
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			["""image_processor""", """tokenizer"""]
      _UpperCAmelCase				:    Optional[int]           =			"""BlipImageProcessor"""
      _UpperCAmelCase				:    List[str]           =			"""AutoTokenizer"""
      def __init__(      self    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Optional[Any]							=      False
       super().__init__(__magic_name__    ,  __magic_name__      )
       lowerCamelCase		:	Dict							=      self.image_processor
      def __call__(      self    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = True    ,  __magic_name__ = False    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = 0    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = False    ,  __magic_name__ = False    ,  __magic_name__ = False    ,  __magic_name__ = False    ,  __magic_name__ = False    ,  __magic_name__ = True    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       if images is None and text is None:
        raise ValueError("""You have to specify either images or text."""      )

       # Get only text
       if images is None:
        lowerCamelCase		:	List[str]							=      self.tokenizer
        lowerCamelCase		:	str							=      self.tokenizer(
            text=__magic_name__    ,  add_special_tokens=__magic_name__    ,  padding=__magic_name__    ,  truncation=__magic_name__    ,  max_length=__magic_name__    ,  stride=__magic_name__    ,  pad_to_multiple_of=__magic_name__    ,  return_attention_mask=__magic_name__    ,  return_overflowing_tokens=__magic_name__    ,  return_special_tokens_mask=__magic_name__    ,  return_offsets_mapping=__magic_name__    ,  return_token_type_ids=__magic_name__    ,  return_length=__magic_name__    ,  verbose=__magic_name__    ,  return_tensors=__magic_name__    ,  **__magic_name__    ,  )
        return text_encoding

       # add pixel_values
       lowerCamelCase		:	List[Any]							=      self.image_processor(__magic_name__    ,  return_tensors=__magic_name__      )

       if text is not None:
        lowerCamelCase		:	Union[str, Any]							=      self.tokenizer(
            text=__magic_name__    ,  add_special_tokens=__magic_name__    ,  padding=__magic_name__    ,  truncation=__magic_name__    ,  max_length=__magic_name__    ,  stride=__magic_name__    ,  pad_to_multiple_of=__magic_name__    ,  return_attention_mask=__magic_name__    ,  return_overflowing_tokens=__magic_name__    ,  return_special_tokens_mask=__magic_name__    ,  return_offsets_mapping=__magic_name__    ,  return_token_type_ids=__magic_name__    ,  return_length=__magic_name__    ,  verbose=__magic_name__    ,  return_tensors=__magic_name__    ,  **__magic_name__    ,  )
       else:
        lowerCamelCase		:	str							=      None

       if text_encoding is not None:
        encoding_image_processor.update(__magic_name__      )

       return encoding_image_processor
      def 						UpperCamelCase__							(      self    ,  *__magic_name__    ,  **__magic_name__      ):
       return self.tokenizer.batch_decode(*__magic_name__    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  *__magic_name__    ,  **__magic_name__      ):
       return self.tokenizer.decode(*__magic_name__    ,  **__magic_name__      )






      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      self.tokenizer.model_input_names
       lowerCamelCase		:	Optional[int]							=      self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFCamembertModel





@require_tf
@require_sentencepiece
@require_tokenizers
class 							A__		(	unittest.TestCase):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""      )

       lowerCamelCase		:	Any							=      tf.convert_to_tensor(
           [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,  dtype=tf.intaa    ,  )  # J'aime le camembert !"

       lowerCamelCase		:	str							=      model(__magic_name__      )["""last_hidden_state"""]
       lowerCamelCase		:	Union[str, Any]							=      tf.TensorShape((1, 1_0, 7_6_8)      )
       self.assertEqual(output.shape    ,  __magic_name__      )
       # compare the actual values for a slice.
       lowerCamelCase		:	Dict							=      tf.convert_to_tensor(
           [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]    ,  dtype=tf.floataa    ,  )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,  expected_slice.numpy()    ,  atol=1e-4      )      )




