
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    ):





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= tempfile.mkdtemp()

      __UpperCamelCase       						= BlipImageProcessor()
      __UpperCamelCase       						= BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'   )

      __UpperCamelCase       						= BlipProcessor(__UpperCAmelCase			,  __UpperCAmelCase   )

      processor.save_pretrained(self.tmpdirname   )





     def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      return AutoProcessor.from_pretrained(self.tmpdirname			,  **__UpperCAmelCase   ).tokenizer





     def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      return AutoProcessor.from_pretrained(self.tmpdirname			,  **__UpperCAmelCase   ).image_processor





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      shutil.rmtree(self.tmpdirname   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

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

      return image_inputs





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      __UpperCamelCase       						= self.get_tokenizer(bos_token='(BOS)'			,  eos_token='(EOS)'   )
      __UpperCamelCase       						= self.get_image_processor(do_normalize=__UpperCAmelCase			,  padding_value=1.0   )

      __UpperCamelCase       						= BlipProcessor.from_pretrained(
          self.tmpdirname			,  bos_token='(BOS)'			,  eos_token='(EOS)'			,  do_normalize=__UpperCAmelCase			,  padding_value=1.0   )

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

      self.assertEqual(processor.image_processor.to_json_string()			,  image_processor_add_kwargs.to_json_string()   )
      self.assertIsInstance(processor.image_processor			,  __UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

      __UpperCamelCase       						= self.prepare_image_inputs()

      __UpperCamelCase       						= image_processor(__UpperCAmelCase			,  return_tensors='np'   )
      __UpperCamelCase       						= processor(images=__UpperCAmelCase			,  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   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

      __UpperCamelCase       						= 'lower newer'

      __UpperCamelCase       						= processor(text=__UpperCAmelCase   )

      __UpperCamelCase       						= tokenizer(__UpperCAmelCase			,  return_token_type_ids=__UpperCAmelCase   )

      for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]			,  encoded_processor[key]   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

      __UpperCamelCase       						= 'lower newer'
      __UpperCamelCase       						= self.prepare_image_inputs()

      __UpperCamelCase       						= processor(text=__UpperCAmelCase			,  images=__UpperCAmelCase   )

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

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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

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

      __UpperCamelCase       						= processor.batch_decode(__UpperCAmelCase   )
      __UpperCamelCase       						= tokenizer.batch_decode(__UpperCAmelCase   )

      self.assertListEqual(__UpperCAmelCase			,  __UpperCAmelCase   )







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.get_image_processor()
      __UpperCamelCase       						= self.get_tokenizer()

      __UpperCamelCase       						= BlipProcessor(tokenizer=__UpperCAmelCase			,  image_processor=__UpperCAmelCase   )

      __UpperCamelCase       						= 'lower newer'
      __UpperCamelCase       						= self.prepare_image_inputs()

      __UpperCamelCase       						= processor(text=__UpperCAmelCase			,  images=__UpperCAmelCase   )

      # 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"""

import re

from filelock import FileLock


try:
      import nltk

      UpperCamelCase			:       int									=				True
except (ImportError, ModuleNotFoundError):
      UpperCamelCase			:       List[str]									=				False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)






def 							A		(       snake_case   :str	)   ->			str:
 re.sub('<n>'       ,      ''       ,      snake_case	)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(snake_case	)	)



