



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

import regex as re

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


lowercase_								=     logging.get_logger(__name__)


lowercase_								=     {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowercase_								=     {
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

lowercase_								=     {"""facebook/blenderbot_small-90M""": 512}



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
   lowercase__					  =				set()
   lowercase__					  =				word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)  )
      lowercase__					  =				char

   lowercase__					  =				set(_lowerCamelCase  )
   return pairs




class 				_snake_case			(					a__):
    UpperCamelCase__				:					Tuple     		=VOCAB_FILES_NAMES
    UpperCamelCase__				:					List[str]     		=PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase__				:					str     		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase__				:					List[Any]     		=["""input_ids""", """attention_mask"""]




    def __init__(						self					:						Tuple,					__lowercase					:						Dict,					__lowercase					:						int,					__lowercase					:						str="__start__",					__lowercase					:						List[str]="__end__",					__lowercase					:						List[str]="__unk__",					__lowercase					:						Union[str, Any]="__null__",					**__lowercase					:						Optional[int],					):
       super().__init__(unk_token=_A,					bos_token=_A,					eos_token=_A,					pad_token=_A,					**_A						)

       with open(_A,					encoding="utf-8"						) as vocab_handle:
          lowercase__					  =				json.load(_A						)
       lowercase__					  =				{v: k for k, v in self.encoder.items()}
       with open(_A,					encoding="utf-8"						) as merges_handle:
          lowercase__					  =				merges_handle.read().split("\n"						)[1:-1]
       lowercase__					  =				[tuple(merge.split()						) for merge in merges]
       lowercase__					  =				dict(zip(_A,					range(len(_A						)						)						)						)
       lowercase__					  =				{}




    @property
    def 							A__      (						self					:						Optional[Any]						):
       return len(self.encoder						)




    def 							A__      (						self					:						Any						):
       return dict(self.encoder,					**self.added_tokens_encoder						)




    def 							A__      (						self					:						str,					__lowercase					:						int						):

       if token in self.cache:
          return self.cache[token]
       lowercase__					  =				re.sub("([.,!?()])",					R" \1",					_A						)
       lowercase__					  =				re.sub("(\')",					R" \1 ",					_A						)
       lowercase__					  =				re.sub(R"\s{2,}",					" ",					_A						)
       if "\n" in token:
          lowercase__					  =				token.replace("\n",					" __newln__"						)

       lowercase__					  =				token.split(" "						)
       lowercase__					  =				[]
       for token in tokens:
          if not len(_A						):
             continue

          lowercase__					  =				token.lower()
          lowercase__					  =				tuple(_A						)
          lowercase__					  =				tuple(list(word[:-1]						) + [word[-1] + "</w>"]						)
          lowercase__					  =				get_pairs(_A						)

          if not pairs:
             words.append(_A						)
             continue

          while True:
             lowercase__					  =				min(_A,					key=lambda __lowercase						: self.bpe_ranks.get(_A,					float("inf"						)						)						)
             if bigram not in self.bpe_ranks:
                break
             lowercase__					  =				bigram
             lowercase__					  =				[]
             lowercase__					  =				0

             while i < len(_A						):
                try:
                   lowercase__					  =				word.index(_A,					_A						)
                   new_word.extend(word[i:j]						)
                   lowercase__					  =				j
                except ValueError:
                   new_word.extend(word[i:]						)
                   break

                if word[i] == first and i < len(_A						) - 1 and word[i + 1] == second:
                   new_word.append(first + second						)
                   i += 2
                else:
                   new_word.append(word[i]						)
                   i += 1
             lowercase__					  =				tuple(_A						)
             lowercase__					  =				new_word
             if len(_A						) == 1:
                break
             else:
                lowercase__					  =				get_pairs(_A						)
          lowercase__					  =				'@@ '.join(_A						)
          lowercase__					  =				word[:-4]

          lowercase__					  =				word
          words.append(_A						)
       return " ".join(_A						)




    def 							A__      (						self					:						Optional[int],					__lowercase					:						Union[str, Any]						):
       lowercase__					  =				[]

       lowercase__					  =				re.findall(R"\S+\n?",					_A						)

       for token in words:
          split_tokens.extend(list(self.bpe(_A						).split(" "						)						)						)
       return split_tokens




    def 							A__      (						self					:						List[str],					__lowercase					:						Dict						):
       lowercase__					  =				token.lower()
       return self.encoder.get(_A,					self.encoder.get(self.unk_token						)						)




    def 							A__      (						self					:						str,					__lowercase					:						int						):
       return self.decoder.get(_A,					self.unk_token						)




    def 							A__      (						self					:						int,					__lowercase					:						Optional[int]						):
       lowercase__					  =				' '.join(_A						).replace("@@ ",					""						).strip()
       return out_string




    def 							A__      (						self					:						List[Any],					__lowercase					:						Any,					__lowercase					:						Any = None						):

       if not os.path.isdir(_A						):
          logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
          return
       lowercase__					  =				os.path.join(
           _A,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
       lowercase__					  =				os.path.join(
           _A,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]						)

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

       lowercase__					  =				0
       with open(_A,					"w",					encoding="utf-8"						) as writer:
          writer.write("#version: 0.2\n"						)
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),					key=lambda __lowercase						: kv[1]						):
             if index != token_index:
                logger.warning(
                    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                    " Please check that the tokenizer is not corrupted!"						)
                lowercase__					  =				token_index
             writer.write(" ".join(_A						) + "\n"						)
             index += 1

       return vocab_file, merge_file









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"],
							    ),					)




