



"""simple docstring"""





import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import LevitImageProcessor



class  _lowerCAmelCase     (					unittest.TestCase		):





		"""simple docstring"""






		def __init__(       self				,     _lowercase				,     _lowercase=7				,     _lowercase=3				,     _lowercase=1_8				,     _lowercase=3_0				,     _lowercase=4_0_0				,     _lowercase=True				,     _lowercase=None				,     _lowercase=True				,     _lowercase=None				,     _lowercase=True				,     _lowercase=[0.5, 0.5, 0.5]				,     _lowercase=[0.5, 0.5, 0.5]				,     )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			Any            =      size if size is not None else {"""shortest_edge""": 1_8}
								snake_case_ :			str            =      crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
								snake_case_ :			List[str]            =      parent
								snake_case_ :			Optional[Any]            =      batch_size
								snake_case_ :			List[Any]            =      num_channels
								snake_case_ :			Any            =      image_size
								snake_case_ :			int            =      min_resolution
								snake_case_ :			Tuple            =      max_resolution
								snake_case_ :			Tuple            =      do_resize
								snake_case_ :			int            =      size
								snake_case_ :			Union[str, Any]            =      do_center_crop
								snake_case_ :			Union[str, Any]            =      crop_size
								snake_case_ :			Optional[int]            =      do_normalize
								snake_case_ :			List[str]            =      image_mean
								snake_case_ :			Optional[Any]            =      image_std





		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "do_center_crop": self.do_center_crop,
								    "size": self.size,
								    "crop_size": self.crop_size,
								}




@require_torch
@require_vision
class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__	,					unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase      	=							LevitImageProcessor if is_vision_available() else None





		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								snake_case_ :			Dict            =      LevitImageProcessingTester(self )





		@property
		def  UpperCAmelCase__				(       self )			->    Optional[int]:







								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()





		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								snake_case_ :			Optional[Any]            =      self.image_processing_class(**self.image_processor_dict )
								self.assertTrue(hasattr(_lowercase				,     """image_mean""" ) )
								self.assertTrue(hasattr(_lowercase				,     """image_std""" ) )
								self.assertTrue(hasattr(_lowercase				,     """do_normalize""" ) )
								self.assertTrue(hasattr(_lowercase				,     """do_resize""" ) )
								self.assertTrue(hasattr(_lowercase				,     """do_center_crop""" ) )
								self.assertTrue(hasattr(_lowercase				,     """size""" ) )





		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      self.image_processing_class.from_dict(self.image_processor_dict )
								self.assertEqual(image_processor.size				,     {"""shortest_edge""": 1_8} )
								self.assertEqual(image_processor.crop_size				,     {"""height""": 1_8, """width""": 1_8} )

								snake_case_ :			int            =      self.image_processing_class.from_dict(self.image_processor_dict				,     size=4_2				,     crop_size=8_4 )
								self.assertEqual(image_processor.size				,     {"""shortest_edge""": 4_2} )
								self.assertEqual(image_processor.crop_size				,     {"""height""": 8_4, """width""": 8_4} )





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







								'''simple docstring'''

								pass





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







								'''simple docstring'''

								snake_case_ :			Union[str, Any]            =      self.image_processing_class(**self.image_processor_dict )
								# create random PIL images
								snake_case_ :			List[str]            =      prepare_image_inputs(self.image_processor_tester				,     equal_resolution=_lowercase )
								for image in image_inputs:
														self.assertIsInstance(_lowercase				,     Image.Image )

								# Test not batched input
								snake_case_ :			Any            =      image_processing(image_inputs[0]				,     return_tensors="""pt""" ).pixel_values
								self.assertEqual(
								    encoded_images.shape				,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,     )

								# Test batched
								snake_case_ :			str            =      image_processing(_lowercase				,     return_tensors="""pt""" ).pixel_values
								self.assertEqual(
								    encoded_images.shape				,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,     )





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







								'''simple docstring'''

								snake_case_ :			Any            =      self.image_processing_class(**self.image_processor_dict )
								# create random numpy tensors
								snake_case_ :			str            =      prepare_image_inputs(self.image_processor_tester				,     equal_resolution=_lowercase				,     numpify=_lowercase )
								for image in image_inputs:
														self.assertIsInstance(_lowercase				,     np.ndarray )

								# Test not batched input
								snake_case_ :			List[Any]            =      image_processing(image_inputs[0]				,     return_tensors="""pt""" ).pixel_values
								self.assertEqual(
								    encoded_images.shape				,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,     )

								# Test batched
								snake_case_ :			Dict            =      image_processing(_lowercase				,     return_tensors="""pt""" ).pixel_values
								self.assertEqual(
								    encoded_images.shape				,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,     )





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







								'''simple docstring'''

								snake_case_ :			int            =      self.image_processing_class(**self.image_processor_dict )
								# create random PyTorch tensors
								snake_case_ :			Any            =      prepare_image_inputs(self.image_processor_tester				,     equal_resolution=_lowercase				,     torchify=_lowercase )
								for image in image_inputs:
														self.assertIsInstance(_lowercase				,     torch.Tensor )

								# Test not batched input
								snake_case_ :			List[str]            =      image_processing(image_inputs[0]				,     return_tensors="""pt""" ).pixel_values
								self.assertEqual(
								    encoded_images.shape				,     (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,     )

								# Test batched
								snake_case_ :			int            =      image_processing(_lowercase				,     return_tensors="""pt""" ).pixel_values
								self.assertEqual(
								    encoded_images.shape				,     (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,     )



import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a         =   logging.get_logger(__name__)


__a         =   {
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

__a         =   {
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

__a         =   '</w>'
__a         =   '@@ '




def 							a		(				snake_case__:   Dict   ):


				'''simple docstring'''
				lowercase_	    =   set()
				lowercase_	    =   word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)   )
								lowercase_	    =   char
				return pairs


# Speech2Text2 has no max input length
__a         =   {'facebook/s2t-wav2vec2-large-en-de': 1_0_2_4}


class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :int       	=      VOCAB_FILES_NAMES
	a     :Optional[int]       	=      PRETRAINED_VOCAB_FILES_MAP
	a     :List[str]       	=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a     :Tuple       	=      ['input_ids', 'attention_mask']
	def __init__(			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     List[Any]="<s>"			,				SCREAMING_SNAKE_CASE_    :     Tuple="<pad>"			,				SCREAMING_SNAKE_CASE_    :     int="</s>"			,				SCREAMING_SNAKE_CASE_    :     int="<unk>"			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=False			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=None			,				**SCREAMING_SNAKE_CASE_    :     str			,				)			->	Optional[int]:
					super().__init__(
					    unk_token=SCREAMING_SNAKE_CASE_			,				bos_token=SCREAMING_SNAKE_CASE_			,				eos_token=SCREAMING_SNAKE_CASE_			,				pad_token=SCREAMING_SNAKE_CASE_			,				do_lower_case=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)

					lowercase_	    =   do_lower_case

					with open(SCREAMING_SNAKE_CASE_			,				encoding='''utf-8'''			) as vocab_handle:
									lowercase_	    =   json.load(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {v: k for k, v in self.encoder.items()}

					if merges_file is None:
									logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''			)

									lowercase_	    =   None
									lowercase_	    =   None
					else:
									with open(SCREAMING_SNAKE_CASE_			,				encoding='''utf-8'''			) as merges_handle:
													lowercase_	    =   merges_handle.read().split('''\n'''			)[:-1]

									lowercase_	    =   [tuple(merge.split()[:2]			) for merge in merges]
									lowercase_	    =   dict(zip(SCREAMING_SNAKE_CASE_			,				range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
									lowercase_	    =   {}
	@property
	def 		_lowercase    (			self    :     Any			)			->	int:
					return len(self.decoder			)
	def 		_lowercase    (			self    :     List[str]			)			->	Dict:
					return dict(self.encoder			,				**self.added_tokens_encoder			)
	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	int:
					lowercase_	    =   tuple(token[:-1]			) + (token[-1] + BPE_TOKEN_MERGES,)
					if token in self.cache:
									return self.cache[token]
					lowercase_	    =   get_pairs(SCREAMING_SNAKE_CASE_			)

					if not pairs:
									return token

					while True:
									lowercase_	    =   min(SCREAMING_SNAKE_CASE_			,				key=lambda SCREAMING_SNAKE_CASE_			: self.bpe_ranks.get(SCREAMING_SNAKE_CASE_			,				float('''inf'''			)			)			)
									if bigram not in self.bpe_ranks:
													break
									lowercase_    ,      lowercase_	    =   bigram
									lowercase_	    =   []
									lowercase_	    =   0
									while i < len(SCREAMING_SNAKE_CASE_			):
													try:
																	lowercase_	    =   word.index(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
													except ValueError:
																	new_word.extend(word[i:]			)
																	break
													else:
																	new_word.extend(word[i:j]			)
																	lowercase_	    =   j

													if word[i] == first and i < len(SCREAMING_SNAKE_CASE_			) - 1 and word[i + 1] == second:
																	new_word.append(first + second			)
																	i += 2
													else:
																	new_word.append(word[i]			)
																	i += 1
									lowercase_	    =   tuple(SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   new_word
									if len(SCREAMING_SNAKE_CASE_			) == 1:
													break
									else:
													lowercase_	    =   get_pairs(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   ''' '''.join(SCREAMING_SNAKE_CASE_			)
					if word == "\n  " + BPE_TOKEN_MERGES:
									lowercase_	    =   '''\n''' + BPE_TOKEN_MERGES

					if word.endswith(SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   word.replace(SCREAMING_SNAKE_CASE_			,				''''''			)

					lowercase_	    =   word.replace(''' '''			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   word
					return word
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Tuple:

					if self.bpe_ranks is None:
									raise ValueError(
									    '''This tokenizer was instantiated without a `merges.txt` file, so'''
									    ''' that it can only be used for decoding, not for encoding.'''
									    '''Make sure to provide `merges.txt` file at instantiation to enable '''
									    '''encoding.'''			)

					if self.do_lower_case:
									lowercase_	    =   text.lower()

					lowercase_	    =   text.split()

					lowercase_	    =   []
					for token in text:
									if token:
													split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE_			).split(''' '''			)			)			)

					return split_tokens
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     str			)			->	int:
					return self.encoder.get(SCREAMING_SNAKE_CASE_			,				self.encoder.get(self.unk_token			)			)
	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     int			)			->	str:
					lowercase_	    =   self.decoder.get(SCREAMING_SNAKE_CASE_			,				self.unk_token			)
					return result
	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     List[str]			)			->	str:
					lowercase_	    =   ''' '''.join(SCREAMING_SNAKE_CASE_			)

					# make sure @@ tokens are concatenated
					lowercase_	    =   ''''''.join(string.split(SCREAMING_SNAKE_CASE_			)			)

					return string




	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			)			->	Tuple[str]:
					if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
									return
					lowercase_	    =   os.path.join(
					    SCREAMING_SNAKE_CASE_			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)
					lowercase_	    =   os.path.join(
					    SCREAMING_SNAKE_CASE_			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']			)

					with open(SCREAMING_SNAKE_CASE_			,				'''w'''			,				encoding='''utf-8'''			) as f:
									f.write(json.dumps(self.encoder			,				indent=2			,				sort_keys=SCREAMING_SNAKE_CASE_			,				ensure_ascii=SCREAMING_SNAKE_CASE_			) + '''\n'''			)

					lowercase_	    =   0
					if self.bpe_ranks is None:
									return (vocab_file,)

					with open(SCREAMING_SNAKE_CASE_			,				'''w'''			,				encoding='''utf-8'''			) as writer:
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,				key=lambda SCREAMING_SNAKE_CASE_			: kv[1]			):
													if index != token_index:
																	logger.warning(
																	    f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
																	    ''' Please check that the tokenizer is not corrupted!'''			)
																	lowercase_	    =   token_index
													writer.write(''' '''.join(SCREAMING_SNAKE_CASE_			) + '''\n'''			)
													index += 1

					return (vocab_file, merges_file)




