import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import GLPNImageProcessor






class 		_lowerCamelCase		( unittest.TestCase     ):
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase=7			,			lowerCAmelCase=3			,			lowerCAmelCase=18			,			lowerCAmelCase=30			,			lowerCAmelCase=400			,			lowerCAmelCase=True			,			lowerCAmelCase=32			,			lowerCAmelCase=True			,			)							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Dict=   parent
     SCREAMING_SNAKE_CASE__: int=   batch_size
     SCREAMING_SNAKE_CASE__: Optional[Any]=   num_channels
     SCREAMING_SNAKE_CASE__: str=   image_size
     SCREAMING_SNAKE_CASE__: Dict=   min_resolution
     SCREAMING_SNAKE_CASE__: Dict=   max_resolution
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   do_resize
     SCREAMING_SNAKE_CASE__: Optional[int]=   size_divisor
     SCREAMING_SNAKE_CASE__: List[Any]=   do_rescale



    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     return {
         "do_resize": self.do_resize,
         "size_divisor": self.size_divisor,
         "do_rescale": self.do_rescale,
     }






@require_torch
@require_vision
class 		_lowerCamelCase		( UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   GLPNImageProcessor if is_vision_available() else None
    def     UpperCamelCase_					(	self )							->       str:
     SCREAMING_SNAKE_CASE__: Any=   GLPNImageProcessingTester(self )
    @property
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     return self.image_processor_tester.prepare_image_processor_dict()
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: List[Any]=   self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''do_resize''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''size_divisor''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''resample''' ) )
     self.assertTrue(hasattr(lowerCAmelCase			,			'''do_rescale''' ) )
    def     UpperCamelCase_					(	self )							->       List[Any]:
     pass
    def     UpperCamelCase_					(	self )							->       List[str]:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__: int=   self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     SCREAMING_SNAKE_CASE__: str=   prepare_image_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase )
     for image in image_inputs:
      self.assertIsInstance(lowerCAmelCase			,			Image.Image )

     # Test not batched input (GLPNImageProcessor doesn't support batching)
     SCREAMING_SNAKE_CASE__: List[Any]=   image_processing(image_inputs[0]			,			return_tensors='''pt''' ).pixel_values
     self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
     self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__: List[str]=   self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     SCREAMING_SNAKE_CASE__: str=   prepare_image_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase			,			numpify=lowerCAmelCase )
     for image in image_inputs:
      self.assertIsInstance(lowerCAmelCase			,			np.ndarray )

     # Test not batched input (GLPNImageProcessor doesn't support batching)
     SCREAMING_SNAKE_CASE__: int=   image_processing(image_inputs[0]			,			return_tensors='''pt''' ).pixel_values
     self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
     self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )



    def     UpperCamelCase_					(	self )							->       str:
     # Initialize image_processing
     SCREAMING_SNAKE_CASE__: Tuple=   self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE__: int=   prepare_image_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase			,			torchify=lowerCAmelCase )
     for image in image_inputs:
      self.assertIsInstance(lowerCAmelCase			,			torch.Tensor )

     # Test not batched input (GLPNImageProcessor doesn't support batching)
     SCREAMING_SNAKE_CASE__: Any=   image_processing(image_inputs[0]			,			return_tensors='''pt''' ).pixel_values
     self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
     self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )


'''simple docstring'''




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate






@dataclass(frozen=lowerCAmelCase__      )
class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			field(default='''automatic-speech-recognition'''					,		metadata={'''include_in_asdict_even_if_is_default''': True}      )
			UpperCAmelCase_     :	ClassVar[Features]						=			Features({'''audio''': Audio()}      )
			UpperCAmelCase_     :	ClassVar[Features]						=			Features({'''transcription''': Value('''string'''      )}      )
			UpperCAmelCase_     :	str						=			"audio"
			UpperCAmelCase_     :	str						=			"transcription"



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					if self.audio_column not in features:
							raise ValueError(f"Column {self.audio_column} is not present in features.")
					if not isinstance(features[self.audio_column]			,       __lowerCAmelCase):
							raise ValueError(f"Column {self.audio_column} is not an Audio type.")
					lowerCAmelCase     =    copy.deepcopy(self)
					lowerCAmelCase     =    self.input_schema.copy()
					lowerCAmelCase     =    features[self.audio_column]
					lowerCAmelCase     =    input_schema
					return task_template







			@property
			def 				a_			(  self):




					"""simple docstring"""


					return {self.audio_column: "audio", self.transcription_column: "transcription"}




