





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase_ 							=       {
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_ 							=       [
					    """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TapasForMaskedLM""",
					    """TapasForQuestionAnswering""",
					    """TapasForSequenceClassification""",
					    """TapasModel""",
					    """TapasPreTrainedModel""",
					    """load_tf_weights_in_tapas""",
					]
try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_ 							=       [
					    """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFTapasForMaskedLM""",
					    """TFTapasForQuestionAnswering""",
					    """TFTapasForSequenceClassification""",
					    """TFTapasModel""",
					    """TFTapasPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
					from .tokenization_tapas import TapasTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tapas import (
										    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TapasForMaskedLM,
										    TapasForQuestionAnswering,
										    TapasForSequenceClassification,
										    TapasModel,
										    TapasPreTrainedModel,
										    load_tf_weights_in_tapas,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_tapas import (
										    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFTapasForMaskedLM,
										    TFTapasForQuestionAnswering,
										    TFTapasForSequenceClassification,
										    TFTapasModel,
										    TFTapasPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase_ 							=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










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


UpperCAmelCase_ 							=       logging.get_logger(__name__)
logging.set_verbosity_info()





def   lowerCamelCase__       (       UpperCamelCase__     :    str    ,      UpperCamelCase__     :    str  )     ->	Union[str, Any]:



					'''simple docstring'''




					if "xprophetnet" in prophetnet_checkpoint_path:
										_snake_case													=			XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__  )
										_snake_case							,       _snake_case													=			XLMProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase__    ,      output_loading_info=UpperCamelCase__  )
					else:
										_snake_case													=			ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__  )
										_snake_case							,       _snake_case													=			ProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase__    ,      output_loading_info=UpperCamelCase__  )

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

					_snake_case													=			{
					    '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"]:
										_snake_case													=			key.split('.'  )

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

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

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

																				elif attribute == "key_proj":
																									_snake_case													=			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]  )
																									_snake_case													=			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]  )
																				elif attribute == "value_proj":
																									_snake_case													=			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]  )
																									_snake_case													=			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]  )
																				_snake_case													=			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."
																				_snake_case													=			nn.Parameter(old_model.embed_positions.weight[:512, :]  )
																				_snake_case													=			True
																				break

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

																				if old_attribute == "":
																									_snake_case													=			old_model
																				else:
																									if not hasattr(UpperCamelCase__    ,      UpperCamelCase__  ):
																														raise ValueError(F'''{old_model} does not have {old_attribute}'''  )
																									_snake_case													=			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__":
					UpperCAmelCase_ 							=       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."""
					)
					UpperCAmelCase_ 							=       parser.parse_args()
					convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)



