

import json
import os
from typing import Optional, Tuple

import regex as re

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


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

SCREAMING_SNAKE_CASE_            = {
    'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
    'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}

SCREAMING_SNAKE_CASE_            = {
    'ctrl': 256,
}

SCREAMING_SNAKE_CASE_            = {
    'Pregnancy': 168629,
    'Christianity': 7675,
    'Explain': 106423,
    'Fitness': 63440,
    'Saving': 63163,
    'Ask': 27171,
    'Ass': 95985,
    'Joke': 163509,
    'Questions': 45622,
    'Thoughts': 49605,
    'Retail': 52342,
    'Feminism': 164338,
    'Writing': 11992,
    'Atheism': 192263,
    'Netflix': 48616,
    'Computing': 39639,
    'Opinion': 43213,
    'Alone': 44967,
    'Funny': 58917,
    'Gaming': 40358,
    'Human': 4088,
    'India': 1331,
    'Joker': 77138,
    'Diet': 36206,
    'Legal': 11859,
    'Norman': 4939,
    'Tip': 72689,
    'Weight': 52343,
    'Movies': 46273,
    'Running': 23425,
    'Science': 2090,
    'Horror': 37793,
    'Confession': 60572,
    'Finance': 12250,
    'Politics': 16360,
    'Scary': 191985,
    'Support': 12654,
    'Technologies': 32516,
    'Teenage': 66160,
    'Event': 32769,
    'Learned': 67460,
    'Notion': 182770,
    'Wikipedia': 37583,
    'Books': 6665,
    'Extract': 76050,
    'Confessions': 102701,
    'Conspiracy': 75932,
    'Links': 63674,
    'Narcissus': 150425,
    'Relationship': 54766,
    'Relationships': 134796,
    'Reviews': 41671,
    'News': 4256,
    'Translation': 26820,
    'multilingual': 128406,
}

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	)				->    int:
				_UpperCAmelCase    :						List[Any]							=      set()
				_UpperCAmelCase    :						int							=      word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)	)
								_UpperCAmelCase    :						Optional[Any]							=      char

				_UpperCAmelCase    :						Optional[int]							=      set(lowerCAmelCase	)
				return pairs





class 			a					(		UpperCAmelCase			):
						_lowercase        =     VOCAB_FILES_NAMES
						_lowercase        =     PRETRAINED_VOCAB_FILES_MAP
						_lowercase        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase        =     CONTROL_CODES


						def __init__(    self  ,   A_  ,   A_  ,   A_="<unk>"  ,   **A_		):







										'''simple docstring'''


										super().__init__(unk_token=A_  ,   **A_		)

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


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return len(self.encoder		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										if token in self.cache:
														return self.cache[token]
										_UpperCAmelCase    :						int							=      tuple(A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
										_UpperCAmelCase    :						Optional[Any]							=      get_pairs(A_		)

										if not pairs:
														return token

										while True:
														_UpperCAmelCase    :						Tuple							=      min(A_  ,   key=lambda A_		: self.bpe_ranks.get(A_  ,   float("inf"		)		)		)
														if bigram not in self.bpe_ranks:
																		break
														_UpperCAmelCase						,							_UpperCAmelCase    :						Union[str, Any]							=      bigram
														_UpperCAmelCase    :						int							=      []
														_UpperCAmelCase    :						Optional[Any]							=      0
														while i < len(A_		):
																		try:
																						_UpperCAmelCase    :						Optional[Any]							=      word.index(A_  ,   A_		)
																		except ValueError:
																						new_word.extend(word[i:]		)
																						break
																		else:
																						new_word.extend(word[i:j]		)
																						_UpperCAmelCase    :						Optional[Any]							=      j

																		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
														_UpperCAmelCase    :						str							=      tuple(A_		)
														_UpperCAmelCase    :						List[Any]							=      new_word
														if len(A_		) == 1:
																		break
														else:
																		_UpperCAmelCase    :						Dict							=      get_pairs(A_		)
										_UpperCAmelCase    :						Tuple							=      "@@ ".join(A_		)
										_UpperCAmelCase    :						List[Any]							=      word[:-4]
										_UpperCAmelCase    :						str							=      word
										return word


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      []

										_UpperCAmelCase    :						str							=      re.findall(R"\S+\n?"  ,   A_		)

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


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										return self.encoder.get(A_  ,   self.encoder.get(self.unk_token		)		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      " ".join(A_		).replace("@@ "  ,   ""		).strip()
										return out_string





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







										'''simple docstring'''


										if not os.path.isdir(A_		):
														logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
														return
										_UpperCAmelCase    :						Optional[Any]							=      os.path.join(
										    A_  ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
										_UpperCAmelCase    :						List[Any]							=      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"		)

										_UpperCAmelCase    :						Optional[int]							=      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 A_		: 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    :						Tuple							=      token_index
																		writer.write(" ".join(A_		) + "\n"		)
																		index += 1

										return vocab_file, merge_file

						# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
						#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
						#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
						#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
						#     return ''.join(tokens_generated_so_far)





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
					from transformers import TvltImageProcessor

if is_speech_available():
					from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class 			a					(		unittest.TestCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      "ZinengTang/tvlt-base"
										_UpperCAmelCase    :						int							=      tempfile.mkdtemp()


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										return TvltImageProcessor.from_pretrained(self.checkpoint  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										return TvltFeatureExtractor.from_pretrained(self.checkpoint  ,   **A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										shutil.rmtree(self.tmpdirname		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.get_image_processor()
										_UpperCAmelCase    :						Optional[int]							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Tuple							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)
										processor.save_pretrained(self.tmpdirname		)
										_UpperCAmelCase    :						str							=      TvltProcessor.from_pretrained(self.tmpdirname		)

										self.assertIsInstance(processor.feature_extractor  ,   A_		)
										self.assertIsInstance(processor.image_processor  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Tuple							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						List[str]							=      np.ones([12000]		)

										_UpperCAmelCase    :						int							=      feature_extractor(A_  ,   return_tensors="np"		)
										_UpperCAmelCase    :						int							=      processor(audio=A_  ,   return_tensors="np"		)

										for key in audio_dict.keys():
														self.assertAlmostEqual(audio_dict[key].sum()  ,   input_processor[key].sum()  ,   delta=1e-2		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Optional[Any]							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						Union[str, Any]							=      np.ones([3, 224, 224]		)

										_UpperCAmelCase    :						Tuple							=      image_processor(A_  ,   return_tensors="np"		)
										_UpperCAmelCase    :						List[str]							=      processor(images=A_  ,   return_tensors="np"		)

										for key in image_dict.keys():
														self.assertAlmostEqual(image_dict[key].sum()  ,   input_processor[key].sum()  ,   delta=1e-2		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.get_image_processor()
										_UpperCAmelCase    :						Any							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Dict							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						str							=      np.ones([12000]		)
										_UpperCAmelCase    :						Optional[Any]							=      np.ones([3, 224, 224]		)

										_UpperCAmelCase    :						List[Any]							=      processor(audio=A_  ,   images=A_		)

										self.assertListEqual(list(inputs.keys()		)  ,   ["audio_values", "audio_mask", "pixel_values", "pixel_mask"]		)

										# test if it raises when no input is passed
										with pytest.raises(A_		):
														processor()





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						str							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										self.assertListEqual(
										    processor.model_input_names  ,   image_processor.model_input_names + feature_extractor.model_input_names  ,   msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"  ,   )


