



'''simple docstring'''





import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class  lowerCamelCase__    (							__A   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=GPTaTokenizer
		snake_case_			=GPTaTokenizerFast
		snake_case_			=True
		snake_case_			={"""add_prefix_space""": True}
		snake_case_			=False



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						lowerCAmelCase__  :       Tuple				=  [
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    '\u0120',
						    '\u0120l',
						    '\u0120n',
						    '\u0120lo',
						    '\u0120low',
						    'er',
						    '\u0120lowest',
						    '\u0120newer',
						    '\u0120wider',
						    '<unk>',
						    '<|endoftext|>',
						]
						lowerCAmelCase__  :       Dict				=  dict(zip(__lowerCamelCase	,range(len(__lowerCamelCase				)				)				)				)
						lowerCAmelCase__  :       Optional[int]				=  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
						lowerCAmelCase__  :       List[str]				=  {'unk_token': '<unk>'}

						lowerCAmelCase__  :       Union[str, Any]				=  os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['''vocab_file''']				)
						lowerCAmelCase__  :       str				=  os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['''merges_file''']				)
						with open(self.vocab_file	,'''w'''	,encoding='''utf-8'''				) as fp:
										fp.write(json.dumps(__lowerCamelCase				) + '''\n'''				)
						with open(self.merges_file	,'''w'''	,encoding='''utf-8'''				) as fp:
										fp.write('''\n'''.join(__lowerCamelCase				)				)



		def 	lowerCAmelCase__   (self	,**__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						kwargs.update(self.special_tokens_map				)
						return GPTaTokenizer.from_pretrained(self.tmpdirname	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,**__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						kwargs.update(self.special_tokens_map				)
						return GPTaTokenizerFast.from_pretrained(self.tmpdirname	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  'lower newer'
						lowerCAmelCase__  :       Optional[int]				=  'lower newer'
						return input_text, output_text



		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  GPTaTokenizer(self.vocab_file	,self.merges_file	,**self.special_tokens_map				)
						lowerCAmelCase__  :       Any				=  'lower newer'
						lowerCAmelCase__  :       Optional[int]				=  ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
						lowerCAmelCase__  :       Optional[int]				=  tokenizer.tokenize(__lowerCamelCase	,add_prefix_space=__lowerCamelCase				)
						self.assertListEqual(__lowerCamelCase	,__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  tokens + [tokenizer.unk_token]
						lowerCAmelCase__  :       Any				=  [14, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase				)	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						if not self.test_rust_tokenizer:
										return

						lowerCAmelCase__  :       List[Any]				=  self.get_tokenizer()
						lowerCAmelCase__  :       Dict				=  self.get_rust_tokenizer(add_prefix_space=__lowerCamelCase				)

						lowerCAmelCase__  :       Dict				=  'lower newer'

						# Testing tokenization
						lowerCAmelCase__  :       int				=  tokenizer.tokenize(__lowerCamelCase	,add_prefix_space=__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  rust_tokenizer.tokenize(__lowerCamelCase				)
						self.assertListEqual(__lowerCamelCase	,__lowerCamelCase				)

						# Testing conversion to ids without special tokens
						lowerCAmelCase__  :       Optional[int]				=  tokenizer.encode(__lowerCamelCase	,add_special_tokens=__lowerCamelCase	,add_prefix_space=__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  rust_tokenizer.encode(__lowerCamelCase	,add_special_tokens=__lowerCamelCase				)
						self.assertListEqual(__lowerCamelCase	,__lowerCamelCase				)

						# Testing conversion to ids with special tokens
						lowerCAmelCase__  :       Any				=  self.get_rust_tokenizer(add_prefix_space=__lowerCamelCase				)
						lowerCAmelCase__  :       Any				=  tokenizer.encode(__lowerCamelCase	,add_prefix_space=__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  rust_tokenizer.encode(__lowerCamelCase				)
						self.assertListEqual(__lowerCamelCase	,__lowerCamelCase				)

						# Testing the unknown token
						lowerCAmelCase__  :       Union[str, Any]				=  tokens + [rust_tokenizer.unk_token]
						lowerCAmelCase__  :       int				=  [14, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowerCamelCase				)	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,*__lowerCamelCase	,**__lowerCamelCase				)  ->    List[Any]:

						"""simple docstring"""






						pass



		def 	lowerCAmelCase__   (self	,__lowerCamelCase=15				)  ->    Tuple:

						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
														lowerCAmelCase__  :       Any				=  self.rust_tokenizer_class.from_pretrained(__lowerCamelCase	,**__lowerCamelCase				)

														# Simple input
														lowerCAmelCase__  :       List[str]				=  'This is a simple input'
														lowerCAmelCase__  :       List[str]				=  ['This is a simple input 1', 'This is a simple input 2']
														lowerCAmelCase__  :       Optional[int]				=  ('This is a simple input', 'This is a pair')
														lowerCAmelCase__  :       List[Any]				=  [
														    ('This is a simple input 1', 'This is a simple input 2'),
														    ('This is a simple pair 1', 'This is a simple pair 2'),
														]

														# Simple input tests
														self.assertRaises(__lowerCamelCase	,tokenizer_r.encode	,__lowerCamelCase	,max_length=__lowerCamelCase	,padding='''max_length'''				)

														# Simple input
														self.assertRaises(__lowerCamelCase	,tokenizer_r.encode_plus	,__lowerCamelCase	,max_length=__lowerCamelCase	,padding='''max_length'''				)

														# Simple input
														self.assertRaises(
														    __lowerCamelCase	,tokenizer_r.batch_encode_plus	,__lowerCamelCase	,max_length=__lowerCamelCase	,padding='''max_length'''	,)

														# Pair input
														self.assertRaises(__lowerCamelCase	,tokenizer_r.encode	,__lowerCamelCase	,max_length=__lowerCamelCase	,padding='''max_length'''				)

														# Pair input
														self.assertRaises(__lowerCamelCase	,tokenizer_r.encode_plus	,__lowerCamelCase	,max_length=__lowerCamelCase	,padding='''max_length'''				)

														# Pair input
														self.assertRaises(
														    __lowerCamelCase	,tokenizer_r.batch_encode_plus	,__lowerCamelCase	,max_length=__lowerCamelCase	,padding='''max_length'''	,)



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  GPTaTokenizer.from_pretrained(self.tmpdirname	,pad_token='''<pad>'''				)

						# Simple input
						lowerCAmelCase__  :       Any				=  'This is a simple input'
						lowerCAmelCase__  :       Dict				=  ['This is a simple input looooooooong', 'This is a simple input']
						lowerCAmelCase__  :       Optional[Any]				=  ('This is a simple input', 'This is a pair')
						lowerCAmelCase__  :       Optional[Any]				=  [
						    ('This is a simple input loooooong', 'This is a simple input'),
						    ('This is a simple pair loooooong', 'This is a simple pair'),
						]

						lowerCAmelCase__  :       Any				=  tokenizer.pad_token_id

						lowerCAmelCase__  :       Optional[Any]				=  tokenizer(__lowerCamelCase	,padding='''max_length'''	,max_length=30	,return_tensors='''np'''				)
						lowerCAmelCase__  :       List[str]				=  tokenizer(__lowerCamelCase	,padding=__lowerCamelCase	,truncate=__lowerCamelCase	,return_tensors='''np'''				)
						lowerCAmelCase__  :       Optional[int]				=  tokenizer(*__lowerCamelCase	,padding='''max_length'''	,max_length=60	,return_tensors='''np'''				)
						lowerCAmelCase__  :       List[Any]				=  tokenizer(__lowerCamelCase	,padding=__lowerCamelCase	,truncate=__lowerCamelCase	,return_tensors='''np'''				)

						# s
						# test single string max_length padding
						self.assertEqual(out_s['''input_ids'''].shape[-1]	,30				)
						self.assertTrue(pad_token_id in out_s['''input_ids''']				)
						self.assertTrue(0 in out_s['''attention_mask''']				)

						# s2
						# test automatic padding
						self.assertEqual(out_sa['''input_ids'''].shape[-1]	,33				)
						# long slice doesn't have padding
						self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]				)
						self.assertFalse(0 in out_sa['''attention_mask'''][0]				)
						# short slice does have padding
						self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]				)
						self.assertTrue(0 in out_sa['''attention_mask'''][1]				)

						# p
						# test single pair max_length padding
						self.assertEqual(out_p['''input_ids'''].shape[-1]	,60				)
						self.assertTrue(pad_token_id in out_p['''input_ids''']				)
						self.assertTrue(0 in out_p['''attention_mask''']				)

						# p2
						# test automatic padding pair
						self.assertEqual(out_pa['''input_ids'''].shape[-1]	,52				)
						# long slice pair doesn't have padding
						self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]				)
						self.assertFalse(0 in out_pa['''attention_mask'''][0]				)
						# short slice pair does have padding
						self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]				)
						self.assertTrue(0 in out_pa['''attention_mask'''][1]				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  '$$$'
						lowerCAmelCase__  :       Optional[int]				=  GPTaTokenizer.from_pretrained(self.tmpdirname	,bos_token=__lowerCamelCase	,add_bos_token=__lowerCamelCase				)

						lowerCAmelCase__  :       Dict				=  'This is a simple input'
						lowerCAmelCase__  :       List[Any]				=  ['This is a simple input 1', 'This is a simple input 2']

						lowerCAmelCase__  :       List[Any]				=  tokenizer.bos_token_id

						lowerCAmelCase__  :       List[Any]				=  tokenizer(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  tokenizer(__lowerCamelCase				)

						self.assertEqual(out_s.input_ids[0]	,__lowerCamelCase				)
						self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids				)				)

						lowerCAmelCase__  :       Dict				=  tokenizer.decode(out_s.input_ids				)
						lowerCAmelCase__  :       List[Any]				=  tokenizer.batch_decode(out_sa.input_ids				)

						self.assertEqual(decode_s.split()[0]	,__lowerCamelCase				)
						self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa				)				)



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

						"""simple docstring"""






						pass



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

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  [self.get_tokenizer(do_lower_case=__lowerCamelCase	,add_bos_token=__lowerCamelCase				)]
						for tokenizer in tokenizers:
										with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
														lowerCAmelCase__  :       Dict				=  'Encode this.'
														lowerCAmelCase__  :       Any				=  'This one too please.'
														lowerCAmelCase__  :       str				=  tokenizer.encode(__lowerCamelCase	,add_special_tokens=__lowerCamelCase				)
														encoded_sequence += tokenizer.encode(__lowerCamelCase	,add_special_tokens=__lowerCamelCase				)
														lowerCAmelCase__  :       Optional[int]				=  tokenizer.encode_plus(
														    __lowerCamelCase	,__lowerCamelCase	,add_special_tokens=__lowerCamelCase	,return_special_tokens_mask=__lowerCamelCase	,)
														lowerCAmelCase__  :       Union[str, Any]				=  encoded_sequence_dict['input_ids']
														lowerCAmelCase__  :       Any				=  encoded_sequence_dict['special_tokens_mask']
														self.assertEqual(len(__lowerCamelCase				)	,len(__lowerCamelCase				)				)

														lowerCAmelCase__  :       Optional[int]				=  [
														    (x if not special_tokens_mask[i] else None) for i, x in enumerate(__lowerCamelCase				)
														]
														lowerCAmelCase__  :       Optional[int]				=  [x for x in filtered_sequence if x is not None]
														self.assertEqual(__lowerCamelCase	,__lowerCamelCase				)



@require_tokenizers
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  AutoTokenizer.from_pretrained('''facebook/opt-350m'''	,from_slow=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  'A photo of a cat'

						lowerCAmelCase__  :       Optional[int]				=  tokenizer.encode(
						    __lowerCamelCase	,)
						self.assertEqual(__lowerCamelCase	,[2, 2_50, 13_45, 9, 10, 47_58]				)
						tokenizer.save_pretrained('''test_opt'''				)

						lowerCAmelCase__  :       List[str]				=  AutoTokenizer.from_pretrained('''./test_opt'''				)
						lowerCAmelCase__  :       Union[str, Any]				=  tokenizer.encode(
						    __lowerCamelCase	,)
						self.assertEqual(__lowerCamelCase	,[2, 2_50, 13_45, 9, 10, 47_58]				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  AutoTokenizer.from_pretrained('''facebook/opt-350m'''	,use_slow=__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  'A photo of a cat'

						lowerCAmelCase__  :       Dict				=  tokenizer.encode(
						    __lowerCamelCase	,)
						# Same as above
						self.assertEqual(__lowerCamelCase	,[2, 2_50, 13_45, 9, 10, 47_58]				)



		@unittest.skip('''This test is failing because of a bug in the fast tokenizer'''				)
		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  AutoTokenizer.from_pretrained('''facebook/opt-350m'''	,from_slow=__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[Any]				=  'bos'
						lowerCAmelCase__  :       List[Any]				=  tokenizer.get_vocab()['bos']

						lowerCAmelCase__  :       Any				=  'A photo of a cat'
						lowerCAmelCase__  :       int				=  tokenizer.encode(
						    __lowerCamelCase	,)
						# We changed the bos token
						self.assertEqual(__lowerCamelCase	,[3_19_57, 2_50, 13_45, 9, 10, 47_58]				)
						tokenizer.save_pretrained('''./tok'''				)
						lowerCAmelCase__  :       Union[str, Any]				=  AutoTokenizer.from_pretrained('''./tok'''				)
						self.assertTrue(tokenizer.is_fast				)
						lowerCAmelCase__  :       Optional[int]				=  tokenizer.encode(
						    __lowerCamelCase	,)
						self.assertEqual(__lowerCamelCase	,[3_19_57, 2_50, 13_45, 9, 10, 47_58]				)






from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    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, is_torch_available, is_torch_tensor, logging


if is_torch_available():
							import torch


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



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=["""pixel_values"""]



		def __init__(self	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = PILImageResampling.BILINEAR	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = True	,__lowerCamelCase = 1 / 2_55	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    None:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  size if size is not None else {'''shortest_edge''': 2_56}
						lowerCAmelCase__  :       Any				=  get_size_dict(__lowerCamelCase	,default_to_square=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
						lowerCAmelCase__  :       Any				=  get_size_dict(__lowerCamelCase	,param_name='''crop_size'''				)
						lowerCAmelCase__  :       Dict				=  do_resize
						lowerCAmelCase__  :       str				=  size
						lowerCAmelCase__  :       Tuple				=  resample
						lowerCAmelCase__  :       List[Any]				=  do_center_crop
						lowerCAmelCase__  :       Dict				=  crop_size
						lowerCAmelCase__  :       Optional[int]				=  do_rescale
						lowerCAmelCase__  :       List[str]				=  rescale_factor
						lowerCAmelCase__  :       List[Any]				=  do_normalize
						lowerCAmelCase__  :       Optional[Any]				=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCAmelCase__  :       str				=  image_std if image_std is not None else IMAGENET_STANDARD_STD



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

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  get_size_dict(__lowerCamelCase	,default_to_square=__lowerCamelCase				)
						if "shortest_edge" not in size:
										raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
						lowerCAmelCase__  :       str				=  get_resize_output_image_size(__lowerCamelCase	,size=size['''shortest_edge''']	,default_to_square=__lowerCamelCase				)
						return resize(__lowerCamelCase	,size=__lowerCamelCase	,resample=__lowerCamelCase	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  get_size_dict(__lowerCamelCase				)
						if "height" not in size or "width" not in size:
										raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""				)
						return center_crop(__lowerCamelCase	,size=(size['''height'''], size['''width'''])	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase				)  ->    np.ndarray:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    np.ndarray:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = ChannelDimension.FIRST	,**__lowerCamelCase	,)  ->    Optional[Any]:

						"""simple docstring"""






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

						lowerCAmelCase__  :       Any				=  make_list_of_images(__lowerCamelCase				)

						if not valid_images(__lowerCamelCase				):
										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.'''				)

						# All transformations expect numpy arrays.
						lowerCAmelCase__  :       Union[str, Any]				=  [to_numpy_array(__lowerCamelCase				) for image in images]

						if do_resize:
										lowerCAmelCase__  :       str				=  [self.resize(image=__lowerCamelCase	,size=__lowerCamelCase	,resample=__lowerCamelCase				) for image in images]

						if do_center_crop:
										lowerCAmelCase__  :       List[Any]				=  [self.center_crop(image=__lowerCamelCase	,size=__lowerCamelCase				) for image in images]

						if do_rescale:
										lowerCAmelCase__  :       Any				=  [self.rescale(image=__lowerCamelCase	,scale=__lowerCamelCase				) for image in images]

						if do_normalize:
										lowerCAmelCase__  :       Tuple				=  [self.normalize(image=__lowerCamelCase	,mean=__lowerCamelCase	,std=__lowerCamelCase				) for image in images]

						lowerCAmelCase__  :       int				=  [to_channel_dimension_format(__lowerCamelCase	,__lowerCamelCase				) for image in images]

						lowerCAmelCase__  :       int				=  {'''pixel_values''': images}
						return BatchFeature(data=__lowerCamelCase	,tensor_type=__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
										if len(__lowerCamelCase				) != len(__lowerCamelCase				):
														raise ValueError(
														    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''				)

										if is_torch_tensor(__lowerCamelCase				):
														lowerCAmelCase__  :       str				=  target_sizes.numpy()

										lowerCAmelCase__  :       Optional[Any]				=  []

										for idx in range(len(__lowerCamelCase				)				):
														lowerCAmelCase__  :       Optional[int]				=  torch.nn.functional.interpolate(
														    logits[idx].unsqueeze(dim=0				)	,size=target_sizes[idx]	,mode='''bilinear'''	,align_corners=__lowerCamelCase				)
														lowerCAmelCase__  :       Optional[Any]				=  resized_logits[0].argmax(dim=0				)
														semantic_segmentation.append(__lowerCamelCase				)
						else:
										lowerCAmelCase__  :       Optional[Any]				=  logits.argmax(dim=1				)
										lowerCAmelCase__  :       int				=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

						return semantic_segmentation

