

'''simple docstring'''



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast







@require_vision
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       Any			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()

      __SCREAMING_SNAKE_CASE     =      BlipImageProcessor()
      __SCREAMING_SNAKE_CASE     =      BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""			)

      __SCREAMING_SNAKE_CASE     =      BlipProcessor(__lowerCAmelCase					,							__lowerCAmelCase			)

      processor.save_pretrained(self.tmpdirname			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]					,							**__SCREAMING_SNAKE_CASE					:       Optional[int]			)  -> Optional[int]:


      """simple docstring"""




      return AutoProcessor.from_pretrained(self.tmpdirname					,							**__lowerCAmelCase			).tokenizer
 def       UpperCAmelCase__		(		self					:       Dict					,							**__SCREAMING_SNAKE_CASE					:       str			)  -> List[Any]:


      """simple docstring"""




      return AutoProcessor.from_pretrained(self.tmpdirname					,							**__lowerCAmelCase			).image_processor
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Dict:


      """simple docstring"""




      shutil.rmtree(self.tmpdirname			)
 def       UpperCAmelCase__		(		self					:       int			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      [np.random.randint(255					,							size=(3, 30, 400)					,							dtype=np.uinta			)]

      __SCREAMING_SNAKE_CASE     =      [Image.fromarray(np.moveaxis(__lowerCAmelCase					,							0					,							-1			)			) for x in image_inputs]

      return image_inputs
 def       UpperCAmelCase__		(		self					:       Any			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      BlipProcessor(tokenizer=self.get_tokenizer()					,							image_processor=self.get_image_processor()			)
      processor.save_pretrained(self.tmpdirname			)

      __SCREAMING_SNAKE_CASE     =      self.get_tokenizer(bos_token="""(BOS)"""					,							eos_token="""(EOS)"""			)
      __SCREAMING_SNAKE_CASE     =      self.get_image_processor(do_normalize=__lowerCAmelCase					,							padding_value=1.0			)

      __SCREAMING_SNAKE_CASE     =      BlipProcessor.from_pretrained(
          self.tmpdirname					,							bos_token="""(BOS)"""					,							eos_token="""(EOS)"""					,							do_normalize=__lowerCAmelCase					,							padding_value=1.0			)

      self.assertEqual(processor.tokenizer.get_vocab()					,							tokenizer_add_kwargs.get_vocab()			)
      self.assertIsInstance(processor.tokenizer					,							__lowerCAmelCase			)

      self.assertEqual(processor.image_processor.to_json_string()					,							image_processor_add_kwargs.to_json_string()			)
      self.assertIsInstance(processor.image_processor					,							__lowerCAmelCase			)
 def       UpperCAmelCase__		(		self					:       Any			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.get_image_processor()
      __SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

      __SCREAMING_SNAKE_CASE     =      BlipProcessor(tokenizer=__lowerCAmelCase					,							image_processor=__lowerCAmelCase			)

      __SCREAMING_SNAKE_CASE     =      self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE     =      image_processor(__lowerCAmelCase					,							return_tensors="""np"""			)
      __SCREAMING_SNAKE_CASE     =      processor(images=__lowerCAmelCase					,							return_tensors="""np"""			)

      for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()					,							input_processor[key].sum()					,							delta=1E-2			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.get_image_processor()
      __SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

      __SCREAMING_SNAKE_CASE     =      BlipProcessor(tokenizer=__lowerCAmelCase					,							image_processor=__lowerCAmelCase			)

      __SCREAMING_SNAKE_CASE     =      """lower newer"""

      __SCREAMING_SNAKE_CASE     =      processor(text=__lowerCAmelCase			)

      __SCREAMING_SNAKE_CASE     =      tokenizer(__lowerCAmelCase					,							return_token_type_ids=__lowerCAmelCase			)

      for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]					,							encoded_processor[key]			)
 def       UpperCAmelCase__		(		self					:       int			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.get_image_processor()
      __SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

      __SCREAMING_SNAKE_CASE     =      BlipProcessor(tokenizer=__lowerCAmelCase					,							image_processor=__lowerCAmelCase			)

      __SCREAMING_SNAKE_CASE     =      """lower newer"""
      __SCREAMING_SNAKE_CASE     =      self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE     =      processor(text=__lowerCAmelCase					,							images=__lowerCAmelCase			)

      self.assertListEqual(list(inputs.keys()			)					,							["""pixel_values""", """input_ids""", """attention_mask"""]			)

      # test if it raises when no input is passed
      with pytest.raises(__lowerCAmelCase			):
           processor()
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.get_image_processor()
      __SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

      __SCREAMING_SNAKE_CASE     =      BlipProcessor(tokenizer=__lowerCAmelCase					,							image_processor=__lowerCAmelCase			)

      __SCREAMING_SNAKE_CASE     =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      __SCREAMING_SNAKE_CASE     =      processor.batch_decode(__lowerCAmelCase			)
      __SCREAMING_SNAKE_CASE     =      tokenizer.batch_decode(__lowerCAmelCase			)

      self.assertListEqual(__lowerCAmelCase					,							__lowerCAmelCase			)


 def       UpperCAmelCase__		(		self					:       str			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.get_image_processor()
      __SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

      __SCREAMING_SNAKE_CASE     =      BlipProcessor(tokenizer=__lowerCAmelCase					,							image_processor=__lowerCAmelCase			)

      __SCREAMING_SNAKE_CASE     =      """lower newer"""
      __SCREAMING_SNAKE_CASE     =      self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE     =      processor(text=__lowerCAmelCase					,							images=__lowerCAmelCase			)

      # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
      self.assertListEqual(list(inputs.keys()			)					,							["""pixel_values""", """input_ids""", """attention_mask"""]			)







'''simple docstring'''




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     str      =       '''EncodecFeatureExtractor'''
			UpperCAmelCase_			:     Dict      =       ('''T5Tokenizer''', '''T5TokenizerFast''')



			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					super().__init__(__lowerCAmelCase       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  self.feature_extractor
					lowerCAmelCase   					=  False



			def 							a_       (   self       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True):




					"""simple docstring"""


					return self.tokenizer.get_decoder_prompt_ids(task=__lowerCAmelCase       ,    language=__lowerCAmelCase       ,    no_timestamps=__lowerCAmelCase)



			def __call__(   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					if self._in_target_context_manager:
							return self.current_processor(*__lowerCAmelCase       ,    **__lowerCAmelCase)

					lowerCAmelCase   					=  kwargs.pop("""audio"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""sampling_rate"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""text"""       ,    __lowerCAmelCase)
					if len(__lowerCAmelCase) > 0:
							lowerCAmelCase   					=  args[0]
							lowerCAmelCase   					=  args[1:]

					if audio is None and text is None:
							raise ValueError("""You need to specify either an `audio` or `text` input to process.""")

					if text is not None:
							lowerCAmelCase   					=  self.tokenizer(__lowerCAmelCase       ,    **__lowerCAmelCase)

					if audio is not None:
							lowerCAmelCase   					=  self.feature_extractor(__lowerCAmelCase       ,    *__lowerCAmelCase       ,    sampling_rate=__lowerCAmelCase       ,    **__lowerCAmelCase)

					if audio is None:
							return inputs

					elif text is None:
							return audio_inputs

					else:
							lowerCAmelCase   					=  audio_inputs["""input_values"""]
							if "padding_mask" in audio_inputs:
									lowerCAmelCase   					=  audio_inputs["""padding_mask"""]
							return inputs



			def 							a_       (   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  kwargs.pop("""audio"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""padding_mask"""       ,    __lowerCAmelCase)

					if len(__lowerCAmelCase) > 0:
							lowerCAmelCase   					=  args[0]
							lowerCAmelCase   					=  args[1:]

					if audio_values is not None:
							return self._decode_audio(__lowerCAmelCase       ,    padding_mask=__lowerCAmelCase)
					else:
							return self.tokenizer.batch_decode(*__lowerCAmelCase       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


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







			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase   					=  to_numpy(__lowerCAmelCase)
					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  audio_values.shape

					if padding_mask is None:
							return list(__lowerCAmelCase)

					lowerCAmelCase   					=  to_numpy(__lowerCAmelCase)

					# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
					# token (so that the generated audio values are **not** treated as padded tokens)
					lowerCAmelCase   					=  seq_len - padding_mask.shape[-1]
					lowerCAmelCase   					=  1 - self.feature_extractor.padding_value
					lowerCAmelCase   					=  np.pad(__lowerCAmelCase       ,    ((0, 0), (0, difference))       ,    """constant"""       ,    constant_values=__lowerCAmelCase)

					lowerCAmelCase   					=  audio_values.tolist()
					for i in range(__lowerCAmelCase):
							lowerCAmelCase   					=  np.asarray(audio_values[i])[
							    padding_mask[i][None, :] != self.feature_extractor.padding_value
							]
							lowerCAmelCase   					=  sliced_audio.reshape(__lowerCAmelCase       ,    -1)

					return audio_values




