
import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Union[str, Any]     						=  ['image_processor', 'tokenizer']
      _A    :		str     						=  'FlavaImageProcessor'
      _A    :		Union[str, Any]     						=  ('BertTokenizer', 'BertTokenizerFast')
      def __init__(      self		,						lowerCamelCase=None		,						lowerCamelCase=None		,						**lowerCamelCase			):
          snake_case__    =					None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                  " instead."		,						lowerCamelCase		,						)
              snake_case__    =					kwargs.pop("feature_extractor"			)

          snake_case__    =					image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError("You need to specify an `image_processor`."			)
          if tokenizer is None:
              raise ValueError("You need to specify a `tokenizer`."			)

          super().__init__(lowerCamelCase		,						lowerCamelCase			)
          snake_case__    =					self.image_processor
      def __call__(      self		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = True		,						lowerCamelCase = False		,						lowerCamelCase = False		,						lowerCamelCase = None		,						lowerCamelCase = 0		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = None		,						lowerCamelCase = False		,						lowerCamelCase = False		,						lowerCamelCase = False		,						lowerCamelCase = False		,						lowerCamelCase = True		,						lowerCamelCase = None		,						**lowerCamelCase		,						):

          if text is None and images is None:
              raise ValueError("You have to specify either text or images. Both cannot be none."			)

          if text is not None:
              snake_case__    =					self.tokenizer(
                  text=lowerCamelCase		,						add_special_tokens=lowerCamelCase		,						padding=lowerCamelCase		,						truncation=lowerCamelCase		,						max_length=lowerCamelCase		,						stride=lowerCamelCase		,						pad_to_multiple_of=lowerCamelCase		,						return_token_type_ids=lowerCamelCase		,						return_attention_mask=lowerCamelCase		,						return_overflowing_tokens=lowerCamelCase		,						return_special_tokens_mask=lowerCamelCase		,						return_offsets_mapping=lowerCamelCase		,						return_length=lowerCamelCase		,						verbose=lowerCamelCase		,						return_tensors=lowerCamelCase		,						**lowerCamelCase		,						)
          if images is not None:
              snake_case__    =					self.image_processor(
                  lowerCamelCase		,						return_image_mask=lowerCamelCase		,						return_codebook_pixels=lowerCamelCase		,						return_tensors=lowerCamelCase		,						**lowerCamelCase		,						)

          if text is not None and images is not None:
              encoding.update(lowerCamelCase			)
              return encoding
          elif text is not None:
              return encoding
          else:
              return BatchEncoding(data=dict(**lowerCamelCase			)		,						tensor_type=lowerCamelCase			)
      def A_      (      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          return self.tokenizer.batch_decode(*lowerCamelCase		,						**lowerCamelCase			)
      def A_      (      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          return self.tokenizer.decode(*lowerCamelCase		,						**lowerCamelCase			)
      @property
      def A_      (      self			):
          snake_case__    =					self.tokenizer.model_input_names
          snake_case__    =					self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)
      @property
      def A_      (      self			):
          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,						lowerCamelCase		,						)
          return self.image_processor_class



      @property
      def A_      (      self			):
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,						lowerCamelCase		,						)
          return self.image_processor





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"		,						)



