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


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





def  _lowerCamelCase					(   a_				: int						,			a_				: Any):






			if "xprophetnet" in prophetnet_checkpoint_path:
						lowerCamelCase						:Dict						  =     XLMProphetNetForConditionalGenerationOld.from_pretrained(a_)
						lowerCamelCase						:List[str]						  =     XLMProphetNetForConditionalGeneration.from_pretrained(
						    a_						,			output_loading_info=a_)
			else:
						lowerCamelCase						:List[Any]						  =     ProphetNetForConditionalGenerationOld.from_pretrained(a_)
						lowerCamelCase						:List[str]						  =     ProphetNetForConditionalGeneration.from_pretrained(
						    a_						,			output_loading_info=a_)

			lowerCamelCase						:Tuple						  =     ['''key_proj''', '''value_proj''', '''query_proj''']

			lowerCamelCase						:List[Any]						  =     {
			    '''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"]:
						lowerCamelCase						:Optional[int]						  =     key.split('''.''')

						if attributes[0] == "lm_head":
									lowerCamelCase						:Optional[Any]						  =     prophet
									lowerCamelCase						:int						  =     prophet_old
						else:
									lowerCamelCase						:List[str]						  =     prophet.prophetnet
									lowerCamelCase						:str						  =     prophet_old.model

						lowerCamelCase						:Any						  =     False
						for attribute in attributes:
									if attribute in mapping:
												lowerCamelCase						:Optional[Any]						  =     mapping[attribute]
												if not hasattr(a_						,			a_) and len(a_) > 0:
															lowerCamelCase						:Union[str, Any]						  =     attribute
									elif hasattr(a_						,			a_):
												lowerCamelCase						:Optional[int]						  =     attribute

									if attribute == "weight":
												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
												lowerCamelCase						:str						  =     old_model.weight
												logger.info(F"{attribute} is initialized.")
												lowerCamelCase						:List[Any]						  =     True
												break
									elif attribute == "bias":
												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
												lowerCamelCase						:Union[str, Any]						  =     old_model.bias
												logger.info(F"{attribute} is initialized")
												lowerCamelCase						:Dict						  =     True
												break
									elif attribute in special_keys and hasattr(a_						,			'''in_proj_weight'''):
												lowerCamelCase						:List[Any]						  =     old_model.in_proj_weight.shape[0] // 3
												lowerCamelCase						:Optional[int]						  =     getattr(a_						,			a_)
												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":
															lowerCamelCase						:str						  =     nn.Parameter(old_model.in_proj_weight[:embed_dim, :])
															lowerCamelCase						:Any						  =     nn.Parameter(old_model.in_proj_bias[:embed_dim])

												elif attribute == "key_proj":
															lowerCamelCase						:Any						  =     nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :])
															lowerCamelCase						:List[str]						  =     nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim])
												elif attribute == "value_proj":
															lowerCamelCase						:Dict						  =     nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :])
															lowerCamelCase						:str						  =     nn.Parameter(old_model.in_proj_bias[2 * embed_dim :])
												lowerCamelCase						:List[Any]						  =     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] == 5_12, "We want 512 position_embeddings."
												lowerCamelCase						:Dict						  =     nn.Parameter(old_model.embed_positions.weight[:5_12, :])
												lowerCamelCase						:Union[str, Any]						  =     True
												break

									if attribute.isdigit():
												lowerCamelCase						:Dict						  =     model[int(a_)]
												lowerCamelCase						:Optional[int]						  =     old_model[int(a_)]
									else:
												lowerCamelCase						:Any						  =     getattr(a_						,			a_)

												if old_attribute == "":
															lowerCamelCase						:Tuple						  =     old_model
												else:
															if not hasattr(a_						,			a_):
																		raise ValueError(F"{old_model} does not have {old_attribute}")
															lowerCamelCase						:str						  =     getattr(a_						,			a_)

						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(a_)


if __name__ == "__main__":
			A__           =							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."""
			)
			A__           =							parser.parse_args()
			convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A__           =							{
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""LayoutLMv2TokenizerFast"""]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""LayoutLMv2FeatureExtractor"""]
			A__           =							["""LayoutLMv2ImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							[
			    """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """LayoutLMv2ForQuestionAnswering""",
			    """LayoutLMv2ForSequenceClassification""",
			    """LayoutLMv2ForTokenClassification""",
			    """LayoutLMv2Layer""",
			    """LayoutLMv2Model""",
			    """LayoutLMv2PreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
			from .processing_layoutlmva import LayoutLMvaProcessor
			from .tokenization_layoutlmva import LayoutLMvaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_layoutlmva import (
						    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LayoutLMvaForQuestionAnswering,
						    LayoutLMvaForSequenceClassification,
						    LayoutLMvaForTokenClassification,
						    LayoutLMvaLayer,
						    LayoutLMvaModel,
						    LayoutLMvaPreTrainedModel,
						)
else:
			import sys

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



