



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


lowercase_								=     logging.get_logger(__name__)


lowercase_								=     {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase_								=     {
    """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""",
    },
}

lowercase_								=     {
    """allenai/led-base-16384""": 1_6384,
}
class 				_snake_case			(					lowercase__):
    UpperCamelCase__				:					int     		=VOCAB_FILES_NAMES
    UpperCamelCase__				:					Any     		=PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase__				:					Dict     		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase__				:					List[Any]     		=LEDTokenizer
    UpperCamelCase__				:					Tuple     		=["""input_ids""", """attention_mask"""]




    def __init__(						self					:						Optional[Any],					__lowercase					:						Optional[Any]=None,					__lowercase					:						Dict=None,					__lowercase					:						Tuple=None,					__lowercase					:						Union[str, Any]="replace",					__lowercase					:						Tuple="<s>",					__lowercase					:						Optional[Any]="</s>",					__lowercase					:						Tuple="</s>",					__lowercase					:						List[str]="<s>",					__lowercase					:						Tuple="<unk>",					__lowercase					:						Dict="<pad>",					__lowercase					:						Dict="<mask>",					__lowercase					:						Any=False,					__lowercase					:						Any=True,					**__lowercase					:						List[Any],					):
       super().__init__(
           __lowercase,					__lowercase,					tokenizer_file=__lowercase,					errors=__lowercase,					bos_token=__lowercase,					eos_token=__lowercase,					sep_token=__lowercase,					cls_token=__lowercase,					unk_token=__lowercase,					pad_token=__lowercase,					mask_token=__lowercase,					add_prefix_space=__lowercase,					trim_offsets=__lowercase,					**__lowercase,					)

       lowercase__					  =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
       if pre_tok_state.get("add_prefix_space",					__lowercase						) != add_prefix_space:
          lowercase__					  =				getattr(__lowercase,					pre_tok_state.pop("type"						)						)
          lowercase__					  =				add_prefix_space
          lowercase__					  =				pre_tok_class(**__lowercase						)

       lowercase__					  =				add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       lowercase__					  =				"post_processor"
       lowercase__					  =				getattr(self.backend_tokenizer,					__lowercase,					__lowercase						)
       if tokenizer_component_instance:
          lowercase__					  =				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:
             lowercase__					  =				tuple(state["sep"]						)
          if "cls" in state:
             lowercase__					  =				tuple(state["cls"]						)

          lowercase__					  =				False

          if state.get("add_prefix_space",					__lowercase						) != add_prefix_space:
             lowercase__					  =				add_prefix_space
             lowercase__					  =				True

          if state.get("trim_offsets",					__lowercase						) != trim_offsets:
             lowercase__					  =				trim_offsets
             lowercase__					  =				True

          if changes_to_apply:
             lowercase__					  =				getattr(__lowercase,					state.pop("type"						)						)
             lowercase__					  =				component_class(**__lowercase						)
             setattr(self.backend_tokenizer,					__lowercase,					__lowercase						)




    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def 							A__      (						self					:						str						):
       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 							A__      (						self					:						Optional[int],					__lowercase					:						Dict						):
       lowercase__					  =				AddedToken(__lowercase,					lstrip=__lowercase,					rstrip=__lowercase						) if isinstance(__lowercase,					__lowercase						) else value
       lowercase__					  =				value




    def 							A__      (						self					:						Any,					*__lowercase					:						List[Any],					**__lowercase					:						Optional[Any]						):
       lowercase__					  =				kwargs.get("is_split_into_words",					__lowercase						)

       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(*__lowercase,					**__lowercase						)




    def 							A__      (						self					:						int,					*__lowercase					:						Union[str, Any],					**__lowercase					:						List[str]						):
       lowercase__					  =				kwargs.get("is_split_into_words",					__lowercase						)

       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(*__lowercase,					**__lowercase						)




    def 							A__      (						self					:						Optional[Any],					__lowercase					:						str,					__lowercase					:						Optional[str] = None						):
       lowercase__					  =				self._tokenizer.model.save(__lowercase,					name=__lowercase						)
       return tuple(__lowercase						)




    def 							A__      (						self					:						List[str],					__lowercase					:						int,					__lowercase					:						Optional[int]=None						):
       lowercase__					  =				[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 							A__      (						self					:						int,					__lowercase					:						List[int],					__lowercase					:						Optional[List[int]] = None						):
       lowercase__					  =				[self.sep_token_id]
       lowercase__					  =				[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 							A__      (						self					:						Union[str, Any],					__lowercase					:						Union[Dict[str, EncodedInput], BatchEncoding],					__lowercase					:						Optional[int] = None,					__lowercase					:						PaddingStrategy = PaddingStrategy.DO_NOT_PAD,					__lowercase					:						Optional[int] = None,					__lowercase					:						Optional[bool] = None,					):
       lowercase__					  =				super()._pad(
           encoded_inputs=__lowercase,					max_length=__lowercase,					padding_strategy=__lowercase,					pad_to_multiple_of=__lowercase,					return_attention_mask=__lowercase,					)

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

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

          if needs_to_be_padded:
             lowercase__					  =				len(__lowercase						) - 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`
                lowercase__					  =				(
                    encoded_inputs["global_attention_mask"] + [-1] * difference
                )
             elif self.padding_side == "left":
                lowercase__					  =				[-1] * difference + encoded_inputs[
                    "global_attention_mask"
                ]
             else:
                raise ValueError("Invalid padding strategy:" + str(self.padding_side						)						)

       return encoded_inputs









import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class 				_snake_case			(					unittest.TestCase):




				def __init__(						self					:						Dict,					__lowercase					:						int,					__lowercase					:						Union[str, Any]=7,					__lowercase					:						Union[str, Any]=3,					__lowercase					:						Any=18,					__lowercase					:						Union[str, Any]=30,					__lowercase					:						Any=400,					__lowercase					:						List[str]=True,					__lowercase					:						Dict=None,					__lowercase					:						List[str]=True,					__lowercase					:						int=False,					__lowercase					:						Union[str, Any]=True,					__lowercase					:						str=True,					__lowercase					:						Optional[int]=[0.5, 0.5, 0.5],					__lowercase					:						List[Any]=[0.5, 0.5, 0.5],					):
							lowercase__					  =				parent
							lowercase__					  =				batch_size
							lowercase__					  =				num_channels
							lowercase__					  =				image_size
							lowercase__					  =				min_resolution
							lowercase__					  =				max_resolution
							lowercase__					  =				do_resize
							lowercase__					  =				size if size is not None else {"height": 18, "width": 20}
							lowercase__					  =				do_thumbnail
							lowercase__					  =				do_align_axis
							lowercase__					  =				do_pad
							lowercase__					  =				do_normalize
							lowercase__					  =				image_mean
							lowercase__					  =				image_std




				def 							A__      (						self					:						Optional[Any]						):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_thumbnail": self.do_thumbnail,
							    "do_align_long_axis": self.do_align_axis,
							    "do_pad": self.do_pad,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}
@require_torch
@require_vision
class 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Optional[int]     		=DonutImageProcessor if is_vision_available() else None




				def 							A__      (						self					:						str						):
							lowercase__					  =				DonutImageProcessingTester(self						)




				@property
				def 							A__      (						self					:						List[str]						):
							return self.image_processor_tester.prepare_image_processor_dict()




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(__lowercase,					"do_resize"						)						)
							self.assertTrue(hasattr(__lowercase,					"size"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_thumbnail"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_align_long_axis"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_pad"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_normalize"						)						)
							self.assertTrue(hasattr(__lowercase,					"image_mean"						)						)
							self.assertTrue(hasattr(__lowercase,					"image_std"						)						)




				def 							A__      (						self					:						str						):
							lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size,					{"height": 18, "width": 20}						)

							lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict,					size=42						)
							self.assertEqual(image_processor.size,					{"height": 42, "width": 42}						)

							# Previous config had dimensions in (width, height) order
							lowercase__					  =				self.image_processing_class.from_dict(self.image_processor_dict,					size=(42, 84)						)
							self.assertEqual(image_processor.size,					{"height": 84, "width": 42}						)




				def 							A__      (						self					:						List[str]						):
							pass




				@is_flaky()
				def 							A__      (						self					:						Dict						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					Image.Image						)

							# Test not batched input
							lowercase__					  =				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.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)

							# Test batched
							lowercase__					  =				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.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)




				@is_flaky()
				def 							A__      (						self					:						Optional[Any]						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							lowercase__					  =				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
							lowercase__					  =				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.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)

							# Test batched
							lowercase__					  =				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.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)




				@is_flaky()
				def 							A__      (						self					:						Tuple						):
							# Initialize image_processing
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							lowercase__					  =				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
							lowercase__					  =				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.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)

							# Test batched
							lowercase__					  =				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.size["height"],
							        self.image_processor_tester.size["width"],
							    ),					)




