"""simple docstring"""
import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)

SCREAMING_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 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[Any]     					=  """bridgetower_vision_model"""

				def __init__(			self						,      lowerCAmelCase=7_68						,      lowerCAmelCase=12						,      lowerCAmelCase=3						,      lowerCAmelCase=16						,      lowerCAmelCase=2_88						,      lowerCAmelCase=1						,      lowerCAmelCase=1E-05						,      lowerCAmelCase=False						,      lowerCAmelCase=True						,      lowerCAmelCase=False						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )
										snake_case        =     hidden_size
										snake_case        =     num_hidden_layers
										snake_case        =     num_channels
										snake_case        =     patch_size
										snake_case        =     image_size
										snake_case        =     initializer_factor
										snake_case        =     layer_norm_eps
										snake_case        =     stop_gradient
										snake_case        =     share_layernorm
										snake_case        =     remove_last_layer

				@classmethod
				def 						snake_case	(			cls						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     cls.get_config_dict(lowerCAmelCase						,      **lowerCAmelCase      )

										if config_dict.get('model_type'      ) == "bridgetower":
																snake_case        =     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 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Union[str, Any]     					=  """bridgetower_text_model"""

				def __init__(			self						,      lowerCAmelCase=5_02_65						,      lowerCAmelCase=7_68						,      lowerCAmelCase=12						,      lowerCAmelCase=12						,      lowerCAmelCase=1						,      lowerCAmelCase=30_72						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=0.1						,      lowerCAmelCase=0.1						,      lowerCAmelCase=5_14						,      lowerCAmelCase=1						,      lowerCAmelCase=1E-05						,      lowerCAmelCase=1						,      lowerCAmelCase=0						,      lowerCAmelCase=2						,      lowerCAmelCase="absolute"						,      lowerCAmelCase=True						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )

										snake_case        =     vocab_size
										snake_case        =     hidden_size
										snake_case        =     num_hidden_layers
										snake_case        =     num_attention_heads
										snake_case        =     hidden_act
										snake_case        =     initializer_factor
										snake_case        =     intermediate_size
										snake_case        =     hidden_dropout_prob
										snake_case        =     attention_probs_dropout_prob
										snake_case        =     max_position_embeddings
										snake_case        =     type_vocab_size
										snake_case        =     layer_norm_eps
										snake_case        =     position_embedding_type
										snake_case        =     use_cache
										snake_case        =     pad_token_id
										snake_case        =     bos_token_id
										snake_case        =     eos_token_id

				@classmethod
				def 						snake_case	(			cls						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     cls.get_config_dict(lowerCAmelCase						,      **lowerCAmelCase      )

										if config_dict.get('model_type'      ) == "bridgetower":
																snake_case        =     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 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      str     					=  """bridgetower"""

				def __init__(			self						,      lowerCAmelCase=True						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=7_68						,      lowerCAmelCase=1						,      lowerCAmelCase=1E-05						,      lowerCAmelCase=False						,      lowerCAmelCase="add"						,      lowerCAmelCase=12						,      lowerCAmelCase=6						,      lowerCAmelCase=False						,      lowerCAmelCase=False						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     kwargs.pop('text_config_dict'						,      lowerCAmelCase      )
										snake_case        =     kwargs.pop('vision_config_dict'						,      lowerCAmelCase      )

										super().__init__(**lowerCAmelCase      )
										snake_case        =     share_cross_modal_transformer_layers
										snake_case        =     hidden_act
										snake_case        =     hidden_size
										snake_case        =     initializer_factor
										snake_case        =     layer_norm_eps
										snake_case        =     share_link_tower_layers
										snake_case        =     link_tower_type
										snake_case        =     num_attention_heads
										snake_case        =     num_hidden_layers
										snake_case        =     tie_word_embeddings
										snake_case        =     init_layernorm_from_vision_encoder

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

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

										snake_case        =     BridgeTowerTextConfig(**lowerCAmelCase      )
										snake_case        =     BridgeTowerVisionConfig(**lowerCAmelCase      )

				@classmethod
				def 						snake_case	(			cls						,      lowerCAmelCase						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

										return cls(text_config=text_config.to_dict()						,      vision_config=vision_config.to_dict()						,      **lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     copy.deepcopy(self.__dict__      )
										snake_case        =     self.text_config.to_dict()
										snake_case        =     self.vision_config.to_dict()
										snake_case        =     self.__class__.model_type
										return output



"""simple docstring"""
import os
import sys
import unittest


SCREAMING_SNAKE_CASE__   =					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
SCREAMING_SNAKE_CASE__   =					os.path.join(git_repo_path, "src", "transformers")

SCREAMING_SNAKE_CASE__   =					"\n{0} = None\n"

SCREAMING_SNAKE_CASE__   =					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


SCREAMING_SNAKE_CASE__   =					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"



class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'      )
										self.assertIsNone(lowerCAmelCase      )

										snake_case        =     find_backend('    if not is_tokenizers_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tokenizers'      )

										snake_case        =     find_backend('    if not is_tensorflow_text_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tensorflow_text'      )

										snake_case        =     find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tensorflow_text'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers_and_vision'      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn('torch'						,      lowerCAmelCase      )
										self.assertIn('tensorflow_text'						,      lowerCAmelCase      )
										self.assertIn('sentencepiece_and_tokenizers'						,      lowerCAmelCase      )

										# Likewise, we can't assert on the exact content of a key
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertModel'						,      objects['tf']      )
										self.assertIn('FlaxBertModel'						,      objects['flax']      )
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertTokenizer'						,      objects['tensorflow_text']      )
										self.assertIn('convert_slow_tokenizer'						,      objects['sentencepiece_and_tokenizers']      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     create_dummy_object('CONSTANT'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      '\nCONSTANT = None\n'      )

										snake_case        =     create_dummy_object('function'						,      '\'torch\''      )
										self.assertEqual(
										    lowerCAmelCase						,      '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'      )

										snake_case        =     '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
										snake_case        =     create_dummy_object('FakeClass'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
										snake_case        =     create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}      )
										self.assertEqual(dummy_files['torch']						,      lowerCAmelCase      )


