNextSentencePrediction"""),
    ]
)

UpperCAmelCase				=						OrderedDict(
    [
        ("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
    ]
)

UpperCAmelCase				=						OrderedDict(
    [
        ("""whisper""", """FlaxWhisperForAudioClassification"""),
    ]
)

UpperCAmelCase				=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase				=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase				=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase				=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase				=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase				=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase				=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase				=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase				=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase				=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase				=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase				=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase				=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase				=						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)




class 	lowercase__				(					_BaseAutoModelClass				):
   __UpperCAmelCase							       =					FLAX_MODEL_MAPPING







UpperCAmelCase				=						auto_class_update(FlaxAutoModel)




class 	lowercase__				(					_BaseAutoModelClass				):
   __UpperCAmelCase							       =					FLAX_MODEL_FOR_PRETRAINING_MAPPING







UpperCAmelCase				=						auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")




class 	lowercase__				(					_BaseAutoModelClass				):
   __UpperCAmelCase							       =					FLAX_MODEL_FOR_CAUSAL_LM_MAPPING







UpperCAmelCase				=						auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")




class 	lowercase__				(					_BaseAutoModelClass				):
   __UpperCAmelCase							       =					FLAX_MODEL_FOR_MASKED_LM_MAPPING







UpperCAmelCase				=						auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")




class 	lowercase__				(					_BaseAutoModelClass				):
   __UpperCAmelCase							       =					FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







UpperCAmelCase				=						auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)




class 	lowercase__				(					_BaseAutoModelClass				):
   __UpperCAmelCase							       =					FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







UpperCAmelCase				=						auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)




class 	lowercase__				(					_BaseAutoModelClass				):
   __UpperCAmelCase							       =					FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING







UpperCAmelCase				=						auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")




class 	lowercase__				(					_BaseAutoModelClass				):
   __UpperCAmelCase							       =					FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING







UpperCAmelCase				=						auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)




class 	lowercase__				(					_BaseAutoModelClass				):
   __UpperCAmelCase							       =					FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING







UpperCAmelCase				=						auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")




class 	lowercase__				(					_BaseAutoModelClass				):
   __UpperCAmelCase							       =					FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING







UpperCAmelCase				=						auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)




class 	lowercase__				(					_BaseAutoModelClass				):
   __UpperCAmelCase							       =					FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







UpperCAmelCase				=						auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="""image classification"""
)




class 	lowercase__				(					_BaseAutoModelClass				):
   __UpperCAmelCase							       =					FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING







UpperCAmelCase				=						auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")




class 	lowercase__				(					_BaseAutoModelClass				):
   __UpperCAmelCase							       =					FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING







UpperCAmelCase				=						auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)

