
'''simple docstring'''



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

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 BridgeTowerImageProcessor




class     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						def __init__(   self       ,  _lowercase       ,  _lowercase = True       ,  _lowercase = None       ,  _lowercase = 32       ,  _lowercase = True       ,  _lowercase = 1 / 255       ,  _lowercase = True       ,  _lowercase = True       ,  _lowercase = [0.4814_5466, 0.457_8275, 0.4082_1073]       ,  _lowercase = [0.2686_2954, 0.2613_0258, 0.2757_7711]       ,  _lowercase = True       ,  _lowercase=7       ,  _lowercase=30       ,  _lowercase=400       ,  _lowercase=3       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					parent
												_lowerCAmelCase    		=					do_resize
												_lowerCAmelCase    		=					size if size is not None else {"""shortest_edge""": 288}
												_lowerCAmelCase    		=					size_divisor
												_lowerCAmelCase    		=					do_rescale
												_lowerCAmelCase    		=					rescale_factor
												_lowerCAmelCase    		=					do_normalize
												_lowerCAmelCase    		=					do_center_crop
												_lowerCAmelCase    		=					image_mean
												_lowerCAmelCase    		=					image_std
												_lowerCAmelCase    		=					do_pad
												_lowerCAmelCase    		=					batch_size
												_lowerCAmelCase    		=					num_channels
												_lowerCAmelCase    		=					min_resolution
												_lowerCAmelCase    		=					max_resolution




						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase=False ):

												"""simple docstring"""



												if not batched:
																		_lowerCAmelCase    		=					self.size["""shortest_edge"""]
																		_lowerCAmelCase    		=					image_inputs[0]
																		if isinstance(_lowercase       ,  Image.Image ):
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					image.size
																		else:
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					image.shape[1], image.shape[2]
																		_lowerCAmelCase    		=					size / min(_lowercase       ,  _lowercase )
																		if h < w:
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					size, scale * w
																		else:
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					scale * h, size

																		_lowerCAmelCase    		=					int((1_333 / 800) * size )
																		if max(_lowercase       ,  _lowercase ) > max_size:
																								_lowerCAmelCase    		=					max_size / max(_lowercase       ,  _lowercase )
																								_lowerCAmelCase    		=					newh * scale
																								_lowerCAmelCase    		=					neww * scale

																		_lowerCAmelCase		,       _lowerCAmelCase    		=					int(newh + 0.5 ), int(neww + 0.5 )
																		_lowerCAmelCase		,       _lowerCAmelCase    		=					(
																		    newh // self.size_divisor * self.size_divisor,
																		    neww // self.size_divisor * self.size_divisor,
																		)

												else:
																		_lowerCAmelCase    		=					[]
																		for image in image_inputs:
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					self.get_expected_values([image] )
																								expected_values.append((expected_height, expected_width) )
																		_lowerCAmelCase    		=					max(_lowercase       ,  key=lambda _lowercase : item[0] )[0]
																		_lowerCAmelCase    		=					max(_lowercase       ,  key=lambda _lowercase : item[1] )[1]

												return expected_height, expected_width






@require_torch
@require_vision
class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: Optional[int]										=  BridgeTowerImageProcessor if is_vision_available() else None




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					BridgeTowerImageProcessingTester(self )




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return self.image_processor_tester.prepare_image_processor_dict()




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					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       ,  """size""" ) )
												self.assertTrue(hasattr(_lowercase       ,  """size_divisor""" ) )




						def    _lowercase      (   self ):

												"""simple docstring"""



												pass




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.image_processing_class(**self.image_processor_dict )
												# create random PIL images
												_lowerCAmelCase    		=					prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_lowercase )
												for image in image_inputs:
																		self.assertIsInstance(_lowercase       ,  Image.Image )

												# Test not batched input
												_lowerCAmelCase    		=					image_processing(image_inputs[0]       ,  return_tensors="""pt""" ).pixel_values

												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.image_processor_tester.get_expected_values(_lowercase )
												self.assertEqual(
												    encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

												# Test batched
												_lowerCAmelCase    		=					image_processing(_lowercase       ,  return_tensors="""pt""" ).pixel_values

												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.image_processor_tester.get_expected_values(_lowercase       ,  batched=_lowercase )
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,  )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.image_processing_class(**self.image_processor_dict )
												# create random numpy tensors
												_lowerCAmelCase    		=					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
												_lowerCAmelCase    		=					image_processing(image_inputs[0]       ,  return_tensors="""pt""" ).pixel_values

												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.image_processor_tester.get_expected_values(_lowercase )
												self.assertEqual(
												    encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

												# Test batched
												_lowerCAmelCase    		=					image_processing(_lowercase       ,  return_tensors="""pt""" ).pixel_values

												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.image_processor_tester.get_expected_values(_lowercase       ,  batched=_lowercase )
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,  )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.image_processing_class(**self.image_processor_dict )
												# create random PyTorch tensors
												_lowerCAmelCase    		=					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
												_lowerCAmelCase    		=					image_processing(image_inputs[0]       ,  return_tensors="""pt""" ).pixel_values

												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.image_processor_tester.get_expected_values(_lowercase )
												self.assertEqual(
												    encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

												# Test batched
												_lowerCAmelCase    		=					image_processing(_lowercase       ,  return_tensors="""pt""" ).pixel_values

												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.image_processor_tester.get_expected_values(_lowercase       ,  batched=_lowercase )
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,  )






'''simple docstring'''



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

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


UpperCAmelCase_							:	Any	     =		logging.get_logger(__name__)


UpperCAmelCase_							:	str	     =		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_							:	str	     =		{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase_							:	Union[str, Any]	     =		{
    '''allenai/led-base-16384''': 1_6_3_8_4,
}





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   Dict										=      VOCAB_FILES_NAMES
       __lowercase	:   Dict										=      PRETRAINED_VOCAB_FILES_MAP
       __lowercase	:   Tuple										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowercase	:   Union[str, Any]										=      LEDTokenizer
       __lowercase	:   int										=      ['''input_ids''', '''attention_mask''']


       def __init__( self     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE="replace"     ,	__SCREAMING_SNAKE_CASE="<s>"     ,	__SCREAMING_SNAKE_CASE="</s>"     ,	__SCREAMING_SNAKE_CASE="</s>"     ,	__SCREAMING_SNAKE_CASE="<s>"     ,	__SCREAMING_SNAKE_CASE="<unk>"     ,	__SCREAMING_SNAKE_CASE="<pad>"     ,	__SCREAMING_SNAKE_CASE="<mask>"     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=True     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	List[Any]:



            '''simple docstring'''




            super().__init__(
                __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	tokenizer_file=__SCREAMING_SNAKE_CASE     ,	errors=__SCREAMING_SNAKE_CASE     ,	bos_token=__SCREAMING_SNAKE_CASE     ,	eos_token=__SCREAMING_SNAKE_CASE     ,	sep_token=__SCREAMING_SNAKE_CASE     ,	cls_token=__SCREAMING_SNAKE_CASE     ,	unk_token=__SCREAMING_SNAKE_CASE     ,	pad_token=__SCREAMING_SNAKE_CASE     ,	mask_token=__SCREAMING_SNAKE_CASE     ,	add_prefix_space=__SCREAMING_SNAKE_CASE     ,	trim_offsets=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE     ,	)

            __snake_case            =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
            if pre_tok_state.get('''add_prefix_space'''     ,	__SCREAMING_SNAKE_CASE	) != add_prefix_space:
                 __snake_case            =	getattr(__SCREAMING_SNAKE_CASE     ,	pre_tok_state.pop('''type'''	)	)
                 __snake_case            =	add_prefix_space
                 __snake_case            =	pre_tok_class(**__SCREAMING_SNAKE_CASE	)

            __snake_case            =	add_prefix_space

            # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
            __snake_case            =	'''post_processor'''
            __snake_case            =	getattr(self.backend_tokenizer     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
            if tokenizer_component_instance:
                 __snake_case            =	json.loads(tokenizer_component_instance.__getstate__()	)

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                      __snake_case            =	tuple(state['''sep''']	)
                 if "cls" in state:
                      __snake_case            =	tuple(state['''cls''']	)

                 __snake_case            =	False

                 if state.get('''add_prefix_space'''     ,	__SCREAMING_SNAKE_CASE	) != add_prefix_space:
                      __snake_case            =	add_prefix_space
                      __snake_case            =	True

                 if state.get('''trim_offsets'''     ,	__SCREAMING_SNAKE_CASE	) != trim_offsets:
                      __snake_case            =	trim_offsets
                      __snake_case            =	True

                 if changes_to_apply:
                      __snake_case            =	getattr(__SCREAMING_SNAKE_CASE     ,	state.pop('''type'''	)	)
                      __snake_case            =	component_class(**__SCREAMING_SNAKE_CASE	)
                      setattr(self.backend_tokenizer     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)


       @property
       # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            if self._mask_token is None:
                 if self.verbose:
                      logger.error('''Using mask_token, but it is not set yet.'''	)
                 return None
            return str(self._mask_token	)


       @mask_token.setter
       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	AddedToken(__SCREAMING_SNAKE_CASE     ,	lstrip=__SCREAMING_SNAKE_CASE     ,	rstrip=__SCREAMING_SNAKE_CASE	) if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) else value
            __snake_case            =	value


       def    lowerCAmelCase   ( self     ,	*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	BatchEncoding:



            '''simple docstring'''




            __snake_case            =	kwargs.get('''is_split_into_words'''     ,	__SCREAMING_SNAKE_CASE	)

            if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                     '''to use it with pretokenized inputs.'''	)

            return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	BatchEncoding:



            '''simple docstring'''




            __snake_case            =	kwargs.get('''is_split_into_words'''     ,	__SCREAMING_SNAKE_CASE	)

            if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                     '''to use it with pretokenized inputs.'''	)

            return super()._encode_plus(*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None	)   ->	Tuple[str]:



            '''simple docstring'''




            __snake_case            =	self._tokenizer.model.save(__SCREAMING_SNAKE_CASE     ,	name=__SCREAMING_SNAKE_CASE	)
            return tuple(__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                 return output

            return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None	)   ->	List[int]:



            '''simple docstring'''




            __snake_case            =	[self.sep_token_id]
            __snake_case            =	[self.cls_token_id]

            if token_ids_a is None:
                 return len(cls + token_ids_a + sep	) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = PaddingStrategy.DO_NOT_PAD     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	)   ->	dict:



            '''simple docstring'''




            __snake_case            =	super()._pad(
                encoded_inputs=__SCREAMING_SNAKE_CASE     ,	max_length=__SCREAMING_SNAKE_CASE     ,	padding_strategy=__SCREAMING_SNAKE_CASE     ,	pad_to_multiple_of=__SCREAMING_SNAKE_CASE     ,	return_attention_mask=__SCREAMING_SNAKE_CASE     ,	)

            # Load from model defaults
            if return_attention_mask is None:
                 __snake_case            =	'''attention_mask''' in self.model_input_names

            if return_attention_mask and "global_attention_mask" in encoded_inputs:
                 __snake_case            =	encoded_inputs[self.model_input_names[0]]
                 # `global_attention_mask` need to have the same length as other (sequential) inputs.
                 __snake_case            =	len(encoded_inputs['''global_attention_mask''']	) != len(__SCREAMING_SNAKE_CASE	)

                 if needs_to_be_padded:
                      __snake_case            =	len(__SCREAMING_SNAKE_CASE	) - len(encoded_inputs['''global_attention_mask''']	)

                      if self.padding_side == "right":
                           # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                           __snake_case            =	(
                               encoded_inputs['''global_attention_mask'''] + [-1] * difference
                           )
                      elif self.padding_side == "left":
                           __snake_case            =	[-1] * difference + encoded_inputs[
                               '''global_attention_mask'''
                           ]
                      else:
                           raise ValueError('''Invalid padding strategy:''' + str(self.padding_side	)	)

            return encoded_inputs






