





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 			__UpperCamelCase    (							unittest.TestCase    ):





     def 		UpperCamelCase(     self					):
        _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					):
        return AutoProcessor.from_pretrained(self.tmpdirname				,							**_UpperCamelCase					).tokenizer





     def 		UpperCamelCase(     self				,							**_UpperCamelCase					):
        return AutoProcessor.from_pretrained(self.tmpdirname				,							**_UpperCamelCase					).image_processor





     def 		UpperCamelCase(     self					):
        shutil.rmtree(self.tmpdirname					)





     def 		UpperCamelCase(     self					):
        _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					):
        _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					):
        _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					):
        _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					):
        _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					):
        _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					):
        _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''']					)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase_           =	{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_           =	[
       "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "XLMForMultipleChoice",
       "XLMForQuestionAnswering",
       "XLMForQuestionAnsweringSimple",
       "XLMForSequenceClassification",
       "XLMForTokenClassification",
       "XLMModel",
       "XLMPreTrainedModel",
       "XLMWithLMHeadModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_           =	[
       "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFXLMForMultipleChoice",
       "TFXLMForQuestionAnsweringSimple",
       "TFXLMForSequenceClassification",
       "TFXLMForTokenClassification",
       "TFXLMMainLayer",
       "TFXLMModel",
       "TFXLMPreTrainedModel",
       "TFXLMWithLMHeadModel",
   ]


if TYPE_CHECKING:
   from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
   from .tokenization_xlm import XLMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm import (
          XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMPreTrainedModel,
          XLMWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMPreTrainedModel,
          TFXLMWithLMHeadModel,
      )

else:
   import sys

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