

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


SCREAMING_SNAKE_CASE		:List[Any]						=     logging.get_logger(__name__)
logging.set_verbosity_info()




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Any:



    """simple docstring"""






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

    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(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			) and len(SCREAMING_SNAKE_CASE_			) > 0:
                    UpperCamelCase_			  =						attribute
            elif hasattr(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
                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(SCREAMING_SNAKE_CASE_			,						"in_proj_weight"			):
                UpperCamelCase_			  =						old_model.in_proj_weight.shape[0] // 3
                UpperCamelCase_			  =						getattr(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
                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] == 5_1_2, "We want 512 position_embeddings."
                UpperCamelCase_			  =						nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]			)
                UpperCamelCase_			  =						True
                break

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

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

        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(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:int						=     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."""
 )
 SCREAMING_SNAKE_CASE		:List[Any]						=     parser.parse_args()
 convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      ["""image_processor""", """tokenizer"""]
     UpperCamelCase_	:str										=      """BridgeTowerImageProcessor"""
     UpperCamelCase_	:Tuple										=      ("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(				self		,						_lowercase		,						_lowercase	)->     Any:
         super().__init__(_lowercase		,						_lowercase	)






     def __call__(				self		,						_lowercase		,						_lowercase = None		,						_lowercase = True		,						_lowercase = False		,						_lowercase = None		,						_lowercase = None		,						_lowercase = 0		,						_lowercase = None		,						_lowercase = None		,						_lowercase = None		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = True		,						_lowercase = None		,						**_lowercase		,						)->     BatchEncoding:
         UpperCamelCase_			  =						self.tokenizer(
             text=_lowercase		,						add_special_tokens=_lowercase		,						padding=_lowercase		,						truncation=_lowercase		,						max_length=_lowercase		,						stride=_lowercase		,						pad_to_multiple_of=_lowercase		,						return_token_type_ids=_lowercase		,						return_attention_mask=_lowercase		,						return_overflowing_tokens=_lowercase		,						return_special_tokens_mask=_lowercase		,						return_offsets_mapping=_lowercase		,						return_length=_lowercase		,						verbose=_lowercase		,						return_tensors=_lowercase		,						**_lowercase		,						)
         # add pixel_values + pixel_mask
         UpperCamelCase_			  =						self.image_processor(
             _lowercase		,						return_tensors=_lowercase		,						do_normalize=_lowercase		,						do_center_crop=_lowercase		,						**_lowercase	)
         encoding.update(_lowercase	)

         return encoding






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     Union[str, Any]:
         return self.tokenizer.batch_decode(*_lowercase		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     List[Any]:
         return self.tokenizer.decode(*_lowercase		,						**_lowercase	)






     @property
     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						self.tokenizer.model_input_names
         UpperCamelCase_			  =						self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
