




import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


SCREAMING_SNAKE_CASE_						:					int			   =       logging.get_logger(__name__)

if is_vision_available():
 import PIL.Image
 from PIL import Image
 from PIL.Image import Image as ImageType
else:
 SCREAMING_SNAKE_CASE_						:					Dict			   =       object

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf







class 		__UpperCAmelCase:





     """simple docstring"""



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






         """simple docstring"""
         a__      =       value

     def __str__(      self :       Optional[Any]    )			->			Optional[int]:






         """simple docstring"""
         return self.to_string()

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]    )			->			List[Any]:






         """simple docstring"""
         logger.error(
             """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""    )
         return self._value






     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]    )			->			str:






         """simple docstring"""
         logger.error(
             """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""    )
         return str(self._value    )







class 		__UpperCAmelCase(		lowercase__       ,	lowercase__ ):





     """simple docstring"""



     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			List[Any]:






         """simple docstring"""
         return self._value






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






         """simple docstring"""
         return self._value







class 		__UpperCAmelCase(		lowercase__       ,	lowercase__ ):





     """simple docstring"""



     def __init__(      self :       Optional[Any]						,				A :       List[Any]    )			->			Any:






         """simple docstring"""
         super().__init__(A    )

         if not is_vision_available():
             raise ImportError("""PIL must be installed in order to handle images."""    )

         a__      =       None
         a__      =       None
         a__      =       None

         if isinstance(A						,				A    ):
             a__      =       value
         elif isinstance(A						,				(str, pathlib.Path)    ):
             a__      =       value
         elif isinstance(A						,				torch.Tensor    ):
             a__      =       value
         else:
             raise ValueError(f"""Unsupported type for {self.__class__.__name__}: {type(A    )}"""    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]						,				A :       List[str]=None						,				A :       Tuple=None    )			->			Optional[Any]:






         """simple docstring"""
         from IPython.display import Image, display

         display(Image(self.to_string()    )    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			Any:






         """simple docstring"""
         if self._raw is not None:
             return self._raw

         if self._path is not None:
             a__      =       Image.open(self._path    )
             return self._raw






     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			Dict:






         """simple docstring"""
         if self._path is not None:
             return self._path

         if self._raw is not None:
             a__      =       tempfile.mkdtemp()
             a__      =       os.path.join(A						,				str(uuid.uuida()    ) + """.png"""    )
             self._raw.save(self._path    )

             return self._path

         if self._tensor is not None:
             a__      =       self._tensor.cpu().detach().numpy()

             # There is likely simpler than load into image into save
             a__      =       Image.fromarray((array * 2_55).astype(np.uinta    )    )

             a__      =       tempfile.mkdtemp()
             a__      =       os.path.join(A						,				str(uuid.uuida()    ) + """.png"""    )

             img.save(self._path    )

             return self._path







class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     def __init__(      self :       Optional[Any]						,				A :       Union[str, Any]						,				A :       List[Any]=1_60_00    )			->			List[Any]:






         """simple docstring"""
         super().__init__(A    )

         if not is_soundfile_availble():
             raise ImportError("""soundfile must be installed in order to handle audio."""    )

         a__      =       None
         a__      =       None

         a__      =       samplerate

         if isinstance(A						,				(str, pathlib.Path)    ):
             a__      =       value
         elif isinstance(A						,				torch.Tensor    ):
             a__      =       value
         else:
             raise ValueError(f"""Unsupported audio type: {type(A    )}"""    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       int						,				A :       List[str]=None						,				A :       List[str]=None    )			->			str:






         """simple docstring"""
         from IPython.display import Audio, display

         display(Audio(self.to_string()						,				rate=self.samplerate    )    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Tuple:






         """simple docstring"""
         if self._tensor is not None:
             return self._tensor

         if self._path is not None:
             a__ ,		a__      =       sf.read(self._path    )
             a__      =       torch.tensor(A    )
             return self._tensor






     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]    )			->			List[str]:






         """simple docstring"""
         if self._path is not None:
             return self._path

         if self._tensor is not None:
             a__      =       tempfile.mkdtemp()
             a__      =       os.path.join(A						,				str(uuid.uuida()    ) + """.wav"""    )
             sf.write(self._path						,				self._tensor						,				samplerate=self.samplerate    )
             return self._path






SCREAMING_SNAKE_CASE_						:					Dict			   =       {'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
SCREAMING_SNAKE_CASE_						:					int			   =       {str: AgentText}

if is_vision_available():
 SCREAMING_SNAKE_CASE_						:					Tuple			   =       AgentImage
def        __lowerCAmelCase (*UpperCAmelCase_   ,    **UpperCAmelCase_       ):
    a__      =       [(arg.to_raw() if isinstance(UpperCAmelCase_   ,    UpperCAmelCase_       ) else arg) for arg in args]
    a__      =       {k: (v.to_raw() if isinstance(UpperCAmelCase_   ,    UpperCAmelCase_       ) else v) for k, v in kwargs.items()}
    return args, kwargs







def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_=None       ):
    if isinstance(UpperCAmelCase_   ,    UpperCAmelCase_       ):
        a__      =       {}
        for i, (k, v) in enumerate(outputs.items()       ):
            if output_types is not None:
                # If the class has defined outputs, we can map directly according to the class definition
                if output_types[i] in AGENT_TYPE_MAPPING:
                    a__      =       AGENT_TYPE_MAPPING[output_types[i]](UpperCAmelCase_       )
                else:
                    a__      =       AgentType(UpperCAmelCase_       )

            else:
                # If the class does not have defined output, then we map according to the type
                for _k, _v in INSTANCE_TYPE_MAPPING.items():
                    if isinstance(UpperCAmelCase_   ,    _k       ):
                        a__      =       _v(UpperCAmelCase_       )
                if k not in decoded_outputs:
                    a__      =       AgentType[v]

    elif isinstance(UpperCAmelCase_   ,    (list, tuple)       ):
        a__      =       type(UpperCAmelCase_       )()
        for i, v in enumerate(UpperCAmelCase_       ):
            if output_types is not None:
                # If the class has defined outputs, we can map directly according to the class definition
                if output_types[i] in AGENT_TYPE_MAPPING:
                    decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](UpperCAmelCase_       )       )
                else:
                    decoded_outputs.append(AgentType(UpperCAmelCase_       )       )
            else:
                # If the class does not have defined output, then we map according to the type
                a__      =       False
                for _k, _v in INSTANCE_TYPE_MAPPING.items():
                    if isinstance(UpperCAmelCase_   ,    _k       ):
                        decoded_outputs.append(_v(UpperCAmelCase_       )       )
                        a__      =       True

                if not found:
                    decoded_outputs.append(AgentType(UpperCAmelCase_       )       )

    else:
        if output_types[0] in AGENT_TYPE_MAPPING:
            # If the class has defined outputs, we can map directly according to the class definition
            a__      =       AGENT_TYPE_MAPPING[output_types[0]](UpperCAmelCase_       )

        else:
            # If the class does not have defined output, then we map according to the type
            for _k, _v in INSTANCE_TYPE_MAPPING.items():
                if isinstance(UpperCAmelCase_   ,    _k       ):
                    return _v(UpperCAmelCase_       )
            return AgentType(UpperCAmelCase_       )

    return decoded_outputs












import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:







 class 		__UpperCAmelCase:





      """simple docstring"""



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






          """simple docstring"""
          pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



     __UpperCAmelCase	: Tuple						=    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     __UpperCAmelCase	: Dict						=    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

     def 				__SCREAMING_SNAKE_CASE		(      self :       str						,				A :       Optional[int]						,				A :       Tuple						,				A :       int    )			->			str:






         """simple docstring"""
         a__      =       ImageClassificationPipeline(model=A						,				image_processor=A						,				top_k=2    )
         a__      =       [
             Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    ),
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
         ]
         return image_classifier, examples

     def 				__SCREAMING_SNAKE_CASE		(      self :       Any						,				A :       List[str]						,				A :       Dict    )			->			Union[str, Any]:






         """simple docstring"""
         a__      =       image_classifier("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )

         self.assertEqual(
             A						,				[
                 {"""score""": ANY(A    ), """label""": ANY(A    )},
                 {"""score""": ANY(A    ), """label""": ANY(A    )},
             ]						,				)

         import datasets

         a__      =       datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""						,				"""image"""						,				split="""test"""    )

         # Accepts URL + PIL.Image + lists
         a__      =       image_classifier(
             [
                 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(
             A						,				[
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
             ]						,				)

     @require_torch
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       """hf-internal-testing/tiny-random-vit"""
         a__      =       pipeline("""image-classification"""						,				model=A    )

         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}]						,				)

         a__      =       image_classifier(
             [
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
             ]						,				top_k=2						,				)
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
                 [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
             ]						,				)

     @require_tf
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			str:






         """simple docstring"""
         a__      =       """hf-internal-testing/tiny-random-vit"""
         a__      =       pipeline("""image-classification"""						,				model=A						,				framework="""tf"""    )

         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}]						,				)

         a__      =       image_classifier(
             [
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
             ]						,				top_k=2						,				)
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
                 [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
             ]						,				)

     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			str:






         """simple docstring"""
         a__      =       PreTrainedTokenizer()

         # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
         a__      =       pipeline(
             """image-classification"""						,				model="""hf-internal-testing/tiny-random-vit"""						,				tokenizer=A    )

         self.assertIs(image_classifier.tokenizer						,				A    )






     @slow
     @require_torch
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			Dict:






         """simple docstring"""
         # Perceiver is not tested by `run_pipeline_test` properly.
         # That is because the type of feature_extractor and model preprocessor need to be kept
         # in sync, which is not the case in the current design
         a__      =       pipeline("""image-classification"""						,				model="""deepmind/vision-perceiver-conv"""    )
         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 {"""score""": 0.4_3_8_5, """label""": """tabby, tabby cat"""},
                 {"""score""": 0.3_2_1, """label""": """tiger cat"""},
                 {"""score""": 0.0_5_0_2, """label""": """Egyptian cat"""},
                 {"""score""": 0.0_1_3_7, """label""": """crib, cot"""},
                 {"""score""": 0.0_0_7, """label""": """radiator"""},
             ]						,				)

         a__      =       pipeline("""image-classification"""						,				model="""deepmind/vision-perceiver-fourier"""    )
         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 {"""score""": 0.5_6_5_8, """label""": """tabby, tabby cat"""},
                 {"""score""": 0.1_3_0_9, """label""": """tiger cat"""},
                 {"""score""": 0.0_7_2_2, """label""": """Egyptian cat"""},
                 {"""score""": 0.0_7_0_7, """label""": """remote control, remote"""},
                 {"""score""": 0.0_0_8_2, """label""": """computer keyboard, keypad"""},
             ]						,				)

         a__      =       pipeline("""image-classification"""						,				model="""deepmind/vision-perceiver-learned"""    )
         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 {"""score""": 0.3_0_2_2, """label""": """tabby, tabby cat"""},
                 {"""score""": 0.2_3_6_2, """label""": """Egyptian cat"""},
                 {"""score""": 0.1_8_5_6, """label""": """tiger cat"""},
                 {"""score""": 0.0_3_2_4, """label""": """remote control, remote"""},
                 {"""score""": 0.0_0_9_6, """label""": """quilt, comforter, comfort, puff"""},
             ]						,				)






