





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__SCREAMING_SNAKE_CASE							:    Dict          =logging.get_logger(__name__)


if is_vision_available():
					import PIL

class     A_		(			__a	):
			_A   :str  				=      ['''pixel_values''']







			def __init__(				self  :							Dict   ,      snake_case__  :							bool = True   ,      snake_case__  :							Dict[str, int] = None   ,      snake_case__  :							PILImageResampling = PILImageResampling.BICUBIC   ,      snake_case__  :							bool = True   ,      snake_case__  :							Dict[str, int] = None   ,      snake_case__  :							bool = True   ,      snake_case__  :							Union[int, float] = 1 / 2_55   ,      snake_case__  :							bool = True   ,      snake_case__  :							Optional[Union[float, List[float]]] = None   ,      snake_case__  :							Optional[Union[float, List[float]]] = None   ,      snake_case__  :							bool = True   ,      **snake_case__  :							Tuple   ,      ):
							super().__init__(**__lowercase      )
							lowercase							=     size if size is not None else {"""shortest_edge""": 2_24}
							lowercase							=     get_size_dict(__lowercase   ,      default_to_square=__lowercase      )
							lowercase							=     crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
							lowercase							=     get_size_dict(__lowercase   ,      default_to_square=__lowercase   ,      param_name="""crop_size"""      )

							lowercase							=     do_resize
							lowercase							=     size
							lowercase							=     resample
							lowercase							=     do_center_crop
							lowercase							=     crop_size
							lowercase							=     do_rescale
							lowercase							=     rescale_factor
							lowercase							=     do_normalize
							lowercase							=     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							lowercase							=     image_std if image_std is not None else OPENAI_CLIP_STD
							lowercase							=     do_convert_rgb







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Optional[Any]   ,      snake_case__  :							np.ndarray   ,      snake_case__  :							Dict[str, int]   ,      snake_case__  :							PILImageResampling = PILImageResampling.BICUBIC   ,      snake_case__  :							Optional[Union[str, ChannelDimension]] = None   ,      **snake_case__  :							List[Any]   ,      ):
							lowercase							=     get_size_dict(__lowercase   ,      default_to_square=__lowercase      )
							if "shortest_edge" not in size:
											raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""      )
							lowercase							=     get_resize_output_image_size(__lowercase   ,      size=size["""shortest_edge"""]   ,      default_to_square=__lowercase      )
							return resize(__lowercase   ,      size=__lowercase   ,      resample=__lowercase   ,      data_format=__lowercase   ,      **__lowercase      )







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Union[str, Any]   ,      snake_case__  :							np.ndarray   ,      snake_case__  :							Dict[str, int]   ,      snake_case__  :							Optional[Union[str, ChannelDimension]] = None   ,      **snake_case__  :							List[str]   ,      ):
							lowercase							=     get_size_dict(__lowercase      )
							if "height" not in size or "width" not in size:
											raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""      )
							return center_crop(__lowercase   ,      size=(size["""height"""], size["""width"""])   ,      data_format=__lowercase   ,      **__lowercase      )







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Any   ,      snake_case__  :							np.ndarray   ,      snake_case__  :							Union[int, float]   ,      snake_case__  :							Optional[Union[str, ChannelDimension]] = None   ,      **snake_case__  :							List[str]   ,      ):
							return rescale(__lowercase   ,      scale=__lowercase   ,      data_format=__lowercase   ,      **__lowercase      )







			def 						SCREAMING_SNAKE_CASE__	(				self  :							List[Any]   ,      snake_case__  :							np.ndarray   ,      snake_case__  :							Union[float, List[float]]   ,      snake_case__  :							Union[float, List[float]]   ,      snake_case__  :							Optional[Union[str, ChannelDimension]] = None   ,      **snake_case__  :							Optional[Any]   ,      ):
							return normalize(__lowercase   ,      mean=__lowercase   ,      std=__lowercase   ,      data_format=__lowercase   ,      **__lowercase      )







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Optional[Any]   ,      snake_case__  :							ImageInput   ,      snake_case__  :							bool = None   ,      snake_case__  :							Dict[str, int] = None   ,      snake_case__  :							PILImageResampling = None   ,      snake_case__  :							bool = None   ,      snake_case__  :							int = None   ,      snake_case__  :							bool = None   ,      snake_case__  :							float = None   ,      snake_case__  :							bool = None   ,      snake_case__  :							Optional[Union[float, List[float]]] = None   ,      snake_case__  :							Optional[Union[float, List[float]]] = None   ,      snake_case__  :							bool = None   ,      snake_case__  :							Optional[Union[str, TensorType]] = None   ,      snake_case__  :							Optional[ChannelDimension] = ChannelDimension.FIRST   ,      **snake_case__  :							List[Any]   ,      ):
							lowercase							=     do_resize if do_resize is not None else self.do_resize
							lowercase							=     size if size is not None else self.size
							lowercase							=     get_size_dict(__lowercase   ,      param_name="""size"""   ,      default_to_square=__lowercase      )
							lowercase							=     resample if resample is not None else self.resample
							lowercase							=     do_center_crop if do_center_crop is not None else self.do_center_crop
							lowercase							=     crop_size if crop_size is not None else self.crop_size
							lowercase							=     get_size_dict(__lowercase   ,      param_name="""crop_size"""   ,      default_to_square=__lowercase      )
							lowercase							=     do_rescale if do_rescale is not None else self.do_rescale
							lowercase							=     rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase							=     do_normalize if do_normalize is not None else self.do_normalize
							lowercase							=     image_mean if image_mean is not None else self.image_mean
							lowercase							=     image_std if image_std is not None else self.image_std
							lowercase							=     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							lowercase							=     make_list_of_images(__lowercase      )

							if not valid_images(__lowercase      ):
											raise ValueError(
											    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
											    """torch.Tensor, tf.Tensor or jax.ndarray."""      )

							if do_resize and size is None:
											raise ValueError("""Size must be specified if do_resize is True."""      )

							if do_center_crop and crop_size is None:
											raise ValueError("""Crop size must be specified if do_center_crop is True."""      )

							if do_rescale and rescale_factor is None:
											raise ValueError("""Rescale factor must be specified if do_rescale is True."""      )

							if do_normalize and (image_mean is None or image_std is None):
											raise ValueError("""Image mean and std must be specified if do_normalize is True."""      )

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
											lowercase							=     [convert_to_rgb(__lowercase      ) for image in images]

							# All transformations expect numpy arrays.
							lowercase							=     [to_numpy_array(__lowercase      ) for image in images]

							if do_resize:
											lowercase							=     [self.resize(image=__lowercase   ,      size=__lowercase   ,      resample=__lowercase      ) for image in images]

							if do_center_crop:
											lowercase							=     [self.center_crop(image=__lowercase   ,      size=__lowercase      ) for image in images]

							if do_rescale:
											lowercase							=     [self.rescale(image=__lowercase   ,      scale=__lowercase      ) for image in images]

							if do_normalize:
											lowercase							=     [self.normalize(image=__lowercase   ,      mean=__lowercase   ,      std=__lowercase      ) for image in images]

							lowercase							=     [to_channel_dimension_format(__lowercase   ,      __lowercase      ) for image in images]

							lowercase							=     {"""pixel_values""": images}
							return BatchFeature(data=__lowercase   ,      tensor_type=__lowercase      )





'''simple docstring'''

import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


UpperCAmelCase					 =	get_tests_dir('''fixtures''')


class 	lowerCAmelCase  (   unittest.TestCase ):







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=mock.Mock()
            __lowercase      		=500
            __lowercase      		={}
            __lowercase      		=HTTPError
            __lowercase      		={}

            # Download this model to make sure it's in the cache.
            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
            # Under the mock environment we get a 500 error when trying to reach the model.
            with mock.patch('requests.Session.request'    ,   return_value=__lowercase   ) as mock_head:
                 __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
                 # This check we did call the fake head request
                 mock_head.assert_called()







       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(
                'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'   )






@is_staging_test
class 	lowerCAmelCase  (   unittest.TestCase ):







       @classmethod
       def       snake_case (				cls      :			str   ):







            """simple docstring"""







            __lowercase      		=TOKEN
            HfFolder.save_token(__lowercase   )







       @classmethod
       def       snake_case (				cls      :			str   ):







            """simple docstring"""







            try:
                 delete_repo(token=cls._token    ,   repo_id='test-feature-extractor'   )
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,   repo_id='valid_org/test-feature-extractor-org'   )
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,   repo_id='test-dynamic-feature-extractor'   )
            except HTTPError:
                 pass







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(__lowercase   )
            feature_extractor.push_to_hub('test-feature-extractor'    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )

            # Reset repo
            delete_repo(token=self._token    ,   repo_id='test-feature-extractor'   )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     __lowercase    ,   repo_id='test-feature-extractor'    ,   push_to_hub=__lowercase    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(__lowercase   )
            feature_extractor.push_to_hub('valid_org/test-feature-extractor'    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )

            # Reset repo
            delete_repo(token=self._token    ,   repo_id='valid_org/test-feature-extractor'   )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     __lowercase    ,   repo_id='valid_org/test-feature-extractor-org'    ,   push_to_hub=__lowercase    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            CustomFeatureExtractor.register_for_auto_class()
            __lowercase      		=CustomFeatureExtractor.from_pretrained(__lowercase   )

            feature_extractor.push_to_hub('test-dynamic-feature-extractor'    ,   use_auth_token=self._token   )

            # This has added the proper auto_map field to the config
            self.assertDictEqual(
                feature_extractor.auto_map    ,   {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'}    ,   )

            __lowercase      		=AutoFeatureExtractor.from_pretrained(
                f'''{USER}/test-dynamic-feature-extractor'''    ,   trust_remote_code=__lowercase   )
            # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
            self.assertEqual(new_feature_extractor.__class__.__name__    ,   'CustomFeatureExtractor'   )


