
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,
       BertTokenizerFast,
       BlipImageProcessor,
       GPTaTokenizer,
       InstructBlipProcessor,
       PreTrainedTokenizerFast,
   )







@require_vision
class   lowerCamelCase	(						unittest.TestCase						):





     def 	UpperCAmelCase(self   :					str     )		->		Dict:
         snake_case				  =      tempfile.mkdtemp()

         snake_case				  =      BlipImageProcessor()
         snake_case				  =      GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"     )
         snake_case				  =      BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert"     )

         snake_case				  =      InstructBlipProcessor(_A       ,					_A       ,					_A     )

         processor.save_pretrained(self.tmpdirname     )





     def 	UpperCAmelCase(self   :					Union[str, Any]       ,					**_A   :					str     )		->		Union[str, Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,					**_A     ).tokenizer





     def 	UpperCAmelCase(self   :					Optional[Any]       ,					**_A   :					str     )		->		str:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,					**_A     ).image_processor





     def 	UpperCAmelCase(self   :					Optional[int]       ,					**_A   :					Dict     )		->		List[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,					**_A     ).qformer_tokenizer





     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Any:
         shutil.rmtree(self.tmpdirname     )





     def 	UpperCAmelCase(self   :					str     )		->		List[str]:
         snake_case				  =      [np.random.randint(2_5_5       ,					size=(3, 3_0, 4_0_0)       ,					dtype=np.uinta     )]

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

         return image_inputs





     def 	UpperCAmelCase(self   :					Any     )		->		List[str]:
         snake_case				  =      InstructBlipProcessor(
             tokenizer=self.get_tokenizer()       ,					image_processor=self.get_image_processor()       ,					qformer_tokenizer=self.get_qformer_tokenizer()       ,					)
         processor.save_pretrained(self.tmpdirname     )

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

         snake_case				  =      InstructBlipProcessor.from_pretrained(
             self.tmpdirname       ,					bos_token="(BOS)"       ,					eos_token="(EOS)"       ,					do_normalize=_A       ,					padding_value=1.0     )

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

         self.assertEqual(processor.image_processor.to_json_string()       ,					image_processor_add_kwargs.to_json_string()     )
         self.assertIsInstance(processor.image_processor       ,					_A     )
         self.assertIsInstance(processor.qformer_tokenizer       ,					_A     )





     def 	UpperCAmelCase(self   :					Any     )		->		List[str]:
         snake_case				  =      self.get_image_processor()
         snake_case				  =      self.get_tokenizer()
         snake_case				  =      self.get_qformer_tokenizer()

         snake_case				  =      InstructBlipProcessor(
             tokenizer=_A       ,					image_processor=_A       ,					qformer_tokenizer=_A     )

         snake_case				  =      self.prepare_image_inputs()

         snake_case				  =      image_processor(_A       ,					return_tensors="np"     )
         snake_case				  =      processor(images=_A       ,					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   :					Dict     )		->		Union[str, Any]:
         snake_case				  =      self.get_image_processor()
         snake_case				  =      self.get_tokenizer()
         snake_case				  =      self.get_qformer_tokenizer()

         snake_case				  =      InstructBlipProcessor(
             tokenizer=_A       ,					image_processor=_A       ,					qformer_tokenizer=_A     )

         snake_case				  =      "lower newer"

         snake_case				  =      processor(text=_A     )

         snake_case				  =      tokenizer(_A       ,					return_token_type_ids=_A     )
         snake_case				  =      qformer_tokenizer(_A       ,					return_token_type_ids=_A     )

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

         for key in encoded_tokens_qformer.keys():
             self.assertListEqual(encoded_tokens_qformer[key]       ,					encoded_processor["qformer_" + key]     )





     def 	UpperCAmelCase(self   :					List[str]     )		->		Tuple:
         snake_case				  =      self.get_image_processor()
         snake_case				  =      self.get_tokenizer()
         snake_case				  =      self.get_qformer_tokenizer()

         snake_case				  =      InstructBlipProcessor(
             tokenizer=_A       ,					image_processor=_A       ,					qformer_tokenizer=_A     )

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

         snake_case				  =      processor(text=_A       ,					images=_A     )

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

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





     def 	UpperCAmelCase(self   :					Dict     )		->		Any:
         snake_case				  =      self.get_image_processor()
         snake_case				  =      self.get_tokenizer()
         snake_case				  =      self.get_qformer_tokenizer()

         snake_case				  =      InstructBlipProcessor(
             tokenizer=_A       ,					image_processor=_A       ,					qformer_tokenizer=_A     )

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

         snake_case				  =      processor.batch_decode(_A     )
         snake_case				  =      tokenizer.batch_decode(_A     )

         self.assertListEqual(_A       ,					_A     )





     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         snake_case				  =      self.get_image_processor()
         snake_case				  =      self.get_tokenizer()
         snake_case				  =      self.get_qformer_tokenizer()

         snake_case				  =      InstructBlipProcessor(
             tokenizer=_A       ,					image_processor=_A       ,					qformer_tokenizer=_A     )

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

         snake_case				  =      processor(text=_A       ,					images=_A     )

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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A						      =				{
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "Wav2Vec2ForAudioFrameClassification",
       "Wav2Vec2ForCTC",
       "Wav2Vec2ForMaskedLM",
       "Wav2Vec2ForPreTraining",
       "Wav2Vec2ForSequenceClassification",
       "Wav2Vec2ForXVector",
       "Wav2Vec2Model",
       "Wav2Vec2PreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFWav2Vec2ForCTC",
       "TFWav2Vec2Model",
       "TFWav2Vec2PreTrainedModel",
       "TFWav2Vec2ForSequenceClassification",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "FlaxWav2Vec2ForCTC",
       "FlaxWav2Vec2ForPreTraining",
       "FlaxWav2Vec2Model",
       "FlaxWav2Vec2PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
   from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
   from .processing_wavaveca import WavaVecaProcessor
   from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca import (
          WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaForAudioFrameClassification,
          WavaVecaForCTC,
          WavaVecaForMaskedLM,
          WavaVecaForPreTraining,
          WavaVecaForSequenceClassification,
          WavaVecaForXVector,
          WavaVecaModel,
          WavaVecaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWavaVecaForCTC,
          TFWavaVecaForSequenceClassification,
          TFWavaVecaModel,
          TFWavaVecaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          FlaxWavaVecaForCTC,
          FlaxWavaVecaForPreTraining,
          FlaxWavaVecaModel,
          FlaxWavaVecaPreTrainedModel,
      )


else:
   import sys

   _A						      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

