

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

import regex as re

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


lowerCAmelCase__	   = logging.get_logger(__name__)


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

lowerCAmelCase__	   = {
    """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"""
        )
    },
}

lowerCAmelCase__	   = {"""facebook/blenderbot_small-90M""": 5_1_2}



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]      )			->      Tuple:




				'''simple docstring'''





				A__        =			set()
				A__        =			word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)      )
								A__        =			char

				A__        =			set(UpperCamelCase__      )
				return pairs





class 					a__						(					lowercase_			):
			"""simple docstring"""







			__lowerCamelCase        =       VOCAB_FILES_NAMES
			__lowerCamelCase        =       PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase        =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase        =       ['''input_ids''', '''attention_mask''']


			def __init__(    self      ,      lowercase      ,      lowercase      ,      lowercase="__start__"      ,      lowercase="__end__"      ,      lowercase="__unk__"      ,      lowercase="__null__"      ,      **lowercase      ,      )  ->						Any:
							'''simple docstring'''






							super().__init__(unk_token=lowercase      ,      bos_token=lowercase      ,      eos_token=lowercase      ,      pad_token=lowercase      ,      **lowercase       )

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


			@property
			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							return len(self.encoder       )


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							return dict(self.encoder      ,      **self.added_tokens_encoder       )


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						Any:
							'''simple docstring'''






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

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

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

											if not pairs:
															words.append(lowercase       )
															continue

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

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

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

											A__        =			word
											words.append(lowercase       )
							return " ".join(lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			[]

							A__        =			re.findall(R"\S+\n?"      ,      lowercase       )

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


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			token.lower()
							return self.encoder.get(lowercase      ,      self.encoder.get(self.unk_token       )       )


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						Optional[Any]:
							'''simple docstring'''






							return self.decoder.get(lowercase      ,      self.unk_token       )


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						Dict:
							'''simple docstring'''






							A__        =			" ".join(lowercase       ).replace("@@ "      ,      ""       ).strip()
							return out_string


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None       )  ->						Tuple:
							'''simple docstring'''






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

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

							A__        =			0
							with open(lowercase      ,      "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!"       )
																			A__        =			token_index
															writer.write(" ".join(lowercase       ) + "\n"       )
															index += 1

							return vocab_file, merge_file






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 PoolFormerImageProcessor

class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase=7      ,      lowercase=3      ,      lowercase=30      ,      lowercase=400      ,      lowercase=True      ,      lowercase=None      ,      lowercase=0.9      ,      lowercase=None      ,      lowercase=True      ,      lowercase=[0.5, 0.5, 0.5]      ,      lowercase=[0.5, 0.5, 0.5]      ,      )  ->						str:
							'''simple docstring'''






							A__        =			size if size is not None else {"shortest_edge": 30}
							A__        =			crop_size if crop_size is not None else {"height": 30, "width": 30}
							A__        =			parent
							A__        =			batch_size
							A__        =			num_channels
							A__        =			min_resolution
							A__        =			max_resolution
							A__        =			do_resize_and_center_crop
							A__        =			size
							A__        =			crop_pct
							A__        =			crop_size
							A__        =			do_normalize
							A__        =			image_mean
							A__        =			image_std


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							return {
							    "size": self.size,
							    "do_resize_and_center_crop": self.do_resize_and_center_crop,
							    "crop_pct": self.crop_pct,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}

@require_torch
@require_vision
class 					a__						(					snake_case							,	unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       PoolFormerImageProcessor if is_vision_available() else None


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							A__        =			PoolFormerImageProcessingTester(self       )


			@property
			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(lowercase      ,      "do_resize_and_center_crop"       )       )
							self.assertTrue(hasattr(lowercase      ,      "size"       )       )
							self.assertTrue(hasattr(lowercase      ,      "crop_pct"       )       )
							self.assertTrue(hasattr(lowercase      ,      "do_normalize"       )       )
							self.assertTrue(hasattr(lowercase      ,      "image_mean"       )       )
							self.assertTrue(hasattr(lowercase      ,      "image_std"       )       )


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			self.image_processing_class.from_dict(self.image_processor_dict       )
							self.assertEqual(image_processor.size      ,      {"shortest_edge": 30}       )
							self.assertEqual(image_processor.crop_size      ,      {"height": 30, "width": 30}       )

							A__        =			self.image_processing_class.from_dict(self.image_processor_dict      ,      size=42      ,      crop_size=84       )
							self.assertEqual(image_processor.size      ,      {"shortest_edge": 42}       )
							self.assertEqual(image_processor.crop_size      ,      {"height": 84, "width": 84}       )


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






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

							# Test not batched input
							A__        =			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
							A__        =			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       )  ->						Tuple:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							A__        =			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
							A__        =			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
							A__        =			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       )  ->						Dict:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							A__        =			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
							A__        =			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
							A__        =			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"],
							    )      ,      )

