import json
import os
import tempfile

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


if is_torch_available():
 import numpy as np
 import torch

if is_vision_available():
 from PIL import Image


def SCREAMING_SNAKE_CASE					(  lowercase_:						int		,    lowercase_:						Optional[int]=False		,    lowercase_:						List[str]=False		,    lowercase_:						Optional[Any]=False					):
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       _UpperCamelCase  :							Dict	    =	[]
       for i in range(image_processor_tester.batch_size					):
              if equal_resolution:
                     _UpperCamelCase  :							Union[str, Any]	    =	image_processor_tester.max_resolution
              else:
                     # To avoid getting image width/height 0
                     _UpperCamelCase  :							Optional[int]	    =	image_processor_tester.min_resolution
                     if getattr(lowercase_		,    'size_divisor'		,    lowercase_					):
                            # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                            _UpperCamelCase  :							Dict	    =	max(image_processor_tester.size_divisor		,    lowercase_					)
                     _UpperCamelCase    ,   _UpperCamelCase  :							str	    =	np.random.choice(np.arange(lowercase_		,    image_processor_tester.max_resolution					)		,    2					)
              image_inputs.append(
                  np.random.randint(255		,    size=(image_processor_tester.num_channels, width, height)		,    dtype=np.uinta					)					)

       if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              _UpperCamelCase  :							Optional[int]	    =	[Image.fromarray(np.moveaxis(lowercase_		,    0		,    -1					)					) for image in image_inputs]

       if torchify:
              _UpperCamelCase  :							Union[str, Any]	    =	[torch.from_numpy(lowercase_					) for image in image_inputs]

       return image_inputs


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[Any]		,    lowercase_:						Optional[Any]=10		,    lowercase_:						int=10		,    lowercase_:						Optional[int]=False		,    lowercase_:						str=False					):
       _UpperCamelCase  :							Dict	    =	[]
       for i in range(image_processor_tester.num_frames					):
              video.append(np.random.randint(255		,    size=(image_processor_tester.num_channels, width, height)		,    dtype=np.uinta					)					)

       if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              _UpperCamelCase  :							Any	    =	[Image.fromarray(np.moveaxis(lowercase_		,    0		,    -1					)					) for frame in video]

       if torchify:
              _UpperCamelCase  :							Any	    =	[torch.from_numpy(lowercase_					) for frame in video]

       return video


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						int=False		,    lowercase_:						List[Any]=False		,    lowercase_:						Union[str, Any]=False					):
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       _UpperCamelCase  :							Union[str, Any]	    =	[]
       for i in range(image_processor_tester.batch_size					):
              if equal_resolution:
                     _UpperCamelCase  :							Any	    =	image_processor_tester.max_resolution
              else:
                     _UpperCamelCase    ,   _UpperCamelCase  :							List[Any]	    =	np.random.choice(
                         np.arange(image_processor_tester.min_resolution		,    image_processor_tester.max_resolution					)		,    2					)
                     _UpperCamelCase  :							Optional[int]	    =	prepare_video(
                         image_processor_tester=lowercase_		,    width=lowercase_		,    height=lowercase_		,    numpify=lowercase_		,    torchify=lowercase_		,    )
              video_inputs.append(lowercase_					)

       return video_inputs





class  __lowerCAmelCase	:



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			None

 def        __snake_case       (      self							:		Dict	)			->				List[str]:
        _UpperCamelCase  :							List[Any]	    =	self.image_processing_class(**self.image_processor_dict	)
        _UpperCamelCase  :							str	    =	json.loads(image_processor.to_json_string()	)
        for key, value in self.image_processor_dict.items():
               self.assertEqual(obj[key]							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[Any]	)			->				Optional[Any]:
        _UpperCamelCase  :							Tuple	    =	self.image_processing_class(**self.image_processor_dict	)

        with tempfile.TemporaryDirectory() as tmpdirname:
               _UpperCamelCase  :							Tuple	    =	os.path.join(lowerCamelCase							,      'image_processor.json'	)
               image_processor_first.to_json_file(lowerCamelCase	)
               _UpperCamelCase  :							Dict	    =	self.image_processing_class.from_json_file(lowerCamelCase	)

        self.assertEqual(image_processor_second.to_dict()							,      image_processor_first.to_dict()	)

 def        __snake_case       (      self							:		str	)			->				Optional[int]:
        _UpperCamelCase  :							Dict	    =	self.image_processing_class(**self.image_processor_dict	)

        with tempfile.TemporaryDirectory() as tmpdirname:
               _UpperCamelCase  :							List[Any]	    =	image_processor_first.save_pretrained(lowerCamelCase	)[0]
               check_json_file_has_correct_format(lowerCamelCase	)
               _UpperCamelCase  :							Any	    =	self.image_processing_class.from_pretrained(lowerCamelCase	)

        self.assertEqual(image_processor_second.to_dict()							,      image_processor_first.to_dict()	)

 def        __snake_case       (      self							:		List[str]	)			->				Optional[Any]:
        _UpperCamelCase  :							Tuple	    =	self.image_processing_class()
        self.assertIsNotNone(lowerCamelCase	)

 @require_torch
 @require_vision
 def        __snake_case       (      self							:		Dict	)			->				Tuple:
        if self.test_cast_dtype is not None:
               # Initialize image_processor
               _UpperCamelCase  :							Optional[int]	    =	self.image_processing_class(**self.image_processor_dict	)

               # create random PyTorch tensors
               _UpperCamelCase  :							Any	    =	prepare_image_inputs(self.image_processor_tester							,      equal_resolution=lowerCamelCase							,      torchify=lowerCamelCase	)

               _UpperCamelCase  :							Any	    =	image_processor(lowerCamelCase							,      return_tensors='pt'	)
               # for layoutLM compatiblity
               self.assertEqual(encoding.pixel_values.device							,      torch.device('cpu'	)	)
               self.assertEqual(encoding.pixel_values.dtype							,      torch.floataa	)

               _UpperCamelCase  :							str	    =	image_processor(lowerCamelCase							,      return_tensors='pt'	).to(torch.floataa	)
               self.assertEqual(encoding.pixel_values.device							,      torch.device('cpu'	)	)
               self.assertEqual(encoding.pixel_values.dtype							,      torch.floataa	)

               _UpperCamelCase  :							Tuple	    =	image_processor(lowerCamelCase							,      return_tensors='pt'	).to('cpu'							,      torch.bfloataa	)
               self.assertEqual(encoding.pixel_values.device							,      torch.device('cpu'	)	)
               self.assertEqual(encoding.pixel_values.dtype							,      torch.bfloataa	)

               with self.assertRaises(lowerCamelCase	):
                      _UpperCamelCase  :							int	    =	image_processor(lowerCamelCase							,      return_tensors='pt'	).to(torch.bfloataa							,      'cpu'	)

               # Try with text + image feature
               _UpperCamelCase  :							Union[str, Any]	    =	image_processor(lowerCamelCase							,      return_tensors='pt'	)
               encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]]	)}	)
               _UpperCamelCase  :							Any	    =	encoding.to(torch.floataa	)

               self.assertEqual(encoding.pixel_values.device							,      torch.device('cpu'	)	)
               self.assertEqual(encoding.pixel_values.dtype							,      torch.floataa	)
               self.assertEqual(encoding.input_ids.dtype							,      torch.long	)




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


_SCREAMING_SNAKE_CASE:    List[Any]     						=							logging.get_logger(__name__)

class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		A_    			=   ['''pixel_values''']



		def __init__(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Optional[Dict[str, int]] = None	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PILImageResampling.BILINEAR	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Union[int, float] = 1 / 255	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       **__SCREAMING_SNAKE_CASE     :					str	,       ):






					"""simple docstring"""
					super().__init__(**__SCREAMING_SNAKE_CASE )
					_snake_case														=   size if size is not None else {"height": 224, "width": 224}
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					_snake_case														=   do_resize
					_snake_case														=   do_rescale
					_snake_case														=   do_normalize
					_snake_case														=   size
					_snake_case														=   resample
					_snake_case														=   rescale_factor
					_snake_case														=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					_snake_case														=   image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 					_a					(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int]	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PILImageResampling.BILINEAR	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Tuple	,       ):






					"""simple docstring"""
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					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(
					    __SCREAMING_SNAKE_CASE	,       size=(size["height"], size["width"])	,       resample=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					float	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					List[str] ):






					"""simple docstring"""
					return rescale(__SCREAMING_SNAKE_CASE	,       scale=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Union[float, List[float]]	,       __SCREAMING_SNAKE_CASE     :					Union[float, List[float]]	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Optional[Any]	,       ):






					"""simple docstring"""
					return normalize(__SCREAMING_SNAKE_CASE	,       mean=__SCREAMING_SNAKE_CASE	,       std=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )







		def 					_a					(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					ImageInput	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int] = None	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = None	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[float] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, TensorType]] = None	,       __SCREAMING_SNAKE_CASE     :					Union[str, ChannelDimension] = ChannelDimension.FIRST	,       **__SCREAMING_SNAKE_CASE     :					List[Any]	,       ):






					"""simple docstring"""
					_snake_case														=   do_resize if do_resize is not None else self.do_resize
					_snake_case														=   do_rescale if do_rescale is not None else self.do_rescale
					_snake_case														=   do_normalize if do_normalize is not None else self.do_normalize
					_snake_case														=   resample if resample is not None else self.resample
					_snake_case														=   rescale_factor if rescale_factor is not None else self.rescale_factor
					_snake_case														=   image_mean if image_mean is not None else self.image_mean
					_snake_case														=   image_std if image_std is not None else self.image_std

					_snake_case														=   size if size is not None else self.size
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )

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

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

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

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

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

					_snake_case														=   [to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) for image in images]

					_snake_case														=   {"pixel_values": images}
					return BatchFeature(data=__SCREAMING_SNAKE_CASE	,       tensor_type=__SCREAMING_SNAKE_CASE )

