




"""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 					_lowerCamelCase(					a ,  a ,  a=None							):
       # set parameter of one layer
       assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
       __a     				=  nn.Parameter(a							)
       if bias is not None:
              assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
              __a     				=  nn.Parameter(a							)



def 					_lowerCamelCase(					a ,  a ,  a							):
       # set torch weights for 1-to-1 comparison
       __a     				=  np.asarray(weights[0]							)
       __a     				=  np.asarray(weights[1]							)
       __a     				=  np.asarray(weights[2]							)

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



def 					_lowerCamelCase(					a ,  a ,  a							):
       # set torch weights for 1-to-1 comparison
       __a     				=  np.asarray(weights[0]							)
       __a     				=  np.asarray(weights[1]							)
       __a     				=  np.asarray(weights[2]							)
       __a     				=  np.asarray(weights[3]							)

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



def 					_lowerCamelCase(					a ,  a ,  a							):
       # layernorm 1
       __a     				=  weights[0][0][0]
       __a     				=  np.asarray(layer_norm_a[0]							)
       __a     				=  np.asarray(layer_norm_a[1]							)
       set_param(
           torch_block.attention.layer_norm ,  torch.tensor(a							) ,  torch.tensor(a							) ,  )

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

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

       # Chunked Feed Forward
       if len(a							) == 4:
              __a     				=  intermediate_weights[2]

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

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

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



def 					_lowerCamelCase(					a ,  a ,  a							):
       # reformer model
       __a     				=  torch_model.reformer

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

       if isinstance(weights[3] ,  a							):
              __a     				=  torch_model_reformer.embeddings.position_embeddings
              for emb_idx in range(len(position_embeddings.weights							)							):
                     __a     				=  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"
                     __a     				=  nn.Parameter(torch.tensor(a							)							)

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

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

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



def 					_lowerCamelCase(					a ,  a ,  a							):
       # Initialise PyTorch model
       __a     				=  ReformerConfig.from_json_file(a							)
       print(F"Building PyTorch model from configuration: {config}"							)
       __a     				=  ReformerModelWithLMHead(a							)

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

       set_model_weights_in_torch(a ,  a ,  config.hidden_size							)

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[str]			=	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__:Tuple			=	parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)

"""simple docstring"""



import functools
import operator

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


SCREAMING_SNAKE_CASE__:Optional[Any]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:str			=	{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		"""sew-d"""


 def __init__(				self  ,						lowerCamelCase=32  ,						lowerCamelCase=768  ,						lowerCamelCase=12  ,						lowerCamelCase=12  ,						lowerCamelCase=3072  ,						lowerCamelCase=2  ,						lowerCamelCase=512  ,						lowerCamelCase=256  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=("p2c", "c2p")  ,						lowerCamelCase="layer_norm"  ,						lowerCamelCase="gelu_python"  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.02  ,						lowerCamelCase=1E-7  ,						lowerCamelCase=1E-5  ,						lowerCamelCase="group"  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)  ,						lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)  ,						lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)  ,						lowerCamelCase=False  ,						lowerCamelCase=128  ,						lowerCamelCase=16  ,						lowerCamelCase=True  ,						lowerCamelCase=0.05  ,						lowerCamelCase=10  ,						lowerCamelCase=2  ,						lowerCamelCase=0.0  ,						lowerCamelCase=10  ,						lowerCamelCase=0  ,						lowerCamelCase="mean"  ,						lowerCamelCase=False  ,						lowerCamelCase=False  ,						lowerCamelCase=256  ,						lowerCamelCase=0  ,						lowerCamelCase=1  ,						lowerCamelCase=2  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase  ,						pad_token_id=lowerCamelCase  ,						bos_token_id=lowerCamelCase  ,						eos_token_id=lowerCamelCase		)
        __a     				=  hidden_size
        __a     				=  feat_extract_norm
        __a     				=  feat_extract_activation
        __a     				=  list(lowerCamelCase		)
        __a     				=  list(lowerCamelCase		)
        __a     				=  list(lowerCamelCase		)
        __a     				=  conv_bias
        __a     				=  num_conv_pos_embeddings
        __a     				=  num_conv_pos_embedding_groups
        __a     				=  len(self.conv_dim		)
        __a     				=  num_hidden_layers
        __a     				=  intermediate_size
        __a     				=  squeeze_factor
        __a     				=  max_position_embeddings
        __a     				=  position_buckets
        __a     				=  share_att_key
        __a     				=  relative_attention
        __a     				=  norm_rel_ebd
        __a     				=  list(lowerCamelCase		)
        __a     				=  hidden_act
        __a     				=  num_attention_heads
        __a     				=  hidden_dropout
        __a     				=  attention_dropout
        __a     				=  activation_dropout
        __a     				=  feat_proj_dropout
        __a     				=  final_dropout
        __a     				=  layer_norm_eps
        __a     				=  feature_layer_norm_eps
        __a     				=  initializer_range
        __a     				=  vocab_size

        if (
            (len(self.conv_stride		) != self.num_feat_extract_layers)
            or (len(self.conv_kernel		) != self.num_feat_extract_layers)
            or (len(self.conv_dim		) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."		)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a     				=  apply_spec_augment
        __a     				=  mask_time_prob
        __a     				=  mask_time_length
        __a     				=  mask_time_min_masks
        __a     				=  mask_feature_prob
        __a     				=  mask_feature_length
        __a     				=  mask_feature_min_masks

        # ctc loss
        __a     				=  ctc_loss_reduction
        __a     				=  ctc_zero_infinity

        # sequence classification
        __a     				=  use_weighted_layer_sum
        __a     				=  classifier_proj_size






 @property
 def 		a__ (				self		):
        return functools.reduce(operator.mul  ,						self.conv_stride  ,						1		)
