


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


UpperCamelCase								=	logging.get_logger(__name__)


if is_vision_available():
     import PIL






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    ["pixel_values"]



       def __init__(      self				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = 1 / 255				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = True				,       **_SCREAMING_SNAKE_CASE				,       )->None:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)
         A_      :				int		     =					size if size is not None else {'''shortest_edge''': 224}
         A_      :				Tuple		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				int		     =					crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         A_      :				Any		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE				,       param_name='''crop_size'''		)

         A_      :				List[Any]		     =					do_resize
         A_      :				int		     =					size
         A_      :				int		     =					resample
         A_      :				Optional[int]		     =					do_center_crop
         A_      :				Optional[int]		     =					crop_size
         A_      :				Optional[Any]		     =					do_rescale
         A_      :				Union[str, Any]		     =					rescale_factor
         A_      :				List[str]		     =					do_normalize
         A_      :				List[Any]		     =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         A_      :				Optional[Any]		     =					image_std if image_std is not None else OPENAI_CLIP_STD
         A_      :				Any		     =					do_convert_rgb



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->np.ndarray:




         '''simple docstring'''





         A_      :				Tuple		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
         A_      :				Any		     =					get_resize_output_image_size(_SCREAMING_SNAKE_CASE				,       size=size['''shortest_edge''']				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         return resize(_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE				,       resample=_SCREAMING_SNAKE_CASE				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->np.ndarray:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					get_size_dict(_SCREAMING_SNAKE_CASE		)
         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(_SCREAMING_SNAKE_CASE				,       size=(size['''height'''], size['''width'''])				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->Optional[Any]:




         '''simple docstring'''





         return rescale(_SCREAMING_SNAKE_CASE				,       scale=_SCREAMING_SNAKE_CASE				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->np.ndarray:




         '''simple docstring'''





         return normalize(_SCREAMING_SNAKE_CASE				,       mean=_SCREAMING_SNAKE_CASE				,       std=_SCREAMING_SNAKE_CASE				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST				,       **_SCREAMING_SNAKE_CASE				,       )->PIL.Image.Image:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					do_resize if do_resize is not None else self.do_resize
         A_      :				Optional[int]		     =					size if size is not None else self.size
         A_      :				List[str]		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       param_name='''size'''				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					resample if resample is not None else self.resample
         A_      :				Union[str, Any]		     =					do_center_crop if do_center_crop is not None else self.do_center_crop
         A_      :				List[str]		     =					crop_size if crop_size is not None else self.crop_size
         A_      :				Tuple		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       param_name='''crop_size'''				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					do_rescale if do_rescale is not None else self.do_rescale
         A_      :				List[str]		     =					rescale_factor if rescale_factor is not None else self.rescale_factor
         A_      :				Tuple		     =					do_normalize if do_normalize is not None else self.do_normalize
         A_      :				Dict		     =					image_mean if image_mean is not None else self.image_mean
         A_      :				Dict		     =					image_std if image_std is not None else self.image_std
         A_      :				List[str]		     =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         A_      :				str		     =					make_list_of_images(_SCREAMING_SNAKE_CASE		)

         if not valid_images(_SCREAMING_SNAKE_CASE		):
           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:
           A_      :				Any		     =					[convert_to_rgb(_SCREAMING_SNAKE_CASE		) for image in images]

         # All transformations expect numpy arrays.
         A_      :				str		     =					[to_numpy_array(_SCREAMING_SNAKE_CASE		) for image in images]

         if do_resize:
           A_      :				int		     =					[self.resize(image=_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE				,       resample=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_center_crop:
           A_      :				int		     =					[self.center_crop(image=_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_rescale:
           A_      :				int		     =					[self.rescale(image=_SCREAMING_SNAKE_CASE				,       scale=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_normalize:
           A_      :				Union[str, Any]		     =					[self.normalize(image=_SCREAMING_SNAKE_CASE				,       mean=_SCREAMING_SNAKE_CASE				,       std=_SCREAMING_SNAKE_CASE		) for image in images]

         A_      :				Optional[Any]		     =					[to_channel_dimension_format(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) for image in images]

         A_      :				Union[str, Any]		     =					{'''pixel_values''': images}
         return BatchFeature(data=_SCREAMING_SNAKE_CASE				,       tensor_type=_SCREAMING_SNAKE_CASE		)








import copy
import tempfile
import unittest

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

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test






class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       @parameterized.expand([(None,), ('''foo.json''',)]		)
       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





         A_      :				Dict		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       bad_words_ids=[[1, 2, 3], [4, 5]]				,       )
         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(_SCREAMING_SNAKE_CASE				,       config_name=_SCREAMING_SNAKE_CASE		)
           A_      :				Tuple		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE				,       config_name=_SCREAMING_SNAKE_CASE		)

         # Checks parameters that were specified
         self.assertEqual(loaded_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(loaded_config.temperature				,       0.7		)
         self.assertEqual(loaded_config.length_penalty				,       1.0		)
         self.assertEqual(loaded_config.bad_words_ids				,       [[1, 2, 3], [4, 5]]		)

         # Checks parameters that were not specified (defaults)
         self.assertEqual(loaded_config.top_k				,       50		)
         self.assertEqual(loaded_config.max_length				,       20		)
         self.assertEqual(loaded_config.max_time				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				str		     =					AutoConfig.from_pretrained('''gpt2'''		)
         A_      :				int		     =					GenerationConfig.from_model_config(_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					GenerationConfig()

         # The generation config has loaded a few non-default parameters from the model config
         self.assertNotEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         # One of those parameters is eos_token_id -- check if it matches
         self.assertNotEqual(generation_config_from_model.eos_token_id				,       default_generation_config.eos_token_id		)
         self.assertEqual(generation_config_from_model.eos_token_id				,       model_config.eos_token_id		)



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




         '''simple docstring'''





         A_      :				List[Any]		     =					GenerationConfig()
         A_      :				Optional[Any]		     =					{
             '''max_new_tokens''': 1024,
             '''foo''': '''bar''',
         }
         A_      :				Dict		     =					copy.deepcopy(_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					generation_config.update(**_SCREAMING_SNAKE_CASE		)

         # update_kwargs was not modified (no side effects)
         self.assertEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         # update_kwargs was used to update the config on valid attributes
         self.assertEqual(generation_config.max_new_tokens				,       1024		)

         # `.update()` returns a dictionary of unused kwargs
         self.assertEqual(_SCREAMING_SNAKE_CASE				,       {'''foo''': '''bar'''}		)



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				List[str]		     =					GenerationConfig()
         A_      :				int		     =					'''bar'''

         with tempfile.TemporaryDirectory('''test-generation-config'''		) as tmp_dir:
           generation_config.save_pretrained(_SCREAMING_SNAKE_CASE		)

           A_      :				Optional[Any]		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
         # update_kwargs was used to update the config on valid attributes
         self.assertEqual(new_config.foo				,       '''bar'''		)

         A_      :				Optional[int]		     =					GenerationConfig.from_model_config(_SCREAMING_SNAKE_CASE		)
         assert not hasattr(_SCREAMING_SNAKE_CASE				,       '''foo'''		)  # no new kwargs should be initialized if from config







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




         '''simple docstring'''





         A_      :				str		     =					GenerationConfig()
         self.assertEqual(default_config.temperature				,       1.0		)
         self.assertEqual(default_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(default_config.num_beams				,       1		)

         A_      :				str		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       bad_words_ids=[[1, 2, 3], [4, 5]]				,       )
         self.assertEqual(config.temperature				,       0.7		)
         self.assertEqual(config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(config.num_beams				,       1		)

         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(_SCREAMING_SNAKE_CASE		)
           A_      :				Dict		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE				,       temperature=1.0		)

         self.assertEqual(loaded_config.temperature				,       1.0		)
         self.assertEqual(loaded_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(loaded_config.num_beams				,       1		)  # default value






@is_staging_test
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       @classmethod
       def        _snake_case       (      cls		)->Optional[int]:




         '''simple docstring'''





         A_      :				str		     =					TOKEN
         HfFolder.save_token(_SCREAMING_SNAKE_CASE		)



       @classmethod
       def        _snake_case       (      cls		)->List[Any]:




         '''simple docstring'''





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

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



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




         '''simple docstring'''





         A_      :				List[Any]		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       )
         config.push_to_hub('''test-generation-config'''				,       use_auth_token=self._token		)

         A_      :				Optional[int]		     =					GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)

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

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(
               _SCREAMING_SNAKE_CASE				,       repo_id='''test-generation-config'''				,       push_to_hub=_SCREAMING_SNAKE_CASE				,       use_auth_token=self._token		)

         A_      :				Optional[Any]		     =					GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)







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




         '''simple docstring'''





         A_      :				Tuple		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       )
         config.push_to_hub('''valid_org/test-generation-config-org'''				,       use_auth_token=self._token		)

         A_      :				Union[str, Any]		     =					GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)

        # Reset repo
         delete_repo(token=self._token				,       repo_id='''valid_org/test-generation-config-org'''		)

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

         A_      :				Dict		     =					GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)




