
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 							A__		(	unittest.TestCase):
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      tempfile.mkdtemp()

       lowerCamelCase		:	Dict							=      BlipImageProcessor()
       lowerCamelCase		:	int							=      GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""      )

       lowerCamelCase		:	List[Any]							=      BlipaProcessor(__magic_name__    ,  __magic_name__      )

       processor.save_pretrained(self.tmpdirname      )
      def 						UpperCamelCase__							(      self    ,  **__magic_name__      ):
       return AutoProcessor.from_pretrained(self.tmpdirname    ,  **__magic_name__      ).tokenizer
      def 						UpperCamelCase__							(      self    ,  **__magic_name__      ):
       return AutoProcessor.from_pretrained(self.tmpdirname    ,  **__magic_name__      ).image_processor
      def 						UpperCamelCase__							(      self      ):
       shutil.rmtree(self.tmpdirname      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	int							=      [np.random.randint(2_5_5    ,  size=(3, 3_0, 4_0_0)    ,  dtype=np.uinta      )]

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

       return image_inputs
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      BlipaProcessor(tokenizer=self.get_tokenizer()    ,  image_processor=self.get_image_processor()      )
       processor.save_pretrained(self.tmpdirname      )

       lowerCamelCase		:	str							=      self.get_tokenizer(bos_token="""(BOS)"""    ,  eos_token="""(EOS)"""      )
       lowerCamelCase		:	Optional[Any]							=      self.get_image_processor(do_normalize=__magic_name__    ,  padding_value=1.0      )

       lowerCamelCase		:	Union[str, Any]							=      BlipaProcessor.from_pretrained(
           self.tmpdirname    ,  bos_token="""(BOS)"""    ,  eos_token="""(EOS)"""    ,  do_normalize=__magic_name__    ,  padding_value=1.0      )

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

       self.assertEqual(processor.image_processor.to_json_string()    ,  image_processor_add_kwargs.to_json_string()      )
       self.assertIsInstance(processor.image_processor    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      self.get_image_processor()
       lowerCamelCase		:	Optional[int]							=      self.get_tokenizer()

       lowerCamelCase		:	List[Any]							=      BlipaProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )

       lowerCamelCase		:	int							=      self.prepare_image_inputs()

       lowerCamelCase		:	Tuple							=      image_processor(__magic_name__    ,  return_tensors="""np"""      )
       lowerCamelCase		:	Union[str, Any]							=      processor(images=__magic_name__    ,  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      ):
       lowerCamelCase		:	Tuple							=      self.get_image_processor()
       lowerCamelCase		:	int							=      self.get_tokenizer()

       lowerCamelCase		:	Union[str, Any]							=      BlipaProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )

       lowerCamelCase		:	int							=      """lower newer"""

       lowerCamelCase		:	str							=      processor(text=__magic_name__      )

       lowerCamelCase		:	Optional[Any]							=      tokenizer(__magic_name__    ,  return_token_type_ids=__magic_name__      )

       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]    ,  encoded_processor[key]      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[Any]							=      self.get_image_processor()
       lowerCamelCase		:	int							=      self.get_tokenizer()

       lowerCamelCase		:	Any							=      BlipaProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )

       lowerCamelCase		:	Optional[int]							=      """lower newer"""
       lowerCamelCase		:	int							=      self.prepare_image_inputs()

       lowerCamelCase		:	List[Any]							=      processor(text=__magic_name__    ,  images=__magic_name__      )

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

       # test if it raises when no input is passed
       with pytest.raises(__magic_name__      ):
        processor()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      self.get_image_processor()
       lowerCamelCase		:	Tuple							=      self.get_tokenizer()

       lowerCamelCase		:	List[Any]							=      BlipaProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )

       lowerCamelCase		:	Optional[Any]							=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowerCamelCase		:	Any							=      processor.batch_decode(__magic_name__      )
       lowerCamelCase		:	Optional[Any]							=      tokenizer.batch_decode(__magic_name__      )

       self.assertListEqual(__magic_name__    ,  __magic_name__      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      self.get_image_processor()
       lowerCamelCase		:	Any							=      self.get_tokenizer()

       lowerCamelCase		:	Dict							=      BlipaProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )

       lowerCamelCase		:	Tuple							=      """lower newer"""
       lowerCamelCase		:	Optional[Any]							=      self.prepare_image_inputs()

       lowerCamelCase		:	List[str]							=      processor(text=__magic_name__    ,  images=__magic_name__      )

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






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""bridgetower_vision_model"""
      def __init__(      self    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=3    ,  __magic_name__=1_6    ,  __magic_name__=2_8_8    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=False    ,  __magic_name__=True    ,  __magic_name__=False    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )
       lowerCamelCase		:	Dict							=      hidden_size
       lowerCamelCase		:	str							=      num_hidden_layers
       lowerCamelCase		:	Optional[int]							=      num_channels
       lowerCamelCase		:	List[str]							=      patch_size
       lowerCamelCase		:	Tuple							=      image_size
       lowerCamelCase		:	Any							=      initializer_factor
       lowerCamelCase		:	Tuple							=      layer_norm_eps
       lowerCamelCase		:	Tuple							=      stop_gradient
       lowerCamelCase		:	Optional[int]							=      share_layernorm
       lowerCamelCase		:	str							=      remove_last_layer






      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  **__magic_name__      ):
       lowerCamelCase			,    lowerCamelCase		:	int							=      cls.get_config_dict(__magic_name__    ,  **__magic_name__      )

       if config_dict.get("""model_type"""      ) == "bridgetower":
        lowerCamelCase		:	str							=      config_dict["""text_config"""]

       if "model_type" in config_dict and hasattr(cls    ,  """model_type"""      ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
            F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

       return cls.from_dict(__magic_name__    ,  **__magic_name__      )





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			"""bridgetower_text_model"""
      def __init__(      self    ,  __magic_name__=5_0_2_6_5    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=1_2    ,  __magic_name__=1    ,  __magic_name__=3_0_7_2    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_4    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=1    ,  __magic_name__=0    ,  __magic_name__=2    ,  __magic_name__="absolute"    ,  __magic_name__=True    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )

       lowerCamelCase		:	int							=      vocab_size
       lowerCamelCase		:	int							=      hidden_size
       lowerCamelCase		:	Any							=      num_hidden_layers
       lowerCamelCase		:	Union[str, Any]							=      num_attention_heads
       lowerCamelCase		:	Tuple							=      hidden_act
       lowerCamelCase		:	Optional[int]							=      initializer_factor
       lowerCamelCase		:	Any							=      intermediate_size
       lowerCamelCase		:	List[str]							=      hidden_dropout_prob
       lowerCamelCase		:	Dict							=      attention_probs_dropout_prob
       lowerCamelCase		:	str							=      max_position_embeddings
       lowerCamelCase		:	Union[str, Any]							=      type_vocab_size
       lowerCamelCase		:	Optional[int]							=      layer_norm_eps
       lowerCamelCase		:	Optional[int]							=      position_embedding_type
       lowerCamelCase		:	List[str]							=      use_cache
       lowerCamelCase		:	List[str]							=      pad_token_id
       lowerCamelCase		:	List[str]							=      bos_token_id
       lowerCamelCase		:	Optional[int]							=      eos_token_id






      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  **__magic_name__      ):
       lowerCamelCase			,    lowerCamelCase		:	int							=      cls.get_config_dict(__magic_name__    ,  **__magic_name__      )

       if config_dict.get("""model_type"""      ) == "bridgetower":
        lowerCamelCase		:	Optional[int]							=      config_dict["""text_config"""]

       if "model_type" in config_dict and hasattr(cls    ,  """model_type"""      ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
            F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

       return cls.from_dict(__magic_name__    ,  **__magic_name__      )





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Dict           =			"""bridgetower"""
      def __init__(      self    ,  __magic_name__=True    ,  __magic_name__="gelu"    ,  __magic_name__=7_6_8    ,  __magic_name__=1    ,  __magic_name__=1e-05    ,  __magic_name__=False    ,  __magic_name__="add"    ,  __magic_name__=1_2    ,  __magic_name__=6    ,  __magic_name__=False    ,  __magic_name__=False    ,  __magic_name__=None    ,  __magic_name__=None    ,  **__magic_name__    ,  ):
       # TODO: remove this once the Hub files are updated.
       lowerCamelCase		:	int							=      kwargs.pop("""text_config_dict"""    ,  __magic_name__      )
       lowerCamelCase		:	str							=      kwargs.pop("""vision_config_dict"""    ,  __magic_name__      )

       super().__init__(**__magic_name__      )
       lowerCamelCase		:	str							=      share_cross_modal_transformer_layers
       lowerCamelCase		:	Union[str, Any]							=      hidden_act
       lowerCamelCase		:	str							=      hidden_size
       lowerCamelCase		:	Tuple							=      initializer_factor
       lowerCamelCase		:	List[str]							=      layer_norm_eps
       lowerCamelCase		:	int							=      share_link_tower_layers
       lowerCamelCase		:	List[Any]							=      link_tower_type
       lowerCamelCase		:	Tuple							=      num_attention_heads
       lowerCamelCase		:	int							=      num_hidden_layers
       lowerCamelCase		:	Union[str, Any]							=      tie_word_embeddings
       lowerCamelCase		:	Tuple							=      init_layernorm_from_vision_encoder

       if text_config is None:
        lowerCamelCase		:	Any							=      {}
        logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""      )

       if vision_config is None:
        lowerCamelCase		:	int							=      {}
        logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."""      )

       lowerCamelCase		:	Any							=      BridgeTowerTextConfig(**__magic_name__      )
       lowerCamelCase		:	Optional[Any]							=      BridgeTowerVisionConfig(**__magic_name__      )
      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__    ,  __magic_name__    ,  **__magic_name__      ):
       return cls(text_config=text_config.to_dict()    ,  vision_config=vision_config.to_dict()    ,  **__magic_name__      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      copy.deepcopy(self.__dict__      )
       lowerCamelCase		:	int							=      self.text_config.to_dict()
       lowerCamelCase		:	Dict							=      self.vision_config.to_dict()
       lowerCamelCase		:	List[str]							=      self.__class__.model_type
       return output




