



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

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


__a      =				logging.get_logger(__name__)

__a      =				{
    "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'''





					_A		:			List[str]						=     """bridgetower_vision_model"""






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






					@classmethod
					def    lowerCAmelCase_				(       cls:							Optional[int]						,     snake_case:							Union[str, os.PathLike]						,     **snake_case:							int						)   ->	"PretrainedConfig":
											snake_case_,       snake_case_ :str						=			cls.get_config_dict(snake_case						,     **snake_case						)

											if config_dict.get("""model_type"""						) == "bridgetower":
																	snake_case_ :Optional[Any]						=			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(snake_case						,     **snake_case						)

class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			Optional[Any]						=     """bridgetower_text_model"""






					def __init__(       self:							List[str]						,     snake_case:							Optional[int]=50_265						,     snake_case:							str=768						,     snake_case:							Optional[int]=12						,     snake_case:							Optional[Any]=12						,     snake_case:							Tuple=1						,     snake_case:							Dict=3_072						,     snake_case:							str="gelu"						,     snake_case:							List[Any]=0.1						,     snake_case:							Dict=0.1						,     snake_case:							Union[str, Any]=514						,     snake_case:							Optional[Any]=1						,     snake_case:							Dict=1E-05						,     snake_case:							Optional[int]=1						,     snake_case:							Tuple=0						,     snake_case:							Tuple=2						,     snake_case:							Optional[Any]="absolute"						,     snake_case:							List[Any]=True						,     **snake_case:							Union[str, Any]						,     )   ->	List[Any]:
											super().__init__(**snake_case						)

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






					@classmethod
					def    lowerCAmelCase_				(       cls:							int						,     snake_case:							Union[str, os.PathLike]						,     **snake_case:							Optional[Any]						)   ->	"PretrainedConfig":
											snake_case_,       snake_case_ :Union[str, Any]						=			cls.get_config_dict(snake_case						,     **snake_case						)

											if config_dict.get("""model_type"""						) == "bridgetower":
																	snake_case_ :List[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(snake_case						,     **snake_case						)

class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			int						=     """bridgetower"""






					def __init__(       self:							int						,     snake_case:							Dict=True						,     snake_case:							Tuple="gelu"						,     snake_case:							str=768						,     snake_case:							Tuple=1						,     snake_case:							Any=1E-05						,     snake_case:							int=False						,     snake_case:							Optional[Any]="add"						,     snake_case:							str=12						,     snake_case:							Any=6						,     snake_case:							Any=False						,     snake_case:							List[str]=False						,     snake_case:							str=None						,     snake_case:							List[Any]=None						,     **snake_case:							Tuple						,     )   ->	int:
											# TODO: remove this once the Hub files are updated.
											snake_case_ :Optional[Any]						=			kwargs.pop("""text_config_dict"""						,     snake_case						)
											snake_case_ :List[Any]						=			kwargs.pop("""vision_config_dict"""						,     snake_case						)

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

											if text_config is None:
																	snake_case_ :Union[str, Any]						=			{}
																	logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."""						)

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

											snake_case_ :Optional[int]						=			BridgeTowerTextConfig(**snake_case						)
											snake_case_ :List[str]						=			BridgeTowerVisionConfig(**snake_case						)






					@classmethod
					def    lowerCAmelCase_				(       cls:							List[str]						,     snake_case:							BridgeTowerTextConfig						,     snake_case:							BridgeTowerVisionConfig						,     **snake_case:							str						)   ->	Dict:
											return cls(text_config=text_config.to_dict()						,     vision_config=vision_config.to_dict()						,     **snake_case						)






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	int:
											snake_case_ :Tuple						=			copy.deepcopy(self.__dict__						)
											snake_case_ :Union[str, Any]						=			self.text_config.to_dict()
											snake_case_ :Union[str, Any]						=			self.vision_config.to_dict()
											snake_case_ :List[Any]						=			self.__class__.model_type
											return output





"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			os.path.join(args.tf_model_dir,					"""parameters.json"""	)
						snake_case_ :Any						=			json.loads(open(_lowercase	).read()	)
						if not params:
												raise ValueError(
												    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""	)
						if not args.output.endswith(""".pt"""	):
												snake_case_ :Optional[int]						=			args.output + """.pt"""
						snake_case_ :List[str]						=			OrderedDict()
						with tf.device("""/CPU:0"""	):
												snake_case_ :Dict						=			tf.train.load_checkpoint(args.tf_model_dir	)
												snake_case_ :str						=			reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		snake_case_ :List[Any]						=			reader.get_tensor(_lowercase	).astype(np.floataa	)
																		if key_name.endswith("""/adam_m"""	) or key_name.endswith("""/adam_v"""	):
																								continue
																		if key_name.startswith("""pasts/"""	):
																								if key_name.startswith("""pasts/mlp"""	):
																														snake_case_ :Any						=			int(key_name[9]	)
																								elif key_name.startswith("""pasts/out"""	):
																														snake_case_ :Optional[int]						=			8
																								snake_case_ :List[str]						=			"""model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								snake_case_ :Optional[Any]						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																								snake_case_ :List[str]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/moe"""	):
																								snake_case_ :Tuple						=			int(key_name[9:].split("""/"""	)[0]	)
																								if key_name.endswith("""/switch_gating/kernel"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
																														snake_case_ :Optional[Any]						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/softmlp/kernel"""	):
																														snake_case_ :List[Any]						=			"""model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
																														snake_case_ :Optional[int]						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/wo/kernel"""	) or key_name.endswith("""/wi/kernel"""	):
																														snake_case_ :Dict						=			key_name[-9:-7]
																														for i in range(16	):
																																				snake_case_ :str						=			"""model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
																																				snake_case_ :Tuple						=			(
																																				    vnp[i].transpose([1, 0]	).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				snake_case_ :Optional[int]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/mlp"""	):
																								snake_case_ :Optional[int]						=			int(key_name[9:].split("""/"""	)[0]	)
																								if key_name.endswith("""/p1/kernel"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.feed_forward.mlp.wi.weight""" % player
																														snake_case_ :Dict						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/p1/bias"""	):
																														snake_case_ :List[Any]						=			"""model.blocks.%d.feed_forward.mlp.wi.bias""" % player
																														snake_case_ :str						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/p2/kernel"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.feed_forward.mlp.wo.weight""" % player
																														snake_case_ :Dict						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Tuple						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/p2/bias"""	):
																														snake_case_ :Dict						=			"""model.blocks.%d.feed_forward.mlp.wo.bias""" % player
																														snake_case_ :Any						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Optional[int]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/ln"""	):
																								snake_case_ :Union[str, Any]						=			int(key_name[8:].split("""/"""	)[0]	)
																								if key_name.endswith("""/b"""	):
																														snake_case_ :str						=			"""model.blocks.%d.feed_forward.norm.bias""" % player
																														snake_case_ :Dict						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :int						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/g"""	):
																														snake_case_ :Dict						=			"""model.blocks.%d.feed_forward.norm.weight""" % player
																														snake_case_ :Dict						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Tuple						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/att"""	):
																								snake_case_ :List[str]						=			int(key_name[9:].split("""/"""	)[0]	)
																								if key_name.endswith("""/qkv/kernel"""	):
																														snake_case_ :Optional[int]						=			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														snake_case_ :Dict						=			state[:, 0, :, :]
																														snake_case_ :int						=			state[:, 1, :, :]
																														snake_case_ :List[str]						=			state[:, 2, :, :]
																														snake_case_ :str						=			(
																														    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Any						=			(
																														    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[int]						=			(
																														    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :int						=			"""model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
																														snake_case_ :int						=			torch.tensor(_lowercase	)
																														snake_case_ :Optional[Any]						=			"""model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
																														snake_case_ :Dict						=			torch.tensor(_lowercase	)
																														snake_case_ :Dict						=			"""model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/o/kernel"""	):
																														snake_case_ :str						=			"""model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
																														snake_case_ :str						=			(
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]	).transpose([1, 0]	).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Any						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/an"""	):
																								snake_case_ :Optional[int]						=			int(key_name[8:].split("""/"""	)[0]	)
																								if key_name.endswith("""/b"""	):
																														snake_case_ :Any						=			"""model.blocks.%d.self_attn.norm.bias""" % player
																														snake_case_ :Optional[int]						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Tuple						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/g"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.self_attn.norm.weight""" % player
																														snake_case_ :Dict						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Optional[int]						=			torch.tensor(_lowercase	)
																		elif (
																		    key_name.startswith("""model/wte"""	)
																		    or key_name.startswith("""model/wpe"""	)
																		    or key_name.startswith("""model/ete"""	)
																		):
																								snake_case_ :List[Any]						=			{"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
																								    key_name[-3:]
																								]
																								snake_case_ :Optional[Any]						=			"""model.%s.weight""" % nlayer
																								snake_case_ :Any						=			vnp.copy()  # same in embedded
																								snake_case_ :List[Any]						=			torch.tensor(_lowercase	)
																								if key_name.startswith("""model/wte"""	):
																														snake_case_ :Tuple						=			"""lm_head.weight"""
																														snake_case_ :List[str]						=			vnp.copy()  # same in embedded
																														snake_case_ :List[Any]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/wob"""	):
																								snake_case_ :str						=			"""final_logits_bias"""
																								snake_case_ :Any						=			vnp.copy()  # same in embedded
																								snake_case_ :List[Any]						=			state.reshape((1, -1)	)
																								snake_case_ :Union[str, Any]						=			torch.tensor(_lowercase	)
																		elif key_name == "model/dense/kernel":
																								snake_case_ :str						=			"""model.last_project.weight"""
																								snake_case_ :Dict						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																								snake_case_ :int						=			torch.tensor(_lowercase	)
																		elif key_name == "model/dense_1/bias":
																								snake_case_ :Optional[int]						=			"""model.last_project.bias"""
																								snake_case_ :Tuple						=			vnp.copy()  # same because it is one dimensional
																								snake_case_ :Any						=			torch.tensor(_lowercase	)
						torch.save(_lowercase,					args.output	)


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser(
						    description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
						)
						parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
						parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
						__a      =				parser.parse_args()
						convert_tf_gptsan_to_pt(args)
