






"""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 PoolFormerImageProcessor







class      UpperCamelCase_			(unittest.TestCase	):





	def __init__( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					List[str]=7      ,      lowerCAmelCase_   :					str=3      ,      lowerCAmelCase_   :					Tuple=30      ,      lowerCAmelCase_   :					str=400      ,      lowerCAmelCase_   :					Dict=True      ,      lowerCAmelCase_   :					int=None      ,      lowerCAmelCase_   :					List[str]=0.9      ,      lowerCAmelCase_   :					Union[str, Any]=None      ,      lowerCAmelCase_   :					str=True      ,      lowerCAmelCase_   :					Dict=[0.5, 0.5, 0.5]      ,      lowerCAmelCase_   :					str=[0.5, 0.5, 0.5]      ,      )		->     int:
				UpperCAmelCase_	:					Dict     	=   size if size is not None else {"shortest_edge": 30}
				UpperCAmelCase_	:					Dict     	=   crop_size if crop_size is not None else {"height": 30, "width": 30}
				UpperCAmelCase_	:					int     	=   parent
				UpperCAmelCase_	:					Dict     	=   batch_size
				UpperCAmelCase_	:					List[Any]     	=   num_channels
				UpperCAmelCase_	:					Union[str, Any]     	=   min_resolution
				UpperCAmelCase_	:					List[str]     	=   max_resolution
				UpperCAmelCase_	:					Any     	=   do_resize_and_center_crop
				UpperCAmelCase_	:					str     	=   size
				UpperCAmelCase_	:					int     	=   crop_pct
				UpperCAmelCase_	:					List[str]     	=   crop_size
				UpperCAmelCase_	:					List[str]     	=   do_normalize
				UpperCAmelCase_	:					Dict     	=   image_mean
				UpperCAmelCase_	:					List[Any]     	=   image_std





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     str:
				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      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							PoolFormerImageProcessor if is_vision_available() else None





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Tuple:
				UpperCAmelCase_	:					Tuple     	=   PoolFormerImageProcessingTester(self		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Union[str, Any]:
				return self.image_processor_tester.prepare_image_processor_dict()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Optional[Any]:
				UpperCAmelCase_	:					Dict     	=   self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "do_resize_and_center_crop"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "size"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "crop_pct"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "do_normalize"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "image_mean"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "image_std"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Optional[int]:
				UpperCAmelCase_	:					Union[str, Any]     	=   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}		)

				UpperCAmelCase_	:					Optional[Any]     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Any:
				pass





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     List[str]:
				# Initialize image_processing
				UpperCAmelCase_	:					List[str]     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random PIL images
				UpperCAmelCase_	:					int     	=   prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase_		)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_      ,      Image.Image		)

				# Test not batched input
				UpperCAmelCase_	:					Dict     	=   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
				UpperCAmelCase_	:					Union[str, Any]     	=   image_processing(lowerCAmelCase_      ,      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 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     int:
				# Initialize image_processing
				UpperCAmelCase_	:					List[Any]     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				UpperCAmelCase_	:					str     	=   prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase_      ,      numpify=lowerCAmelCase_		)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_      ,      np.ndarray		)

				# Test not batched input
				UpperCAmelCase_	:					Union[str, 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
				UpperCAmelCase_	:					Union[str, Any]     	=   image_processing(lowerCAmelCase_      ,      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 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Union[str, Any]:
				# Initialize image_processing
				UpperCAmelCase_	:					List[str]     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				UpperCAmelCase_	:					Tuple     	=   prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase_      ,      torchify=lowerCAmelCase_		)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_      ,      torch.Tensor		)

				# Test not batched input
				UpperCAmelCase_	:					Optional[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
				UpperCAmelCase_	:					List[Any]     	=   image_processing(lowerCAmelCase_      ,      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"],
				    )      ,      )











"""simple docstring"""
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''': 512}

def 							snake_case (  A__		):
			UpperCAmelCase_	:					str     	=   set()
			UpperCAmelCase_	:					int     	=   word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)		)
						UpperCAmelCase_	:					Dict     	=   char

			UpperCAmelCase_	:					List[Any]     	=   set(A__		)
			return pairs







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							['''input_ids''', '''attention_mask''']





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

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





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				return len(self.encoder		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Dict:
				return dict(self.encoder      ,      **self.added_tokens_encoder		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					str		)		->     str:
				if token in self.cache:
							return self.cache[token]
				UpperCAmelCase_	:					Dict     	=   re.sub("([.,!?()])"      ,      R" \1"      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   re.sub("(')"      ,      R" \1 "      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   re.sub(R"\s{2,}"      ,      " "      ,      lowerCAmelCase_		)
				if "\n" in token:
							UpperCAmelCase_	:					Tuple     	=   token.replace("\n"      ,      " __newln__"		)

				UpperCAmelCase_	:					Tuple     	=   token.split(" "		)
				UpperCAmelCase_	:					int     	=   []
				for token in tokens:
							if not len(lowerCAmelCase_		):
										continue

							UpperCAmelCase_	:					Any     	=   token.lower()
							UpperCAmelCase_	:					List[str]     	=   tuple(lowerCAmelCase_		)
							UpperCAmelCase_	:					List[str]     	=   tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
							UpperCAmelCase_	:					List[Any]     	=   get_pairs(lowerCAmelCase_		)

							if not pairs:
										words.append(lowerCAmelCase_		)
										continue

							while True:
										UpperCAmelCase_	:					Union[str, Any]     	=   min(lowerCAmelCase_      ,      key=lambda lowerCAmelCase_		: self.bpe_ranks.get(lowerCAmelCase_      ,      float("inf"		)		)		)
										if bigram not in self.bpe_ranks:
													break
										UpperCAmelCase_	,      UpperCAmelCase_	:					Dict     	=   bigram
										UpperCAmelCase_	:					Dict     	=   []
										UpperCAmelCase_	:					Dict     	=   0

										while i < len(lowerCAmelCase_		):
													try:
																UpperCAmelCase_	:					int     	=   word.index(lowerCAmelCase_      ,      lowerCAmelCase_		)
																new_word.extend(word[i:j]		)
																UpperCAmelCase_	:					Any     	=   j
													except ValueError:
																new_word.extend(word[i:]		)
																break

													if word[i] == first and i < len(lowerCAmelCase_		) - 1 and word[i + 1] == second:
																new_word.append(first + second		)
																i += 2
													else:
																new_word.append(word[i]		)
																i += 1
										UpperCAmelCase_	:					Dict     	=   tuple(lowerCAmelCase_		)
										UpperCAmelCase_	:					Any     	=   new_word
										if len(lowerCAmelCase_		) == 1:
													break
										else:
													UpperCAmelCase_	:					Union[str, Any]     	=   get_pairs(lowerCAmelCase_		)
							UpperCAmelCase_	:					Optional[int]     	=   "@@ ".join(lowerCAmelCase_		)
							UpperCAmelCase_	:					List[str]     	=   word[:-4]

							UpperCAmelCase_	:					Optional[Any]     	=   word
							words.append(lowerCAmelCase_		)
				return " ".join(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					str		)		->     List[str]:
				UpperCAmelCase_	:					Union[str, Any]     	=   []

				UpperCAmelCase_	:					List[Any]     	=   re.findall(R"\S+\n?"      ,      lowerCAmelCase_		)

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					str		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   token.lower()
				return self.encoder.get(lowerCAmelCase_      ,      self.encoder.get(self.unk_token		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					int		)		->     str:
				return self.decoder.get(lowerCAmelCase_      ,      self.unk_token		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					List[str]		)		->     str:
				UpperCAmelCase_	:					List[str]     	=   " ".join(lowerCAmelCase_		).replace("@@ "      ,      ""		).strip()
				return out_string





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

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

				UpperCAmelCase_	:					Any     	=   0
				with open(lowerCAmelCase_      ,      "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 lowerCAmelCase_		: 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!"		)
													UpperCAmelCase_	:					List[Any]     	=   token_index
										writer.write(" ".join(lowerCAmelCase_		) + "\n"		)
										index += 1

				return vocab_file, merge_file



