



"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__      =    logging.get_logger(__name__)

lowerCAmelCase__      =    {
    '''sail/poolformer_s12''': '''https://huggingface.co/sail/poolformer_s12/resolve/main/config.json''',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}






class 	__snake_case	(     __SCREAMING_SNAKE_CASE):
  snake_case__    :       Any        =						"poolformer"




  def __init__(			self					:						List[str]						, __lowerCAmelCase					:						List[Any]=3						, __lowerCAmelCase					:						List[str]=1_6						, __lowerCAmelCase					:						int=1_6						, __lowerCAmelCase					:						str=3						, __lowerCAmelCase					:						List[str]=4.0						, __lowerCAmelCase					:						List[str]=[2, 2, 6, 2]						, __lowerCAmelCase					:						int=[6_4, 1_2_8, 3_2_0, 5_1_2]						, __lowerCAmelCase					:						str=[7, 3, 3, 3]						, __lowerCAmelCase					:						List[str]=[4, 2, 2, 2]						, __lowerCAmelCase					:						Union[str, Any]=[2, 1, 1, 1]						, __lowerCAmelCase					:						str=4						, __lowerCAmelCase					:						int=0.0						, __lowerCAmelCase					:						Dict="gelu"						, __lowerCAmelCase					:						Dict=True						, __lowerCAmelCase					:						int=1E-5						, __lowerCAmelCase					:						List[Any]=0.02						, **__lowerCAmelCase					:						Union[str, Any]						, ):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			num_channels
         _lowerCamelCase						:    List[str]         =			patch_size
         _lowerCamelCase						:    List[str]         =			stride
         _lowerCamelCase						:    Dict         =			padding
         _lowerCamelCase						:    Tuple         =			pool_size
         _lowerCamelCase						:    Optional[Any]         =			hidden_sizes
         _lowerCamelCase						:    List[Any]         =			mlp_ratio
         _lowerCamelCase						:    List[str]         =			depths
         _lowerCamelCase						:    Union[str, Any]         =			patch_sizes
         _lowerCamelCase						:    Dict         =			strides
         _lowerCamelCase						:    Dict         =			num_encoder_blocks
         _lowerCamelCase						:    Any         =			drop_path_rate
         _lowerCamelCase						:    str         =			hidden_act
         _lowerCamelCase						:    Optional[Any]         =			use_layer_scale
         _lowerCamelCase						:    Tuple         =			layer_scale_init_value
         _lowerCamelCase						:    Union[str, Any]         =			initializer_range
         super().__init__(**__UpperCAmelCase		)






class 	__snake_case	(     __SCREAMING_SNAKE_CASE):
  snake_case__    :       List[str]        =						version.parse("1.11")




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]		)




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         return 2E-3






"""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


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






def 	snake_case_  (				A_ :		str,    A_ :		str						):


       '''simple docstring'''





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

       _lowerCamelCase						:    Optional[Any]         =			['''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						:    Union[str, Any]         =			key.split('''.'''						)

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

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

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

                            elif attribute == "key_proj":
                                   _lowerCamelCase						:    int         =			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]						)
                                   _lowerCamelCase						:    str         =			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]						)
                            elif attribute == "value_proj":
                                   _lowerCamelCase						:    Tuple         =			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]						)
                                   _lowerCamelCase						:    str         =			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]						)
                            _lowerCamelCase						:    Dict         =			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						:    List[str]         =			nn.Parameter(old_model.embed_positions.weight[:5_12, :]						)
                            _lowerCamelCase						:    Optional[Any]         =			True
                            break

                     if attribute.isdigit():
                            _lowerCamelCase						:    Optional[int]         =			model[int(A_						)]
                            _lowerCamelCase						:    List[Any]         =			old_model[int(A_						)]
                     else:
                            _lowerCamelCase						:    List[str]         =			getattr(A_,    A_						)

                            if old_attribute == "":
                                   _lowerCamelCase						:    str         =			old_model
                            else:
                                   if not hasattr(A_,    A_						):
                                          raise ValueError(F'''{old_model} does not have {old_attribute}'''						)
                                   _lowerCamelCase						:    Optional[int]         =			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__":
   lowerCAmelCase__      =    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.'''
   )
   lowerCAmelCase__      =    parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


