
import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{'''vocab_file''': '''vocab.txt'''}

__magic_name__  							=	{
    '''vocab_file''': {
        '''facebook/esm2_t6_8M_UR50D''': '''https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt''',
        '''facebook/esm2_t12_35M_UR50D''': '''https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt''',
    },
}

__magic_name__  							=	{
    '''facebook/esm2_t6_8M_UR50D''': 1_024,
    '''facebook/esm2_t12_35M_UR50D''': 1_024,
}





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    with open(__lowerCAmelCase     ,		"r"				) as f:
        snake_case__    =					f.read().splitlines()
        return [l.strip() for l in lines]





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Optional[int]     						=  VOCAB_FILES_NAMES
      _A    :		List[Any]     						=  PRETRAINED_VOCAB_FILES_MAP
      _A    :		List[Any]     						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A    :		Any     						=  ['input_ids', 'attention_mask']
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase="<unk>"		,						lowerCamelCase="<cls>"		,						lowerCamelCase="<pad>"		,						lowerCamelCase="<mask>"		,						lowerCamelCase="<eos>"		,						**lowerCamelCase		,						):
          super().__init__(**lowerCamelCase			)
          snake_case__    =					load_vocab_file(lowerCamelCase			)
          snake_case__    =					dict(enumerate(self.all_tokens			)			)
          snake_case__    =					{tok: ind for ind, tok in enumerate(self.all_tokens			)}
          snake_case__    =					unk_token
          snake_case__    =					cls_token
          snake_case__    =					pad_token
          snake_case__    =					mask_token
          snake_case__    =					eos_token
          snake_case__    =					self.all_tokens
          self._create_trie(self.unique_no_split_tokens			)
      def A_      (      self		,						lowerCamelCase			):
          return self._id_to_token.get(lowerCamelCase		,						self.unk_token			)
      def A_      (      self		,						lowerCamelCase			):
          return self._token_to_id.get(lowerCamelCase		,						self._token_to_id.get(self.unk_token			)			)
      def A_      (      self		,						lowerCamelCase		,						**lowerCamelCase			):
          return text.split()
      def A_      (      self		,						lowerCamelCase=False			):
          return len(self._id_to_token			)
      def A_      (      self			):
          return {token: i for i, token in enumerate(self.all_tokens			)}
      def A_      (      self		,						lowerCamelCase			):
          return self._token_to_id.get(lowerCamelCase		,						self._token_to_id.get(self.unk_token			)			)
      def A_      (      self		,						lowerCamelCase			):
          return self._id_to_token.get(lowerCamelCase		,						self.unk_token			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase = None			):
          snake_case__    =					[self.cls_token_id]
          snake_case__    =					[self.eos_token_id]  # No sep token in ESM vocabulary
          if token_ids_a is None:
              if self.eos_token_id is None:
                  return cls + token_ids_a
              else:
                  return cls + token_ids_a + sep
          elif self.eos_token_id is None:
              raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!"			)
          return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase = None		,						lowerCamelCase = False			):
          if already_has_special_tokens:
              if token_ids_a is not None:
                  raise ValueError(
                      "You should not supply a second sequence if the provided sequence of "
                      "ids is already formatted with special tokens for the model."			)

              return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
          snake_case__    =					[1] + ([0] * len(lowerCamelCase			)) + [1]
          if token_ids_a is not None:
              mask += [0] * len(lowerCamelCase			) + [1]
          return mask
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					os.path.join(lowerCamelCase		,						(filename_prefix + "-" if filename_prefix else "") + "vocab.txt"			)
          with open(lowerCamelCase		,						"w"			) as f:
              f.write("\n".join(self.all_tokens			)			)
          return (vocab_file,)
      @property
      def A_      (      self			):
          return self.get_vocab_size(with_added_tokens=lowerCamelCase			)



      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase = False			):
          return super()._add_tokens(lowerCamelCase		,						special_tokens=lowerCamelCase			)





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        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      def A_      (      self			):
          snake_case__    =					"ZinengTang/tvlt-base"
          snake_case__    =					tempfile.mkdtemp()
      def A_      (      self		,						**lowerCamelCase			):
          return TvltImageProcessor.from_pretrained(self.checkpoint		,						**lowerCamelCase			)
      def A_      (      self		,						**lowerCamelCase			):
          return TvltFeatureExtractor.from_pretrained(self.checkpoint		,						**lowerCamelCase			)
      def A_      (      self			):
          shutil.rmtree(self.tmpdirname			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)
          processor.save_pretrained(self.tmpdirname			)
          snake_case__    =					TvltProcessor.from_pretrained(self.tmpdirname			)

          self.assertIsInstance(processor.feature_extractor		,						lowerCamelCase			)
          self.assertIsInstance(processor.image_processor		,						lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([1_20_00]			)

          snake_case__    =					feature_extractor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					processor(audio=lowerCamelCase		,						return_tensors="np"			)

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

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([3, 2_24, 2_24]			)

          snake_case__    =					image_processor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					processor(images=lowerCamelCase		,						return_tensors="np"			)

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

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([1_20_00]			)
          snake_case__    =					np.ones([3, 2_24, 2_24]			)

          snake_case__    =					processor(audio=lowerCamelCase		,						images=lowerCamelCase			)

          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(lowerCamelCase			):
              processor()



      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          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"		,						)



