
'''simple docstring'''


import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


snake_case_ :  str     =					logging.get_logger(__name__)
logging.set_verbosity_info()



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       str):
			if "xprophetnet" in prophetnet_checkpoint_path:
						UpperCamelCase       =  XLMProphetNetForConditionalGenerationOld.from_pretrained(_UpperCAmelCase)
						UpperCamelCase     , UpperCamelCase       =  XLMProphetNetForConditionalGeneration.from_pretrained(
						    _UpperCAmelCase,      output_loading_info=_UpperCAmelCase)
			else:
						UpperCamelCase       =  ProphetNetForConditionalGenerationOld.from_pretrained(_UpperCAmelCase)
						UpperCamelCase     , UpperCamelCase       =  ProphetNetForConditionalGeneration.from_pretrained(
						    _UpperCAmelCase,      output_loading_info=_UpperCAmelCase)

			UpperCamelCase       =  ['''key_proj''', '''value_proj''', '''query_proj''']

			UpperCamelCase       =  {
			    '''self_attn''': '''ngram_self_attn''',
			    '''cross_attn''': '''encoder_attn''',
			    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
			    '''feed_forward_layer_norm''': '''final_layer_norm''',
			    '''feed_forward''': '''''',
			    '''intermediate''': '''fc1''',
			    '''output''': '''fc2''',
			    '''key_proj''': '''k_proj''',
			    '''query_proj''': '''q_proj''',
			    '''value_proj''': '''v_proj''',
			    '''word_embeddings''': '''embed_tokens''',
			    '''embeddings_layer_norm''': '''emb_layer_norm''',
			    '''relative_pos_embeddings''': '''relative_linear''',
			    '''ngram_embeddings''': '''ngram_input_embed''',
			    '''position_embeddings''': '''embed_positions''',
			}

			for key in loading_info["missing_keys"]:
						UpperCamelCase       =  key.split('''.''')

						if attributes[0] == "lm_head":
									UpperCamelCase       =  prophet
									UpperCamelCase       =  prophet_old
						else:
									UpperCamelCase       =  prophet.prophetnet
									UpperCamelCase       =  prophet_old.model

						UpperCamelCase       =  False
						for attribute in attributes:
									if attribute in mapping:
												UpperCamelCase       =  mapping[attribute]
												if not hasattr(_UpperCAmelCase,      _UpperCAmelCase) and len(_UpperCAmelCase) > 0:
															UpperCamelCase       =  attribute
									elif hasattr(_UpperCAmelCase,      _UpperCAmelCase):
												UpperCamelCase       =  attribute

									if attribute == "weight":
												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
												UpperCamelCase       =  old_model.weight
												logger.info(f'{attribute} is initialized.')
												UpperCamelCase       =  True
												break
									elif attribute == "bias":
												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
												UpperCamelCase       =  old_model.bias
												logger.info(f'{attribute} is initialized')
												UpperCamelCase       =  True
												break
									elif attribute in special_keys and hasattr(_UpperCAmelCase,      '''in_proj_weight'''):
												UpperCamelCase       =  old_model.in_proj_weight.shape[0] // 3
												UpperCamelCase       =  getattr(_UpperCAmelCase,      _UpperCAmelCase)
												param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
												param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
												if attribute == "query_proj":
															UpperCamelCase       =  nn.Parameter(old_model.in_proj_weight[:embed_dim, :])
															UpperCamelCase       =  nn.Parameter(old_model.in_proj_bias[:embed_dim])

												elif attribute == "key_proj":
															UpperCamelCase       =  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :])
															UpperCamelCase       =  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim])
												elif attribute == "value_proj":
															UpperCamelCase       =  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :])
															UpperCamelCase       =  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :])
												UpperCamelCase       =  True
												break
									elif attribute == "position_embeddings":
												assert (
												    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
												), "Hidden size has to match"
												assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
												UpperCamelCase       =  nn.Parameter(old_model.embed_positions.weight[:512, :])
												UpperCamelCase       =  True
												break

									if attribute.isdigit():
												UpperCamelCase       =  model[int(_UpperCAmelCase)]
												UpperCamelCase       =  old_model[int(_UpperCAmelCase)]
									else:
												UpperCamelCase       =  getattr(_UpperCAmelCase,      _UpperCAmelCase)

												if old_attribute == "":
															UpperCamelCase       =  old_model
												else:
															if not hasattr(_UpperCAmelCase,      _UpperCAmelCase):
																		raise ValueError(f'{old_model} does not have {old_attribute}')
															UpperCamelCase       =  getattr(_UpperCAmelCase,      _UpperCAmelCase)

						if not is_key_init:
									raise ValueError(f'{key} was not correctly initialized!')

			print(f'Saving model to {pytorch_dump_folder_path}')
			prophet.save_pretrained(_UpperCAmelCase)


if __name__ == "__main__":
			snake_case_ :  Any     =					argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			snake_case_ :  Dict     =					parser.parse_args()
			convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_ :  Any     =					{
    'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
    'tokenization_electra': ['ElectraTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Union[str, Any]     =					['ElectraTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Tuple     =					[
       'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ElectraForCausalLM',
       'ElectraForMaskedLM',
       'ElectraForMultipleChoice',
       'ElectraForPreTraining',
       'ElectraForQuestionAnswering',
       'ElectraForSequenceClassification',
       'ElectraForTokenClassification',
       'ElectraModel',
       'ElectraPreTrainedModel',
       'load_tf_weights_in_electra',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  int     =					[
       'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFElectraForMaskedLM',
       'TFElectraForMultipleChoice',
       'TFElectraForPreTraining',
       'TFElectraForQuestionAnswering',
       'TFElectraForSequenceClassification',
       'TFElectraForTokenClassification',
       'TFElectraModel',
       'TFElectraPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case_ :  Optional[int]     =					[
       'FlaxElectraForCausalLM',
       'FlaxElectraForMaskedLM',
       'FlaxElectraForMultipleChoice',
       'FlaxElectraForPreTraining',
       'FlaxElectraForQuestionAnswering',
       'FlaxElectraForSequenceClassification',
       'FlaxElectraForTokenClassification',
       'FlaxElectraModel',
       'FlaxElectraPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
   from .tokenization_electra import ElectraTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_electra_fast import ElectraTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_electra import (
          ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
          ElectraPreTrainedModel,
          load_tf_weights_in_electra,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_electra import (
          TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFElectraForMaskedLM,
          TFElectraForMultipleChoice,
          TFElectraForPreTraining,
          TFElectraForQuestionAnswering,
          TFElectraForSequenceClassification,
          TFElectraForTokenClassification,
          TFElectraModel,
          TFElectraPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
          FlaxElectraPreTrainedModel,
      )

else:
   import sys

   snake_case_ :  Union[str, Any]     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





