
"""simple docstring"""





import copy
import os
from typing import Union

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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  int						   =		'''bridgetower_vision_model'''




		def __init__(     self							,  lowerCAmelCase__=7_6_8							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=3							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=2_8_8							,  lowerCAmelCase__=1							,  lowerCAmelCase__=1E-05							,  lowerCAmelCase__=False							,  lowerCAmelCase__=True							,  lowerCAmelCase__=False							,  **lowerCAmelCase__							,  ):
				super().__init__(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = hidden_size
				__SCREAMING_SNAKE_CASE            = num_hidden_layers
				__SCREAMING_SNAKE_CASE            = num_channels
				__SCREAMING_SNAKE_CASE            = patch_size
				__SCREAMING_SNAKE_CASE            = image_size
				__SCREAMING_SNAKE_CASE            = initializer_factor
				__SCREAMING_SNAKE_CASE            = layer_norm_eps
				__SCREAMING_SNAKE_CASE            = stop_gradient
				__SCREAMING_SNAKE_CASE            = share_layernorm
				__SCREAMING_SNAKE_CASE            = remove_last_layer




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = cls.get_config_dict(lowerCAmelCase__							,  **lowerCAmelCase__)

				if config_dict.get("""model_type""") == "bridgetower":
						__SCREAMING_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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  int						   =		'''bridgetower_text_model'''




		def __init__(     self							,  lowerCAmelCase__=5_0_2_6_5							,  lowerCAmelCase__=7_6_8							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=1							,  lowerCAmelCase__=3_0_7_2							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=5_1_4							,  lowerCAmelCase__=1							,  lowerCAmelCase__=1E-05							,  lowerCAmelCase__=1							,  lowerCAmelCase__=0							,  lowerCAmelCase__=2							,  lowerCAmelCase__="absolute"							,  lowerCAmelCase__=True							,  **lowerCAmelCase__							,  ):
				super().__init__(**lowerCAmelCase__)

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




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = cls.get_config_dict(lowerCAmelCase__							,  **lowerCAmelCase__)

				if config_dict.get("""model_type""") == "bridgetower":
						__SCREAMING_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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  List[str]						   =		'''bridgetower'''




		def __init__(     self							,  lowerCAmelCase__=True							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=7_6_8							,  lowerCAmelCase__=1							,  lowerCAmelCase__=1E-05							,  lowerCAmelCase__=False							,  lowerCAmelCase__="add"							,  lowerCAmelCase__=1_2							,  lowerCAmelCase__=6							,  lowerCAmelCase__=False							,  lowerCAmelCase__=False							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  **lowerCAmelCase__							,  ):
				# TODO: remove this once the Hub files are updated.
				__SCREAMING_SNAKE_CASE            = kwargs.pop("""text_config_dict"""							,  lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = kwargs.pop("""vision_config_dict"""							,  lowerCAmelCase__)

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

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

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

				__SCREAMING_SNAKE_CASE            = BridgeTowerTextConfig(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = BridgeTowerVisionConfig(**lowerCAmelCase__)




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				return cls(text_config=text_config.to_dict()							,  vision_config=vision_config.to_dict()							,  **lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = copy.deepcopy(self.__dict__)
				__SCREAMING_SNAKE_CASE            = self.text_config.to_dict()
				__SCREAMING_SNAKE_CASE            = self.vision_config.to_dict()
				__SCREAMING_SNAKE_CASE            = self.__class__.model_type
				return output












"""simple docstring"""






from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


UpperCAmelCase_   : Optional[int]   			= logging.get_logger(__name__)



def 					_A			(__a    ,				__a							) ->       Tuple:
    """simple docstring"""






    try:
        with open(__a    ,				'''rb'''							) as flax_state_f:
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		from_bytes(__a    ,				flax_state_f.read()							)
    except UnpicklingError as e:
        try:
            with open(__a							) as f:
                if f.read().startswith('''version'''							):
                    raise OSError(
                        '''You seem to have cloned a repository without having git-lfs installed. Please'''
                        ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
                        ''' folder you cloned.'''							)
                else:
                    raise ValueError from e
        except (UnicodeDecodeError, ValueError):
            raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. '							)

    return load_flax_weights_in_pytorch_model(__a    ,				__a							)





def 					_A			(__a    ,				__a							) ->       Tuple:
    """simple docstring"""






    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
            ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
            ''' instructions.'''							)
        raise

    # check if we have bf16 weights
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		flatten_dict(jax.tree_util.tree_map(lambda __a							: x.dtype == jnp.bfloataa    ,				__a							)							).values()
    if any(__a							):
        # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

        # and bf16 is not fully supported in PT yet.
        logger.warning(
            '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
            '''before loading those in PyTorch model.'''							)
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		jax.tree_util.tree_map(
            lambda __a							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params    ,				__a							)

    SCREAMING_SNAKE_CASE_      :  int    						=		''''''

    SCREAMING_SNAKE_CASE_      :  str    						=		flatten_dict(__a    ,				sep='''.'''							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		pt_model.state_dict()

    # keep track of unexpected & missing keys
    SCREAMING_SNAKE_CASE_      :  str    						=		[]
    SCREAMING_SNAKE_CASE_      :  Any    						=		set(pt_model_dict.keys()							)

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        SCREAMING_SNAKE_CASE_      :  Any    						=		flax_key_tuple.split('''.'''							)

        if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
            SCREAMING_SNAKE_CASE_      :  Any    						=		flax_key_tuple_array[:-1] + ['''weight''']
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		jnp.transpose(__a    ,				(3, 2, 0, 1)							)
        elif flax_key_tuple_array[-1] == "kernel":
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		flax_key_tuple_array[:-1] + ['''weight''']
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		flax_tensor.T
        elif flax_key_tuple_array[-1] == "scale":
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		flax_key_tuple_array[:-1] + ['''weight''']

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(__a							):
                SCREAMING_SNAKE_CASE_      :  List[str]    						=		(
                    flax_key_tuple_string.replace('''_0'''    ,				'''.0'''							)
                    .replace('''_1'''    ,				'''.1'''							)
                    .replace('''_2'''    ,				'''.2'''							)
                    .replace('''_3'''    ,				'''.3'''							)
                    .replace('''_4'''    ,				'''.4'''							)
                    .replace('''_5'''    ,				'''.5'''							)
                    .replace('''_6'''    ,				'''.6'''							)
                    .replace('''_7'''    ,				'''.7'''							)
                    .replace('''_8'''    ,				'''.8'''							)
                    .replace('''_9'''    ,				'''.9'''							)
                )

        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		'''.'''.join(__a							)

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
                    f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'							)
            else:
                # add weight to pytorch dict
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		np.asarray(__a							) if not isinstance(__a    ,				np.ndarray							) else flax_tensor
                SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.from_numpy(__a							)
                # remove from missing keys
                missing_keys.remove(__a							)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(__a							)

    pt_model.load_state_dict(__a							)

    # re-transform missing_keys to list
    SCREAMING_SNAKE_CASE_      :  int    						=		list(__a							)

    if len(__a							) > 0:
        logger.warning(
            '''Some weights of the Flax model were not used when initializing the PyTorch model'''
            f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
            f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
            ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
            f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
            ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
            ''' FlaxBertForSequenceClassification model).'''							)
    if len(__a							) > 0:
        logger.warning(
            f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
            f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
            ''' use it for predictions and inference.'''							)

    return pt_model


