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     a_			(						unittest.TestCase						):






    '''simple docstring'''






    def 		__snake_case						(      self :  Tuple):







           '''simple docstring'''



           lowerCAmelCase__					     = tempfile.mkdtemp()

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

           lowerCAmelCase__					     = InstructBlipProcessor(lowercase__      ,   lowercase__      ,   lowercase__)

           processor.save_pretrained(self.tmpdirname)




    def 		__snake_case						(      self :  int      ,   **lowercase__ :  Any):







           '''simple docstring'''



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




    def 		__snake_case						(      self :  Any      ,   **lowercase__ :  Union[str, Any]):







           '''simple docstring'''



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




    def 		__snake_case						(      self :  Union[str, Any]      ,   **lowercase__ :  Tuple):







           '''simple docstring'''



           return AutoProcessor.from_pretrained(self.tmpdirname      ,   **lowercase__).qformer_tokenizer




    def 		__snake_case						(      self :  Dict):







           '''simple docstring'''



           shutil.rmtree(self.tmpdirname)




    def 		__snake_case						(      self :  List[Any]):







           '''simple docstring'''



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

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

           return image_inputs




    def 		__snake_case						(      self :  Dict):







           '''simple docstring'''



           lowerCAmelCase__					     = InstructBlipProcessor(
               tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()      ,   qformer_tokenizer=self.get_qformer_tokenizer()      ,   )
           processor.save_pretrained(self.tmpdirname)

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

           lowerCAmelCase__					     = InstructBlipProcessor.from_pretrained(
               self.tmpdirname      ,   bos_token='(BOS)'      ,   eos_token='(EOS)'      ,   do_normalize=lowercase__      ,   padding_value=1.0)

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

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




    def 		__snake_case						(      self :  Optional[int]):







           '''simple docstring'''



           lowerCAmelCase__					     = self.get_image_processor()
           lowerCAmelCase__					     = self.get_tokenizer()
           lowerCAmelCase__					     = self.get_qformer_tokenizer()

           lowerCAmelCase__					     = InstructBlipProcessor(
               tokenizer=lowercase__      ,   image_processor=lowercase__      ,   qformer_tokenizer=lowercase__)

           lowerCAmelCase__					     = self.prepare_image_inputs()

           lowerCAmelCase__					     = image_processor(lowercase__      ,   return_tensors='np')
           lowerCAmelCase__					     = processor(images=lowercase__      ,   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 :  int):







           '''simple docstring'''



           lowerCAmelCase__					     = self.get_image_processor()
           lowerCAmelCase__					     = self.get_tokenizer()
           lowerCAmelCase__					     = self.get_qformer_tokenizer()

           lowerCAmelCase__					     = InstructBlipProcessor(
               tokenizer=lowercase__      ,   image_processor=lowercase__      ,   qformer_tokenizer=lowercase__)

           lowerCAmelCase__					     = """lower newer"""

           lowerCAmelCase__					     = processor(text=lowercase__)

           lowerCAmelCase__					     = tokenizer(lowercase__      ,   return_token_type_ids=lowercase__)
           lowerCAmelCase__					     = qformer_tokenizer(lowercase__      ,   return_token_type_ids=lowercase__)

           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 		__snake_case						(      self :  int):







           '''simple docstring'''



           lowerCAmelCase__					     = self.get_image_processor()
           lowerCAmelCase__					     = self.get_tokenizer()
           lowerCAmelCase__					     = self.get_qformer_tokenizer()

           lowerCAmelCase__					     = InstructBlipProcessor(
               tokenizer=lowercase__      ,   image_processor=lowercase__      ,   qformer_tokenizer=lowercase__)

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

           lowerCAmelCase__					     = processor(text=lowercase__      ,   images=lowercase__)

           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(lowercase__):
                  processor()




    def 		__snake_case						(      self :  Optional[int]):







           '''simple docstring'''



           lowerCAmelCase__					     = self.get_image_processor()
           lowerCAmelCase__					     = self.get_tokenizer()
           lowerCAmelCase__					     = self.get_qformer_tokenizer()

           lowerCAmelCase__					     = InstructBlipProcessor(
               tokenizer=lowercase__      ,   image_processor=lowercase__      ,   qformer_tokenizer=lowercase__)

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

           lowerCAmelCase__					     = processor.batch_decode(lowercase__)
           lowerCAmelCase__					     = tokenizer.batch_decode(lowercase__)

           self.assertListEqual(lowercase__      ,   lowercase__)




    def 		__snake_case						(      self :  Dict):







           '''simple docstring'''



           lowerCAmelCase__					     = self.get_image_processor()
           lowerCAmelCase__					     = self.get_tokenizer()
           lowerCAmelCase__					     = self.get_qformer_tokenizer()

           lowerCAmelCase__					     = InstructBlipProcessor(
               tokenizer=lowercase__      ,   image_processor=lowercase__      ,   qformer_tokenizer=lowercase__)

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

           lowerCAmelCase__					     = processor(text=lowercase__      ,   images=lowercase__)

           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_tf_available, is_torch_available


lowerCAmelCase__     						=			{
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'AdaptiveEmbedding',
        'TransfoXLForSequenceClassification',
        'TransfoXLLMHeadModel',
        'TransfoXLModel',
        'TransfoXLPreTrainedModel',
        'load_tf_weights_in_transfo_xl',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFAdaptiveEmbedding',
        'TFTransfoXLForSequenceClassification',
        'TFTransfoXLLMHeadModel',
        'TFTransfoXLMainLayer',
        'TFTransfoXLModel',
        'TFTransfoXLPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
    from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_transfo_xl import (
            TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            AdaptiveEmbedding,
            TransfoXLForSequenceClassification,
            TransfoXLLMHeadModel,
            TransfoXLModel,
            TransfoXLPreTrainedModel,
            load_tf_weights_in_transfo_xl,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_transfo_xl import (
            TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAdaptiveEmbedding,
            TFTransfoXLForSequenceClassification,
            TFTransfoXLLMHeadModel,
            TFTransfoXLMainLayer,
            TFTransfoXLModel,
            TFTransfoXLPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__     						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
