






"""simple docstring"""




import copy
import os
from typing import Union

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


_snake_case        =  logging.get_logger(__name__)

_snake_case        =  {
    '''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 			UpperCamelCase     (      snake_case_ ):
 UpperCamelCase   :			int     =   '''bridgetower_vision_model'''


 def __init__(	self						:		List[str]				,		UpperCAmelCase__						:		Tuple=768				,		UpperCAmelCase__						:		int=12				,		UpperCAmelCase__						:		Tuple=3				,		UpperCAmelCase__						:		Any=16				,		UpperCAmelCase__						:		str=288				,		UpperCAmelCase__						:		int=1				,		UpperCAmelCase__						:		Union[str, Any]=1E-05				,		UpperCAmelCase__						:		Tuple=False				,		UpperCAmelCase__						:		Optional[Any]=True				,		UpperCAmelCase__						:		Optional[Any]=False				,		**UpperCAmelCase__						:		Any				,		)   ->			Tuple:
   super().__init__(**__lowerCAmelCase				)
   _a    :			Union[str, Any]			 =      hidden_size
   _a    :			Any			 =      num_hidden_layers
   _a    :			List[Any]			 =      num_channels
   _a    :			List[str]			 =      patch_size
   _a    :			Optional[int]			 =      image_size
   _a    :			int			 =      initializer_factor
   _a    :			Optional[int]			 =      layer_norm_eps
   _a    :			int			 =      stop_gradient
   _a    :			str			 =      share_layernorm
   _a    :			Tuple			 =      remove_last_layer







 @classmethod
 def _lowercase      (	cls						:		Optional[int]				,		UpperCAmelCase__						:		Union[str, os.PathLike]				,		**UpperCAmelCase__						:		int				)   ->			"PretrainedConfig":
   _a ,    _a    :			int			 =      cls.get_config_dict(__lowerCAmelCase				,		**__lowerCAmelCase				)

   if config_dict.get("""model_type"""				) == "bridgetower":
     _a    :			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(__lowerCAmelCase				,		**__lowerCAmelCase				)







class 			UpperCamelCase     (      snake_case_ ):
 UpperCamelCase   :			List[Any]     =   '''bridgetower_text_model'''


 def __init__(	self						:		Union[str, Any]				,		UpperCAmelCase__						:		Any=50265				,		UpperCAmelCase__						:		Union[str, Any]=768				,		UpperCAmelCase__						:		List[Any]=12				,		UpperCAmelCase__						:		int=12				,		UpperCAmelCase__						:		str=1				,		UpperCAmelCase__						:		int=3072				,		UpperCAmelCase__						:		List[Any]="gelu"				,		UpperCAmelCase__						:		Any=0.1				,		UpperCAmelCase__						:		Dict=0.1				,		UpperCAmelCase__						:		Tuple=514				,		UpperCAmelCase__						:		Optional[Any]=1				,		UpperCAmelCase__						:		Optional[Any]=1E-05				,		UpperCAmelCase__						:		Optional[Any]=1				,		UpperCAmelCase__						:		Optional[int]=0				,		UpperCAmelCase__						:		Dict=2				,		UpperCAmelCase__						:		List[Any]="absolute"				,		UpperCAmelCase__						:		Optional[Any]=True				,		**UpperCAmelCase__						:		Union[str, Any]				,		)   ->			Optional[int]:
   super().__init__(**__lowerCAmelCase				)

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







 @classmethod
 def _lowercase      (	cls						:		List[Any]				,		UpperCAmelCase__						:		Union[str, os.PathLike]				,		**UpperCAmelCase__						:		str				)   ->			"PretrainedConfig":
   _a ,    _a    :			Dict			 =      cls.get_config_dict(__lowerCAmelCase				,		**__lowerCAmelCase				)

   if config_dict.get("""model_type"""				) == "bridgetower":
     _a    :			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(__lowerCAmelCase				,		**__lowerCAmelCase				)







class 			UpperCamelCase     (      snake_case_ ):
 UpperCamelCase   :			Union[str, Any]     =   '''bridgetower'''


 def __init__(	self						:		Optional[int]				,		UpperCAmelCase__						:		Dict=True				,		UpperCAmelCase__						:		Dict="gelu"				,		UpperCAmelCase__						:		Optional[Any]=768				,		UpperCAmelCase__						:		List[Any]=1				,		UpperCAmelCase__						:		Tuple=1E-05				,		UpperCAmelCase__						:		Union[str, Any]=False				,		UpperCAmelCase__						:		Any="add"				,		UpperCAmelCase__						:		Dict=12				,		UpperCAmelCase__						:		List[str]=6				,		UpperCAmelCase__						:		Tuple=False				,		UpperCAmelCase__						:		Optional[Any]=False				,		UpperCAmelCase__						:		List[str]=None				,		UpperCAmelCase__						:		List[str]=None				,		**UpperCAmelCase__						:		Optional[int]				,		)   ->			List[str]:
   _a    :			int			 =      kwargs.pop("""text_config_dict"""				,		__lowerCAmelCase				)
   _a    :			Any			 =      kwargs.pop("""vision_config_dict"""				,		__lowerCAmelCase				)

   super().__init__(**__lowerCAmelCase				)
   _a    :			List[Any]			 =      share_cross_modal_transformer_layers
   _a    :			Optional[int]			 =      hidden_act
   _a    :			str			 =      hidden_size
   _a    :			Dict			 =      initializer_factor
   _a    :			Optional[int]			 =      layer_norm_eps
   _a    :			List[str]			 =      share_link_tower_layers
   _a    :			List[str]			 =      link_tower_type
   _a    :			Optional[Any]			 =      num_attention_heads
   _a    :			str			 =      num_hidden_layers
   _a    :			Any			 =      tie_word_embeddings
   _a    :			Optional[int]			 =      init_layernorm_from_vision_encoder

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

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

   _a    :			Optional[Any]			 =      BridgeTowerTextConfig(**__lowerCAmelCase				)
   _a    :			str			 =      BridgeTowerVisionConfig(**__lowerCAmelCase				)


 @classmethod
 def _lowercase      (	cls						:		Optional[Any]				,		UpperCAmelCase__						:		BridgeTowerTextConfig				,		UpperCAmelCase__						:		BridgeTowerVisionConfig				,		**UpperCAmelCase__						:		Dict				)   ->			List[Any]:
   return cls(text_config=text_config.to_dict()				,		vision_config=vision_config.to_dict()				,		**__lowerCAmelCase				)







 def _lowercase      (	self						:		List[str]				)   ->			Optional[int]:
   _a    :			Tuple			 =      copy.deepcopy(self.__dict__				)
   _a    :			str			 =      self.text_config.to_dict()
   _a    :			int			 =      self.vision_config.to_dict()
   _a    :			int			 =      self.__class__.model_type
   return output








import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import CLIPImageProcessor, CLIPProcessor


@require_vision
class     A     (unittest.TestCase       ):
			'''simple docstring'''




			def   a_			(     self  :       Optional[int]  )      ->      Dict:







					"""simple docstring"""






					A__     =  tempfile.mkdtemp()

					# fmt: off
					A__     =  ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
					# fmt: on
					A__     =  dict(zip(__lowerCAmelCase				,      range(len(__lowerCAmelCase  )  )  )  )
					A__     =  ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
					A__     =  {"""unk_token""": """<unk>"""}

					A__     =  os.path.join(self.tmpdirname				,      VOCAB_FILES_NAMES["""vocab_file"""]  )
					A__     =  os.path.join(self.tmpdirname				,      VOCAB_FILES_NAMES["""merges_file"""]  )
					with open(self.vocab_file				,      """w"""				,      encoding="""utf-8"""  ) as fp:
							fp.write(json.dumps(__lowerCAmelCase  ) + """\n"""  )
					with open(self.merges_file				,      """w"""				,      encoding="""utf-8"""  ) as fp:
							fp.write("""\n""".join(__lowerCAmelCase  )  )

					A__     =  {
					    """do_resize""": True,
					    """size""": 20,
					    """do_center_crop""": True,
					    """crop_size""": 18,
					    """do_normalize""": True,
					    """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
					    """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
					}
					A__     =  os.path.join(self.tmpdirname				,      __lowerCAmelCase  )
					with open(self.image_processor_file				,      """w"""				,      encoding="""utf-8"""  ) as fp:
							json.dump(__lowerCAmelCase				,      __lowerCAmelCase  )






			def   a_			(     self  :       str				,      **__lowerCAmelCase  :       Optional[int]  )      ->      str:







					"""simple docstring"""






					return CLIPTokenizer.from_pretrained(self.tmpdirname				,      **__lowerCAmelCase  )






			def   a_			(     self  :       Dict				,      **__lowerCAmelCase  :       List[Any]  )      ->      Optional[int]:







					"""simple docstring"""






					return CLIPTokenizerFast.from_pretrained(self.tmpdirname				,      **__lowerCAmelCase  )






			def   a_			(     self  :       List[Any]				,      **__lowerCAmelCase  :       List[Any]  )      ->      Tuple:







					"""simple docstring"""






					return CLIPImageProcessor.from_pretrained(self.tmpdirname				,      **__lowerCAmelCase  )






			def   a_			(     self  :       Optional[Any]  )      ->      str:







					"""simple docstring"""






					shutil.rmtree(self.tmpdirname  )






			def   a_			(     self  :       int  )      ->      Tuple:







					"""simple docstring"""






					A__     =  [np.random.randint(2_55				,      size=(3, 30, 4_00)				,      dtype=np.uinta  )]

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

					return image_inputs






			def   a_			(     self  :       List[str]  )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  self.get_tokenizer()
					A__     =  self.get_rust_tokenizer()
					A__     =  self.get_image_processor()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )
					processor_slow.save_pretrained(self.tmpdirname  )
					A__     =  CLIPProcessor.from_pretrained(self.tmpdirname				,      use_fast=__lowerCAmelCase  )

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )
					processor_fast.save_pretrained(self.tmpdirname  )
					A__     =  CLIPProcessor.from_pretrained(self.tmpdirname  )

					self.assertEqual(processor_slow.tokenizer.get_vocab()				,      tokenizer_slow.get_vocab()  )
					self.assertEqual(processor_fast.tokenizer.get_vocab()				,      tokenizer_fast.get_vocab()  )
					self.assertEqual(tokenizer_slow.get_vocab()				,      tokenizer_fast.get_vocab()  )
					self.assertIsInstance(processor_slow.tokenizer				,      __lowerCAmelCase  )
					self.assertIsInstance(processor_fast.tokenizer				,      __lowerCAmelCase  )

					self.assertEqual(processor_slow.image_processor.to_json_string()				,      image_processor.to_json_string()  )
					self.assertEqual(processor_fast.image_processor.to_json_string()				,      image_processor.to_json_string()  )
					self.assertIsInstance(processor_slow.image_processor				,      __lowerCAmelCase  )
					self.assertIsInstance(processor_fast.image_processor				,      __lowerCAmelCase  )






			def   a_			(     self  :       int  )      ->      Any:







					"""simple docstring"""






					A__     =  CLIPProcessor(tokenizer=self.get_tokenizer()				,      image_processor=self.get_image_processor()  )
					processor.save_pretrained(self.tmpdirname  )

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

					A__     =  CLIPProcessor.from_pretrained(
					    self.tmpdirname				,      bos_token="""(BOS)"""				,      eos_token="""(EOS)"""				,      do_normalize=__lowerCAmelCase				,      padding_value=1.0  )

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

					self.assertEqual(processor.image_processor.to_json_string()				,      image_processor_add_kwargs.to_json_string()  )
					self.assertIsInstance(processor.image_processor				,      __lowerCAmelCase  )






			def   a_			(     self  :       List[Any]  )      ->      Union[str, Any]:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

					A__     =  self.prepare_image_inputs()

					A__     =  image_processor(__lowerCAmelCase				,      return_tensors="""np"""  )
					A__     =  processor(images=__lowerCAmelCase				,      return_tensors="""np"""  )

					for key in input_image_proc.keys():
							self.assertAlmostEqual(input_image_proc[key].sum()				,      input_processor[key].sum()				,      delta=1e-2  )






			def   a_			(     self  :       Union[str, Any]  )      ->      Tuple:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

					A__     =  """lower newer"""

					A__     =  processor(text=__lowerCAmelCase  )

					A__     =  tokenizer(__lowerCAmelCase  )

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






			def   a_			(     self  :       str  )      ->      Optional[Any]:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

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

					A__     =  processor(text=__lowerCAmelCase				,      images=__lowerCAmelCase  )

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

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






			def   a_			(     self  :       Optional[int]  )      ->      Tuple:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

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

					A__     =  processor.batch_decode(__lowerCAmelCase  )
					A__     =  tokenizer.batch_decode(__lowerCAmelCase  )

					self.assertListEqual(__lowerCAmelCase				,      __lowerCAmelCase  )







			def   a_			(     self  :       Dict  )      ->      str:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

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

					A__     =  processor(text=__lowerCAmelCase				,      images=__lowerCAmelCase  )

					self.assertListEqual(list(inputs.keys()  )				,      processor.model_input_names  )



