




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 		__A   (      __UpperCamelCase      ):
						UpperCamelCase      =       ['image_processor', 'tokenizer']
						UpperCamelCase      =       'ViTImageProcessor'
						UpperCamelCase      =       ('CLIPTokenizer', 'CLIPTokenizerFast')

						def __init__(		self :List[Any]     ,			__snake_case :List[str]=None     ,			__snake_case :str=None     ,			**__snake_case :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						int										=None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
														    """ instead."""     ,			__snake_case     ,			)
														__magic_name__						:						str										=kwargs.pop("""feature_extractor"""			)

										__magic_name__						:						Optional[int]										=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__(__snake_case     ,			__snake_case			)

						def __call__(		self :str     ,			__snake_case :Dict=None     ,			__snake_case :Union[str, Any]=None     ,			__snake_case :Optional[int]=None     ,			__snake_case :Union[str, Any]=None     ,			**__snake_case :Dict			):
										'''simple docstring'''

										if text is None and visual_prompt is None and images is None:
														raise ValueError("""You have to specify either text, visual prompt or images."""			)

										if text is not None and visual_prompt is not None:
														raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""			)

										if text is not None:
														__magic_name__						:						Optional[int]										=self.tokenizer(__snake_case     ,			return_tensors=__snake_case     ,			**__snake_case			)

										if visual_prompt is not None:
														__magic_name__						:						Dict										=self.image_processor(__snake_case     ,			return_tensors=__snake_case     ,			**__snake_case			)

										if images is not None:
														__magic_name__						:						Tuple										=self.image_processor(__snake_case     ,			return_tensors=__snake_case     ,			**__snake_case			)

										if visual_prompt is not None and images is not None:
														__magic_name__						:						Any										={
														    """pixel_values""": image_features.pixel_values,
														    """conditional_pixel_values""": prompt_features.pixel_values,
														}
														return encoding
										elif text is not None and images is not None:
														__magic_name__						:						str										=image_features.pixel_values
														return encoding
										elif text is not None:
														return encoding
										elif visual_prompt is not None:
														__magic_name__						:						Optional[int]										={
														    """conditional_pixel_values""": prompt_features.pixel_values,
														}
														return encoding
										else:
														return BatchEncoding(data=dict(**__snake_case			)     ,			tensor_type=__snake_case			)

						def      A__    (		self :Optional[int]     ,			*__snake_case :Tuple     ,			**__snake_case :Tuple			):
										'''simple docstring'''

										return self.tokenizer.batch_decode(*__snake_case     ,			**__snake_case			)

						def      A__    (		self :List[str]     ,			*__snake_case :Optional[int]     ,			**__snake_case :Dict			):
										'''simple docstring'''

										return self.tokenizer.decode(*__snake_case     ,			**__snake_case			)

						@property
						def      A__    (		self :Any			):
										'''simple docstring'''

										warnings.warn(
										    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,			__snake_case     ,			)
										return self.image_processor_class


						@property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,			__snake_case     ,			)
										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"		,						)



