



"""simple docstring"""


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			{
    """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json""",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				'''m2m_100'''
						__UpperCamelCase													=				['''past_key_values''']
						__UpperCamelCase													=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






						def __init__(   self					:Optional[int]  ,    lowercase_					:List[str]=12_81_12  ,    lowercase_					:Optional[Any]=10_24  ,    lowercase_					:int=12  ,    lowercase_					:List[Any]=40_96  ,    lowercase_					:Union[str, Any]=16  ,    lowercase_					:int=12  ,    lowercase_					:List[str]=40_96  ,    lowercase_					:Tuple=16  ,    lowercase_					:Any=0.05  ,    lowercase_					:Tuple=0.05  ,    lowercase_					:Dict=True  ,    lowercase_					:List[Any]=True  ,    lowercase_					:Any="relu"  ,    lowercase_					:str=10_24  ,    lowercase_					:Union[str, Any]=0.1  ,    lowercase_					:List[Any]=0.1  ,    lowercase_					:List[str]=0.0  ,    lowercase_					:List[Any]=0.02  ,    lowercase_					:Any=2  ,    lowercase_					:str=True  ,    lowercase_					:List[str]=1  ,    lowercase_					:Any=0  ,    lowercase_					:Tuple=2  ,    **lowercase_					:str  ,    )							->     str:
												UpperCAmelCase			 =					vocab_size
												UpperCAmelCase			 =					max_position_embeddings
												UpperCAmelCase			 =					d_model
												UpperCAmelCase			 =					encoder_ffn_dim
												UpperCAmelCase			 =					encoder_layers
												UpperCAmelCase			 =					encoder_attention_heads
												UpperCAmelCase			 =					decoder_ffn_dim
												UpperCAmelCase			 =					decoder_layers
												UpperCAmelCase			 =					decoder_attention_heads
												UpperCAmelCase			 =					dropout
												UpperCAmelCase			 =					attention_dropout
												UpperCAmelCase			 =					activation_dropout
												UpperCAmelCase			 =					activation_function
												UpperCAmelCase			 =					init_std
												UpperCAmelCase			 =					encoder_layerdrop
												UpperCAmelCase			 =					decoder_layerdrop
												UpperCAmelCase			 =					use_cache
												UpperCAmelCase			 =					encoder_layers
												UpperCAmelCase			 =					scale_embedding  # scale factor will be sqrt(d_model) if True

												super().__init__(
												    pad_token_id=A_  ,    bos_token_id=A_  ,    eos_token_id=A_  ,    is_encoder_decoder=A_  ,    decoder_start_token_id=A_  ,    **A_  ,    )







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						@property
						def       UpperCAmelCase__	(   self					:Any   )							->     int:
												UpperCAmelCase			 =					OrderedDict(
												    [
												        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
												        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
												    ]   )

												if self.use_past:
																		UpperCAmelCase			 =					{0: "batch"}
																		UpperCAmelCase			 =					{0: "batch", 1: "past_decoder_sequence + sequence"}
												else:
																		UpperCAmelCase			 =					{0: "batch", 1: "decoder_sequence"}
																		UpperCAmelCase			 =					{0: "batch", 1: "decoder_sequence"}

												if self.use_past:
																		self.fill_with_past_key_values_(A_  ,    direction='inputs'   )
												return common_inputs






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:PreTrainedTokenizer  ,    lowercase_					:int = -1  ,    lowercase_					:int = -1  ,    lowercase_					:bool = False  ,    lowercase_					:Optional[TensorType] = None  ,    )							->     List[str]:
												UpperCAmelCase			 =					compute_effective_axis_dimension(
												    A_  ,    fixed_dimension=OnnxConfig.default_fixed_batch  ,    num_token_to_add=0   )

												# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
												UpperCAmelCase			 =					tokenizer.num_special_tokens_to_add(A_   )
												UpperCAmelCase			 =					compute_effective_axis_dimension(
												    A_  ,    fixed_dimension=OnnxConfig.default_fixed_sequence  ,    num_token_to_add=A_   )

												# Generate dummy inputs according to compute batch and sequence
												UpperCAmelCase			 =					[" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
												UpperCAmelCase			 =					dict(tokenizer(A_  ,    return_tensors=A_   )   )
												return common_inputs






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:PreTrainedTokenizer  ,    lowercase_					:int = -1  ,    lowercase_					:int = -1  ,    lowercase_					:bool = False  ,    lowercase_					:Optional[TensorType] = None  ,    )							->     str:
												UpperCAmelCase			 =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    A_  ,    A_  ,    A_  ,    A_  ,    A_   )

												# Generate decoder inputs
												UpperCAmelCase			 =					seq_length if not self.use_past else 1
												UpperCAmelCase			 =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    A_  ,    A_  ,    A_  ,    A_  ,    A_   )
												UpperCAmelCase			 =					{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
												UpperCAmelCase			 =					dict(**A_  ,    **A_   )

												if self.use_past:
																		if not is_torch_available():
																								raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
																		else:
																								import torch
																		UpperCAmelCase			 =					common_inputs["input_ids"].shape
																		UpperCAmelCase			 =					common_inputs["decoder_input_ids"].shape[1]
																		UpperCAmelCase			 =					self.num_attention_heads
																		UpperCAmelCase			 =					(
																		    batch,
																		    num_encoder_attention_heads,
																		    encoder_seq_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)
																		UpperCAmelCase			 =					decoder_seq_length + 3
																		UpperCAmelCase			 =					(
																		    batch,
																		    num_decoder_attention_heads,
																		    decoder_past_length,
																		    self._config.hidden_size // num_decoder_attention_heads,
																		)

																		UpperCAmelCase			 =					torch.cat(
																		    [common_inputs['decoder_attention_mask'], torch.ones(A_  ,    A_   )]  ,    dim=1   )

																		UpperCAmelCase			 =					[]
																		# If the number of encoder and decoder layers are present in the model configuration, both are considered
																		UpperCAmelCase			 =					self.num_layers
																		UpperCAmelCase			 =					min(A_  ,    A_   )
																		UpperCAmelCase			 =					max(A_  ,    A_   ) - min_num_layers
																		UpperCAmelCase			 =					"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

																		for _ in range(A_   ):
																								common_inputs["past_key_values"].append(
																								    (
																								        torch.zeros(A_   ),
																								        torch.zeros(A_   ),
																								        torch.zeros(A_   ),
																								        torch.zeros(A_   ),
																								    )   )
																		# TODO: test this.
																		UpperCAmelCase			 =					encoder_shape if remaining_side_name == "encoder" else decoder_shape
																		for _ in range(A_  ,    A_   ):
																								common_inputs["past_key_values"].append((torch.zeros(A_   ), torch.zeros(A_   ))   )
												return common_inputs




						__UpperCamelCase													=				_generate_dummy_inputs_for_default_and_seqaseq_lm








import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     if "model" in orig_key:
          _lowerCAmelCase :       Tuple	   =     orig_key.replace("model."		,				""					)
     if "norm1" in orig_key:
          _lowerCAmelCase :       Optional[Any]	   =     orig_key.replace("norm1"		,				"attention.output.LayerNorm"					)
     if "norm2" in orig_key:
          _lowerCAmelCase :       Optional[int]	   =     orig_key.replace("norm2"		,				"output.LayerNorm"					)
     if "norm" in orig_key:
          _lowerCAmelCase :       Any	   =     orig_key.replace("norm"		,				"LayerNorm"					)
     if "transformer" in orig_key:
          _lowerCAmelCase :       str	   =     orig_key.split("."					)[0].split("_"					)[-1]
          _lowerCAmelCase :       Any	   =     orig_key.replace(f'''transformer_{layer_num}'''		,				f'''encoder.layer.{layer_num}'''					)
     if "mha.attn" in orig_key:
          _lowerCAmelCase :       int	   =     orig_key.replace("mha.attn"		,				"attention.self"					)
     if "mha" in orig_key:
          _lowerCAmelCase :       Dict	   =     orig_key.replace("mha"		,				"attention"					)
     if "W_q" in orig_key:
          _lowerCAmelCase :       Union[str, Any]	   =     orig_key.replace("W_q"		,				"self.query"					)
     if "W_k" in orig_key:
          _lowerCAmelCase :       Tuple	   =     orig_key.replace("W_k"		,				"self.key"					)
     if "W_v" in orig_key:
          _lowerCAmelCase :       Optional[int]	   =     orig_key.replace("W_v"		,				"self.value"					)
     if "ff1" in orig_key:
          _lowerCAmelCase :       Tuple	   =     orig_key.replace("ff1"		,				"intermediate.dense"					)
     if "ff2" in orig_key:
          _lowerCAmelCase :       List[Any]	   =     orig_key.replace("ff2"		,				"output.dense"					)
     if "ff" in orig_key:
          _lowerCAmelCase :       str	   =     orig_key.replace("ff"		,				"output.dense"					)
     if "mlm_class" in orig_key:
          _lowerCAmelCase :       int	   =     orig_key.replace("mlm.mlm_class"		,				"cls.predictions.decoder"					)
     if "mlm" in orig_key:
          _lowerCAmelCase :       Union[str, Any]	   =     orig_key.replace("mlm"		,				"cls.predictions.transform"					)
     if "cls" not in orig_key:
          _lowerCAmelCase :       Optional[int]	   =     "nystromformer." + orig_key

     return orig_key







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Any:

     """simple docstring"""







     for key in orig_state_dict.copy().keys():
          _lowerCAmelCase :       List[str]	   =     orig_state_dict.pop(SCREAMING_SNAKE_CASE					)

          if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
               continue
          else:
               _lowerCAmelCase :       int	   =     val

     _lowerCAmelCase :       Tuple	   =     orig_state_dict["cls.predictions.decoder.bias"]
     _lowerCAmelCase :       Any	   =     (
         torch.arange(config.max_position_embeddings					).expand((1, -1)					) + 2
     )

     return orig_state_dict







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Optional[Any]	   =     torch.load(SCREAMING_SNAKE_CASE		,				map_location="cpu"					)["model_state_dict"]
     _lowerCAmelCase :       Optional[Any]	   =     NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       List[str]	   =     NystromformerForMaskedLM(SCREAMING_SNAKE_CASE					)

     _lowerCAmelCase :       Union[str, Any]	   =     convert_checkpoint_helper(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)

     model.load_state_dict(SCREAMING_SNAKE_CASE					)
     model.eval()
     model.save_pretrained(SCREAMING_SNAKE_CASE					)

     print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''					)


if __name__ == "__main__":
  __UpperCAmelCase     					=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--pytorch_model_path', default=None, type=str, required=True, help='Path to Nystromformer pytorch checkpoint.'
  )
  parser.add_argument(
      '--config_file',
      default=None,
      type=str,
      required=True,
      help='The json file for Nystromformer model config.',
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  __UpperCAmelCase     					=  parser.parse_args()
  convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






