






import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard

class        snake_case_				(							unittest.TestCase   ):







   """simple docstring"""



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


       """simple docstring"""



       _lowercase     			=						{
           'model_details': {
               'Organization': 'testing',
               'Model date': 'today',
               'Model version': 'v2.1, Developed by Test Corp in 2019.',
               'Architecture': 'Convolutional Neural Network.',
           },
           'metrics': 'BLEU and ROUGE-1',
           'evaluation_data': {
               'Datasets': {'BLEU': 'My-great-dataset-v1', 'ROUGE-1': 'My-short-dataset-v2.1'},
               'Preprocessing': 'See details on https://arxiv.org/pdf/1810.03993.pdf',
           },
           'training_data': {
               'Dataset': 'English Wikipedia dump dated 2018-12-01',
               'Preprocessing': (
                   'Using SentencePiece vocabulary of size 52k tokens. See details on'
                   ' https://arxiv.org/pdf/1810.03993.pdf'
               ),
           },
           'quantitative_analyses': {'BLEU': 55.1, 'ROUGE-1': 76},
       }

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		List[str]:


       """simple docstring"""



       _lowercase     			=						ModelCard.from_dict(self.inputs_dict						)
       self.assertTrue(hasattr(_A ,							'model_details'						)						)
       self.assertTrue(hasattr(_A ,							'intended_use'						)						)
       self.assertTrue(hasattr(_A ,							'factors'						)						)
       self.assertTrue(hasattr(_A ,							'metrics'						)						)
       self.assertTrue(hasattr(_A ,							'evaluation_data'						)						)
       self.assertTrue(hasattr(_A ,							'training_data'						)						)
       self.assertTrue(hasattr(_A ,							'quantitative_analyses'						)						)
       self.assertTrue(hasattr(_A ,							'ethical_considerations'						)						)
       self.assertTrue(hasattr(_A ,							'caveats_and_recommendations'						)						)

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


       """simple docstring"""



       _lowercase     			=						ModelCard.from_dict(self.inputs_dict						)
       _lowercase     			=						json.loads(modelcard.to_json_string()						)
       for key, value in self.inputs_dict.items():
           self.assertEqual(obj[key] ,							_A						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		int:


       """simple docstring"""



       _lowercase     			=						ModelCard.from_dict(self.inputs_dict						)

       with tempfile.TemporaryDirectory() as tmpdirname:
           _lowercase     			=						os.path.join(_A ,							'modelcard.json'						)
           model_card_first.to_json_file(_A						)
           _lowercase     			=						ModelCard.from_json_file(_A						)

       self.assertEqual(model_card_second.to_dict() ,							model_card_first.to_dict()						)

   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						ModelCard.from_dict(self.inputs_dict						)

       with tempfile.TemporaryDirectory() as tmpdirname:
           model_card_first.save_pretrained(_A						)
           _lowercase     			=						ModelCard.from_pretrained(_A						)

       self.assertEqual(model_card_second.to_dict() ,							model_card_first.to_dict()						)



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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase__	:   Union[str, Any]								=     logging.get_logger(__name__)



class    __snake_case	(     lowerCAmelCase__							):
  __lowerCAmelCase  :						Tuple    			=	['pixel_values']

  def __init__(       self    ,		_A = True    ,		_A = None    ,		_A = PILImageResampling.BILINEAR    ,		_A = True    ,		_A = 1 / 255    ,		_A = True    ,		_A = None    ,		_A = None    ,		**_A    ,		):
      super().__init__(**_A)
      SCREAMING_SNAKE_CASE_						=    size if size is not None else {'height': 224, 'width': 224}
      SCREAMING_SNAKE_CASE_						=    get_size_dict(_A)
      SCREAMING_SNAKE_CASE_						=    do_resize
      SCREAMING_SNAKE_CASE_						=    do_rescale
      SCREAMING_SNAKE_CASE_						=    do_normalize
      SCREAMING_SNAKE_CASE_						=    size
      SCREAMING_SNAKE_CASE_						=    resample
      SCREAMING_SNAKE_CASE_						=    rescale_factor
      SCREAMING_SNAKE_CASE_						=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      SCREAMING_SNAKE_CASE_						=    image_std if image_std is not None else IMAGENET_STANDARD_STD

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A = PILImageResampling.BILINEAR    ,		_A = None    ,		**_A    ,		):
      SCREAMING_SNAKE_CASE_						=    get_size_dict(_A)
      if "height" not in size or "width" not in size:
          raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""")
      return resize(
          _A    ,		size=(size['height'], size['width'])    ,		resample=_A    ,		data_format=_A    ,		**_A)

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A = None    ,		**_A):
      return rescale(_A    ,		scale=_A    ,		data_format=_A    ,		**_A)

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A    ,		_A = None    ,		**_A    ,		):
      return normalize(_A    ,		mean=_A    ,		std=_A    ,		data_format=_A    ,		**_A)


  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = ChannelDimension.FIRST    ,		**_A    ,		):
      SCREAMING_SNAKE_CASE_						=    do_resize if do_resize is not None else self.do_resize
      SCREAMING_SNAKE_CASE_						=    do_rescale if do_rescale is not None else self.do_rescale
      SCREAMING_SNAKE_CASE_						=    do_normalize if do_normalize is not None else self.do_normalize
      SCREAMING_SNAKE_CASE_						=    resample if resample is not None else self.resample
      SCREAMING_SNAKE_CASE_						=    rescale_factor if rescale_factor is not None else self.rescale_factor
      SCREAMING_SNAKE_CASE_						=    image_mean if image_mean is not None else self.image_mean
      SCREAMING_SNAKE_CASE_						=    image_std if image_std is not None else self.image_std

      SCREAMING_SNAKE_CASE_						=    size if size is not None else self.size
      SCREAMING_SNAKE_CASE_						=    get_size_dict(_A)

      SCREAMING_SNAKE_CASE_						=    make_list_of_images(_A)

      if not valid_images(_A):
          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_rescale and rescale_factor is None:
          raise ValueError('Rescale factor must be specified if do_rescale is True.')

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

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

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

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

      SCREAMING_SNAKE_CASE_						=    [to_channel_dimension_format(_A    ,		_A) for image in images]

      SCREAMING_SNAKE_CASE_						=    {'pixel_values': images}
      return BatchFeature(data=_A    ,		tensor_type=_A)


