





'''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    a__							(							lowercase   :	Tuple,   lowercase   :	Optional[int],   lowercase   :	str=None )						->						Tuple:





     """simple docstring"""

     assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
     _UpperCamelCase							       =			nn.Parameter(lowercase )
     if bias is not None:
          assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
          _UpperCamelCase							       =			nn.Parameter(lowercase )







def    a__							(							lowercase   :	Optional[int],   lowercase   :	List[Any],   lowercase   :	Optional[int] )						->						Optional[int]:





     """simple docstring"""

     _UpperCamelCase							       =			np.asarray(weights[0] )
     _UpperCamelCase							       =			np.asarray(weights[1] )
     _UpperCamelCase							       =			np.asarray(weights[2] )

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







def    a__							(							lowercase   :	int,   lowercase   :	int,   lowercase   :	str )						->						Dict:





     """simple docstring"""

     _UpperCamelCase							       =			np.asarray(weights[0] )
     _UpperCamelCase							       =			np.asarray(weights[1] )
     _UpperCamelCase							       =			np.asarray(weights[2] )
     _UpperCamelCase							       =			np.asarray(weights[3] )

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







def    a__							(							lowercase   :	List[Any],   lowercase   :	List[str],   lowercase   :	Tuple )						->						Dict:





     """simple docstring"""

     _UpperCamelCase							       =			weights[0][0][0]
     _UpperCamelCase							       =			np.asarray(layer_norm_a[0] )
     _UpperCamelCase							       =			np.asarray(layer_norm_a[1] )
     set_param(
         torch_block.attention.layer_norm,   torch.tensor(lowercase ),   torch.tensor(lowercase ),   )

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

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

     # Chunked Feed Forward
     if len(lowercase ) == 4:
          _UpperCamelCase							       =			intermediate_weights[2]

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

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

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







def    a__							(							lowercase   :	int,   lowercase   :	str,   lowercase   :	List[str] )						->						str:





     """simple docstring"""

     _UpperCamelCase							       =			torch_model.reformer

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

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

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

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

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







def    a__							(							lowercase   :	List[Any],   lowercase   :	Optional[int],   lowercase   :	Tuple )						->						List[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			ReformerConfig.from_json_file(lowercase )
     print(F"""Building PyTorch model from configuration: {config}""" )
     _UpperCamelCase							       =			ReformerModelWithLMHead(lowercase )

     with open(lowercase,   '''rb''' ) as f:
          _UpperCamelCase							       =			pickle.load(lowercase )['''weights''']

     set_model_weights_in_torch(lowercase,   lowercase,   config.hidden_size )

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


if __name__ == "__main__":
   lowercase__		:     Union[str, Any]			=							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.'
   )
   lowercase__		:     Union[str, Any]			=							parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  List[Any]				    =      'philschmid/bart-large-cnn-samsum'
      snake_case__     :  int				    =      (
          'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
          'and returns a summary of the text.'
      )
      snake_case__     :  Tuple				    =      'summarizer'
      snake_case__     :  int				    =      AutoTokenizer
      snake_case__     :  Any				    =      AutoModelForSeqaSeqLM

      snake_case__     :  Optional[int]				    =      ['text']
      snake_case__     :  Optional[int]				    =      ['text']

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.pre_processor(__lowerCAmelCase						,			return_tensors="""pt"""						,			truncation=__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.model.generate(**__lowerCAmelCase		)[0]







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.pre_processor.decode(__lowerCAmelCase						,			skip_special_tokens=__lowerCAmelCase						,			clean_up_tokenization_spaces=__lowerCAmelCase		)



