





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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class   __lowerCAmelCase			(					unittest.TestCase  ):





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						Tuple      		=  tempfile.mkdtemp()

      a__	:						Optional[int]      		=  BlipImageProcessor()
      a__	:						Any      		=  GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"      )

      a__	:						Tuple      		=  BlipaProcessor(snake_case					,			snake_case      )

      processor.save_pretrained(self.tmpdirname      )





   def 	_snake_case ( self					,			**snake_case      )				->  List[Any]:
      """simple docstring"""





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





   def 	_snake_case ( self					,			**snake_case      )				->  List[str]:
      """simple docstring"""





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





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      shutil.rmtree(self.tmpdirname      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  [np.random.randint(255					,			size=(3, 30, 400)					,			dtype=np.uinta      )]

      a__	:						int      		=  [Image.fromarray(np.moveaxis(snake_case					,			0					,			-1      )      ) for x in image_inputs]

      return image_inputs





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Tuple      		=  BlipaProcessor(tokenizer=self.get_tokenizer()					,			image_processor=self.get_image_processor()      )
      processor.save_pretrained(self.tmpdirname      )

      a__	:						Tuple      		=  self.get_tokenizer(bos_token="(BOS)"					,			eos_token="(EOS)"      )
      a__	:						int      		=  self.get_image_processor(do_normalize=snake_case					,			padding_value=1.0      )

      a__	:						Optional[int]      		=  BlipaProcessor.from_pretrained(
          self.tmpdirname					,			bos_token="(BOS)"					,			eos_token="(EOS)"					,			do_normalize=snake_case					,			padding_value=1.0      )

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

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





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Dict      		=  self.get_image_processor()
      a__	:						Any      		=  self.get_tokenizer()

      a__	:						str      		=  BlipaProcessor(tokenizer=snake_case					,			image_processor=snake_case      )

      a__	:						str      		=  self.prepare_image_inputs()

      a__	:						Dict      		=  image_processor(snake_case					,			return_tensors="np"      )
      a__	:						Optional[int]      		=  processor(images=snake_case					,			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 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  self.get_image_processor()
      a__	:						int      		=  self.get_tokenizer()

      a__	:						Union[str, Any]      		=  BlipaProcessor(tokenizer=snake_case					,			image_processor=snake_case      )

      a__	:						Optional[int]      		=  "lower newer"

      a__	:						Dict      		=  processor(text=snake_case      )

      a__	:						str      		=  tokenizer(snake_case					,			return_token_type_ids=snake_case      )

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





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  self.get_image_processor()
      a__	:						Dict      		=  self.get_tokenizer()

      a__	:						Union[str, Any]      		=  BlipaProcessor(tokenizer=snake_case					,			image_processor=snake_case      )

      a__	:						List[Any]      		=  "lower newer"
      a__	:						Any      		=  self.prepare_image_inputs()

      a__	:						Any      		=  processor(text=snake_case					,			images=snake_case      )

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

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





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  self.get_image_processor()
      a__	:						Union[str, Any]      		=  self.get_tokenizer()

      a__	:						List[Any]      		=  BlipaProcessor(tokenizer=snake_case					,			image_processor=snake_case      )

      a__	:						List[str]      		=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      a__	:						List[Any]      		=  processor.batch_decode(snake_case      )
      a__	:						int      		=  tokenizer.batch_decode(snake_case      )

      self.assertListEqual(snake_case					,			snake_case      )





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Any      		=  self.get_image_processor()
      a__	:						Optional[int]      		=  self.get_tokenizer()

      a__	:						Optional[Any]      		=  BlipaProcessor(tokenizer=snake_case					,			image_processor=snake_case      )

      a__	:						Tuple      		=  "lower newer"
      a__	:						Dict      		=  self.prepare_image_inputs()

      a__	:						str      		=  processor(text=snake_case					,			images=snake_case      )

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__       :			Any			      =						{"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						[
      """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """SEWForCTC""",
      """SEWForSequenceClassification""",
      """SEWModel""",
      """SEWPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

