


import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
			pass

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


_snake_case	:						str         =							logging.get_logger(__name__)

_snake_case	:						Tuple         =							{
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[int]	    =    "align_text_model"

							def __init__(				self			:    Tuple       ,      lowerCamelCase			:    Optional[Any]=30522       ,      lowerCamelCase			:    List[Any]=768       ,      lowerCamelCase			:    Any=12       ,      lowerCamelCase			:    Tuple=12       ,      lowerCamelCase			:    Dict=3072       ,      lowerCamelCase			:    Tuple="gelu"       ,      lowerCamelCase			:    Optional[Any]=0.1       ,      lowerCamelCase			:    Union[str, Any]=0.1       ,      lowerCamelCase			:    Any=512       ,      lowerCamelCase			:    int=2       ,      lowerCamelCase			:    Union[str, Any]=0.02       ,      lowerCamelCase			:    Any=1E-12       ,      lowerCamelCase			:    Union[str, Any]=0       ,      lowerCamelCase			:    Optional[int]="absolute"       ,      lowerCamelCase			:    List[str]=True       ,      **lowerCamelCase			:    Dict       ,      )      ->       Optional[int]:
														super().__init__(**lowerCamelCase							)

														__snake_case						:     Optional[Any]									=  vocab_size
														__snake_case						:     List[str]									=  hidden_size
														__snake_case						:     Any									=  num_hidden_layers
														__snake_case						:     Optional[Any]									=  num_attention_heads
														__snake_case						:     List[Any]									=  hidden_act
														__snake_case						:     List[Any]									=  intermediate_size
														__snake_case						:     int									=  hidden_dropout_prob
														__snake_case						:     Union[str, Any]									=  attention_probs_dropout_prob
														__snake_case						:     Tuple									=  max_position_embeddings
														__snake_case						:     List[Any]									=  type_vocab_size
														__snake_case						:     List[Any]									=  initializer_range
														__snake_case						:     List[str]									=  layer_norm_eps
														__snake_case						:     List[Any]									=  position_embedding_type
														__snake_case						:     List[Any]									=  use_cache
														__snake_case						:     str									=  pad_token_id






							@classmethod
							def        __snake_case   (				cls			:    List[str]       ,      lowerCamelCase			:    Union[str, os.PathLike]       ,      **lowerCamelCase			:    Any							)      ->       "PretrainedConfig":
														cls._set_token_in_kwargs(lowerCamelCase							)

														__snake_case					,     __snake_case						:     Union[str, Any]									=  cls.get_config_dict(lowerCamelCase       ,      **lowerCamelCase							)

														# get the text config dict if we are loading from AlignConfig
														if config_dict.get("model_type"							) == "align":
																					__snake_case						:     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 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Any	    =    "align_vision_model"

							def __init__(				self			:    Dict       ,      lowerCamelCase			:    int = 3       ,      lowerCamelCase			:    int = 600       ,      lowerCamelCase			:    float = 2.0       ,      lowerCamelCase			:    float = 3.1       ,      lowerCamelCase			:    int = 8       ,      lowerCamelCase			:    List[int] = [3, 3, 5, 3, 5, 5, 3]       ,      lowerCamelCase			:    List[int] = [32, 16, 24, 40, 80, 112, 192]       ,      lowerCamelCase			:    List[int] = [16, 24, 40, 80, 112, 192, 320]       ,      lowerCamelCase			:    List[int] = []       ,      lowerCamelCase			:    List[int] = [1, 2, 2, 2, 1, 2, 1]       ,      lowerCamelCase			:    List[int] = [1, 2, 2, 3, 3, 4, 1]       ,      lowerCamelCase			:    List[int] = [1, 6, 6, 6, 6, 6, 6]       ,      lowerCamelCase			:    float = 0.25       ,      lowerCamelCase			:    str = "swish"       ,      lowerCamelCase			:    int = 2560       ,      lowerCamelCase			:    str = "mean"       ,      lowerCamelCase			:    float = 0.02       ,      lowerCamelCase			:    float = 0.0_01       ,      lowerCamelCase			:    float = 0.99       ,      lowerCamelCase			:    float = 0.2       ,      **lowerCamelCase			:    Optional[Any]       ,      )      ->       List[Any]:
														super().__init__(**lowerCamelCase							)

														__snake_case						:     Union[str, Any]									=  num_channels
														__snake_case						:     int									=  image_size
														__snake_case						:     Any									=  width_coefficient
														__snake_case						:     List[Any]									=  depth_coefficient
														__snake_case						:     Any									=  depth_divisor
														__snake_case						:     Union[str, Any]									=  kernel_sizes
														__snake_case						:     Union[str, Any]									=  in_channels
														__snake_case						:     int									=  out_channels
														__snake_case						:     Tuple									=  depthwise_padding
														__snake_case						:     List[str]									=  strides
														__snake_case						:     Optional[int]									=  num_block_repeats
														__snake_case						:     Tuple									=  expand_ratios
														__snake_case						:     List[Any]									=  squeeze_expansion_ratio
														__snake_case						:     int									=  hidden_act
														__snake_case						:     int									=  hidden_dim
														__snake_case						:     List[str]									=  pooling_type
														__snake_case						:     Optional[int]									=  initializer_range
														__snake_case						:     str									=  batch_norm_eps
														__snake_case						:     Union[str, Any]									=  batch_norm_momentum
														__snake_case						:     Dict									=  drop_connect_rate
														__snake_case						:     Dict									=  sum(lowerCamelCase							) * 4






							@classmethod
							def        __snake_case   (				cls			:    Any       ,      lowerCamelCase			:    Union[str, os.PathLike]       ,      **lowerCamelCase			:    int							)      ->       "PretrainedConfig":
														cls._set_token_in_kwargs(lowerCamelCase							)

														__snake_case					,     __snake_case						:     List[Any]									=  cls.get_config_dict(lowerCamelCase       ,      **lowerCamelCase							)

														# get the vision config dict if we are loading from AlignConfig
														if config_dict.get("model_type"							) == "align":
																					__snake_case						:     List[str]									=  config_dict["vision_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 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Dict	    =    "align"
							__UpperCAmelCase       :				Optional[int]	    =    True

							def __init__(				self			:    str       ,      lowerCamelCase			:    List[Any]=None       ,      lowerCamelCase			:    Optional[Any]=None       ,      lowerCamelCase			:    Tuple=640       ,      lowerCamelCase			:    List[Any]=1.0       ,      lowerCamelCase			:    Dict=0.02       ,      **lowerCamelCase			:    Tuple       ,      )      ->       str:
														super().__init__(**lowerCamelCase							)

														if text_config is None:
																					__snake_case						:     Dict									=  {}
																					logger.info("text_config is None. Initializing the AlignTextConfig with default values."							)

														if vision_config is None:
																					__snake_case						:     Union[str, Any]									=  {}
																					logger.info("vision_config is None. Initializing the AlignVisionConfig with default values."							)

														__snake_case						:     Union[str, Any]									=  AlignTextConfig(**lowerCamelCase							)
														__snake_case						:     List[Any]									=  AlignVisionConfig(**lowerCamelCase							)

														__snake_case						:     List[Any]									=  projection_dim
														__snake_case						:     Optional[int]									=  temperature_init_value
														__snake_case						:     Any									=  initializer_range

							@classmethod
							def        __snake_case   (				cls			:    Union[str, Any]       ,      lowerCamelCase			:    AlignTextConfig       ,      lowerCamelCase			:    AlignVisionConfig       ,      **lowerCamelCase			:    Tuple							)      ->       Any:
														return cls(text_config=text_config.to_dict()       ,      vision_config=vision_config.to_dict()       ,      **lowerCamelCase							)






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



"""simple docstring"""
import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=None						):
						# set parameter of one layer
						assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
						__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
												__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  np.asarray(weights[2]						)

						set_param(
						    torch_layer.self_attention.query_key ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.value ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.output.dense ,    torch.tensor(_lowerCAmelCase						).view(-1 ,    _lowerCAmelCase						).contiguous().transpose(0 ,    1						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  np.asarray(weights[2]						)
						__lowerCAmelCase     =  np.asarray(weights[3]						)

						set_param(
						    torch_layer.self_attention.query ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.key ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.value ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.output.dense ,    torch.tensor(_lowerCAmelCase						).view(-1 ,    _lowerCAmelCase						).contiguous().transpose(0 ,    1						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# layernorm 1
						__lowerCAmelCase     =  weights[0][0][0]
						__lowerCAmelCase     =  np.asarray(layer_norm_a[0]						)
						__lowerCAmelCase     =  np.asarray(layer_norm_a[1]						)
						set_param(
						    torch_block.attention.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# lsh weights + output
						__lowerCAmelCase     =  weights[0][1]
						if len(_lowerCAmelCase						) < 4:
												set_layer_weights_in_torch_lsh(_lowerCAmelCase ,    torch_block.attention ,    _lowerCAmelCase						)
						else:
												set_layer_weights_in_torch_local(_lowerCAmelCase ,    torch_block.attention ,    _lowerCAmelCase						)

						# intermediate weighs
						__lowerCAmelCase     =  weights[2][0][1][2]

						# Chunked Feed Forward
						if len(_lowerCAmelCase						) == 4:
												__lowerCAmelCase     =  intermediate_weights[2]

						# layernorm 2
						__lowerCAmelCase     =  np.asarray(intermediate_weights[0][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[0][1]						)
						set_param(
						    torch_block.feed_forward.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# intermediate dense
						__lowerCAmelCase     =  np.asarray(intermediate_weights[1][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[1][1]						)
						set_param(
						    torch_block.feed_forward.dense.dense ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

						# intermediate out
						__lowerCAmelCase     =  np.asarray(intermediate_weights[4][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[4][1]						)
						set_param(
						    torch_block.feed_forward.output.dense ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# reformer model
						__lowerCAmelCase     =  torch_model.reformer

						# word embeds
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						set_param(
						    torch_model_reformer.embeddings.word_embeddings ,    torch.tensor(_lowerCAmelCase						) ,    )

						if isinstance(weights[3] ,    _lowerCAmelCase						):
												__lowerCAmelCase     =  torch_model_reformer.embeddings.position_embeddings
												for emb_idx in range(len(position_embeddings.weights						)						):
																		__lowerCAmelCase     =  np.asarray(weights[3][emb_idx][0]						)
																		assert (
																		    position_embeddings.weights[emb_idx].shape == emb_weights.shape
																		), f"""{position_embeddings[emb_idx]} emb does not match"""
																		__lowerCAmelCase     =  nn.Parameter(torch.tensor(_lowerCAmelCase						)						)

						__lowerCAmelCase     =  weights[5]
						assert len(torch_model_reformer.encoder.layers						) * 4 == len(
						    _lowerCAmelCase						), "HF and trax model do not have the same number of layers"
						for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers						):
												__lowerCAmelCase     =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
												set_block_weights_in_torch(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)

						# output layer norm
						__lowerCAmelCase     =  np.asarray(weights[7][0]						)
						__lowerCAmelCase     =  np.asarray(weights[7][1]						)
						set_param(
						    torch_model_reformer.encoder.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# output embeddings
						__lowerCAmelCase     =  np.asarray(weights[9][0]						)
						__lowerCAmelCase     =  np.asarray(weights[9][1]						)
						set_param(
						    torch_model.lm_head.decoder ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# Initialise PyTorch model
						__lowerCAmelCase     =  ReformerConfig.from_json_file(_lowerCAmelCase						)
						print(f"""Building PyTorch model from configuration: {config}"""						)
						__lowerCAmelCase     =  ReformerModelWithLMHead(_lowerCAmelCase						)

						with open(_lowerCAmelCase ,    """rb"""						) as f:
												__lowerCAmelCase     =  pickle.load(_lowerCAmelCase						)["""weights"""]

						set_model_weights_in_torch(_lowerCAmelCase ,    _lowerCAmelCase ,    config.hidden_size						)

						# Save pytorch-model
						print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
						torch.save(model.state_dict() ,    _lowerCAmelCase						)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_					     =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained Reformer model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
