





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


if is_vision_available():
       import PIL


a__:      str									=		logging.get_logger(__name__)


def 	UpperCamelCase__(       UpperCamelCase__			:       Tuple ,     UpperCamelCase__			:       List[Any]  )->Tuple:
       A__											=						b.T
       A__											=						np.sum(np.square(__SCREAMING_SNAKE_CASE  ) ,     axis=1  )
       A__											=						np.sum(np.square(__SCREAMING_SNAKE_CASE  ) ,     axis=0  )
       A__											=						np.matmul(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE  )
       A__											=						aa[:, None] - 2 * ab + ba[None, :]
       return d


def 	UpperCamelCase__(       UpperCamelCase__			:       Union[str, Any] ,     UpperCamelCase__			:       Dict  )->List[str]:
       A__											=						x.reshape(-1 ,     3  )
       A__											=						squared_euclidean_distance(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE  )
       return np.argmin(__SCREAMING_SNAKE_CASE ,     axis=1  )




class     SCREAMING_SNAKE_CASE__ (						lowerCamelCase_      ):
 __SCREAMING_SNAKE_CASE							       =	['''pixel_values''']



 def __init__(							self,__lowerCamelCase = None,__lowerCamelCase = True,__lowerCamelCase = None,__lowerCamelCase = PILImageResampling.BILINEAR,__lowerCamelCase = True,__lowerCamelCase = True,**__lowerCamelCase,):
        super().__init__(**__SCREAMING_SNAKE_CASE )
        A__											=						size if size is not None else {'''height''': 256, '''width''': 256}
        A__											=						get_size_dict(__SCREAMING_SNAKE_CASE )
        A__											=						np.array(__SCREAMING_SNAKE_CASE ) if clusters is not None else None
        A__											=						do_resize
        A__											=						size
        A__											=						resample
        A__											=						do_normalize
        A__											=						do_color_quantize



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = PILImageResampling.BILINEAR,__lowerCamelCase = None,**__lowerCamelCase,):
        A__											=						get_size_dict(__SCREAMING_SNAKE_CASE )
        if "height" not in size or "width" not in size:
               raise ValueError(f"Size dictionary must contain both height and width keys. 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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,):
        A__											=						rescale(image=__SCREAMING_SNAKE_CASE,scale=1 / 127.5,data_format=__SCREAMING_SNAKE_CASE )
        A__											=						image - 1
        return image




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = ChannelDimension.FIRST,**__lowerCamelCase,):
        A__											=						do_resize if do_resize is not None else self.do_resize
        A__											=						size if size is not None else self.size
        A__											=						get_size_dict(__SCREAMING_SNAKE_CASE )
        A__											=						resample if resample is not None else self.resample
        A__											=						do_normalize if do_normalize is not None else self.do_normalize
        A__											=						do_color_quantize if do_color_quantize is not None else self.do_color_quantize
        A__											=						clusters if clusters is not None else self.clusters
        A__											=						np.array(__SCREAMING_SNAKE_CASE )

        A__											=						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 or resample is None:
               raise ValueError('''Size and resample must be specified if do_resize is True.''' )

        if do_color_quantize and clusters is None:
               raise ValueError('''Clusters must be specified if do_color_quantize is True.''' )

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

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

        if do_normalize:
               A__											=						[self.normalize(image=__SCREAMING_SNAKE_CASE ) for image in images]

        if do_color_quantize:
               A__											=						[to_channel_dimension_format(__SCREAMING_SNAKE_CASE,ChannelDimension.LAST ) for image in images]
               # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
               A__											=						np.array(__SCREAMING_SNAKE_CASE )
               A__											=						color_quantize(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ).reshape(images.shape[:-1] )

               # flatten to (batch_size, height*width)
               A__											=						images.shape[0]
               A__											=						images.reshape(__SCREAMING_SNAKE_CASE,-1 )

               # We need to convert back to a list of images to keep consistent behaviour across processors.
               A__											=						list(__SCREAMING_SNAKE_CASE )
        else:
               A__											=						[to_channel_dimension_format(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) for image in images]

        A__											=						{'''input_ids''': images}
        return BatchFeature(data=__SCREAMING_SNAKE_CASE,tensor_type=__SCREAMING_SNAKE_CASE )







'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
							from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase__   (  unittest.TestCase     ):






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''				,							return_dict=__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       AutoTokenizer.from_pretrained('''google/mt5-small'''		)

											lowercase_	:     int													=       tokenizer('''Hello there'''				,							return_tensors='''pt'''		).input_ids
											lowercase_	:     Union[str, Any]													=       tokenizer('''Hi I am'''				,							return_tensors='''pt'''		).input_ids

											lowercase_	:     Union[str, Any]													=       model(input_ids.to(__SCREAMING_SNAKE_CASE		)				,							labels=labels.to(__SCREAMING_SNAKE_CASE		)		).loss
											lowercase_	:     int													=       -(labels.shape[-1] * loss.item())

											lowercase_	:     Any													=       -84.9_127
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)



