





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


__UpperCAmelCase			:  Dict										=				logging.get_logger(__name__)

__UpperCAmelCase			:  str										=				{
    "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        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	"""m2m_100"""
       lowerCAmelCase__ 						=	["""past_key_values"""]
       lowerCAmelCase__ 						=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




       def __init__(    self					:       List[str]				,							A					:       str=128_112				,							A					:       Optional[Any]=1_024				,							A					:       Optional[Any]=12				,							A					:       List[str]=4_096				,							A					:       List[str]=16				,							A					:       List[str]=12				,							A					:       List[str]=4_096				,							A					:       Union[str, Any]=16				,							A					:       Any=0.05				,							A					:       Tuple=0.05				,							A					:       Optional[Any]=True				,							A					:       Dict=True				,							A					:       Optional[int]="relu"				,							A					:       Optional[int]=1_024				,							A					:       Any=0.1				,							A					:       List[Any]=0.1				,							A					:       int=0.0				,							A					:       Optional[int]=0.02				,							A					:       int=2				,							A					:       List[Any]=True				,							A					:       Optional[Any]=1				,							A					:       Dict=0				,							A					:       List[str]=2				,							**A					:       Any				,							):
         __snake_case:		int					    =		vocab_size
         __snake_case:		Any					    =		max_position_embeddings
         __snake_case:		List[str]					    =		d_model
         __snake_case:		str					    =		encoder_ffn_dim
         __snake_case:		List[Any]					    =		encoder_layers
         __snake_case:		Dict					    =		encoder_attention_heads
         __snake_case:		int					    =		decoder_ffn_dim
         __snake_case:		Dict					    =		decoder_layers
         __snake_case:		Optional[int]					    =		decoder_attention_heads
         __snake_case:		Union[str, Any]					    =		dropout
         __snake_case:		List[Any]					    =		attention_dropout
         __snake_case:		Union[str, Any]					    =		activation_dropout
         __snake_case:		Tuple					    =		activation_function
         __snake_case:		Union[str, Any]					    =		init_std
         __snake_case:		Dict					    =		encoder_layerdrop
         __snake_case:		Union[str, Any]					    =		decoder_layerdrop
         __snake_case:		Any					    =		use_cache
         __snake_case:		Any					    =		encoder_layers
         __snake_case:		Optional[int]					    =		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        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       @property
       def 				UpperCAmelCase__					(    self					:       int						):
         __snake_case:		str					    =		OrderedDict(
             [
                 ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                 ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
             ]						)

         if self.use_past:
           __snake_case:		Optional[Any]					    =		{0: """batch"""}
           __snake_case:		Any					    =		{0: """batch""", 1: """past_decoder_sequence + sequence"""}
         else:
           __snake_case:		Any					    =		{0: """batch""", 1: """decoder_sequence"""}
           __snake_case:		List[Any]					    =		{0: """batch""", 1: """decoder_sequence"""}

         if self.use_past:
           self.fill_with_past_key_values_(A				,							direction="""inputs"""						)
         return common_inputs




       def 				UpperCAmelCase__					(    self					:       str				,							A					:       PreTrainedTokenizer				,							A					:       int = -1				,							A					:       int = -1				,							A					:       bool = False				,							A					:       Optional[TensorType] = None				,							):
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         __snake_case:		List[Any]					    =		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
         __snake_case:		List[str]					    =		tokenizer.num_special_tokens_to_add(A						)
         __snake_case:		Any					    =		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
         __snake_case:		Union[str, Any]					    =		[""" """.join([tokenizer.unk_token]						) * seq_length] * batch_size
         __snake_case:		Union[str, Any]					    =		dict(tokenizer(A				,							return_tensors=A						)						)
         return common_inputs




       def 				UpperCAmelCase__					(    self					:       str				,							A					:       PreTrainedTokenizer				,							A					:       int = -1				,							A					:       int = -1				,							A					:       bool = False				,							A					:       Optional[TensorType] = None				,							):
         __snake_case:		Tuple					    =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             A				,							A				,							A				,							A				,							A						)

         # Generate decoder inputs
         __snake_case:		Union[str, Any]					    =		seq_length if not self.use_past else 1
         __snake_case:		Optional[Any]					    =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             A				,							A				,							A				,							A				,							A						)
         __snake_case:		List[str]					    =		{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
         __snake_case:		int					    =		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
           __snake_case    ,  __snake_case:		int					    =		common_inputs["""input_ids"""].shape
           __snake_case:		Dict					    =		common_inputs["""decoder_input_ids"""].shape[1]
           __snake_case    ,  __snake_case:		Dict					    =		self.num_attention_heads
           __snake_case:		Tuple					    =		(
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           __snake_case:		Optional[int]					    =		decoder_seq_length + 3
           __snake_case:		Any					    =		(
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )

           __snake_case:		str					    =		torch.cat(
               [common_inputs["""decoder_attention_mask"""], torch.ones(A				,							A						)]				,							dim=1						)

           __snake_case:		List[str]					    =		[]
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           __snake_case    ,  __snake_case:		Tuple					    =		self.num_layers
           __snake_case:		Dict					    =		min(A				,							A						)
           __snake_case:		Tuple					    =		max(A				,							A						) - min_num_layers
           __snake_case:		Tuple					    =		"""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.
           __snake_case:		List[str]					    =		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






       lowerCAmelCase__ 						=	_generate_dummy_inputs_for_default_and_seqaseq_lm













import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







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

  return orig_key







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[int]:
  for key in orig_state_dict.copy().keys():
    __snake_case:		str					    =		orig_state_dict.pop(SCREAMING_SNAKE_CASE__)

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

  __snake_case:		str					    =		orig_state_dict["""cls.predictions.decoder.bias"""]
  __snake_case:		int					    =		(
      torch.arange(config.max_position_embeddings).expand((1, -1)) + 2
  )

  return orig_state_dict







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[Any]:
  __snake_case:		int					    =		torch.load(SCREAMING_SNAKE_CASE__   ,   map_location="""cpu""")["""model_state_dict"""]
  __snake_case:		List[Any]					    =		NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE__)
  __snake_case:		Optional[int]					    =		NystromformerForMaskedLM(SCREAMING_SNAKE_CASE__)

  __snake_case:		Dict					    =		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			:  str										=				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			:  Optional[Any]										=				parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






