





"""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


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

def      __A   (      a_    :str ,							a_    :str)		->		Dict:
				if "xprophetnet" in prophetnet_checkpoint_path:
								__a			:    str      							=	XLMProphetNetForConditionalGenerationOld.from_pretrained(a_)
								__a							,    __a			:    int      							=	XLMProphetNetForConditionalGeneration.from_pretrained(
								    a_ ,							output_loading_info=a_)
				else:
								__a			:    Union[str, Any]      							=	ProphetNetForConditionalGenerationOld.from_pretrained(a_)
								__a							,    __a			:    Dict      							=	ProphetNetForConditionalGeneration.from_pretrained(
								    a_ ,							output_loading_info=a_)

				__a			:    Optional[Any]      							=	['''key_proj''', '''value_proj''', '''query_proj''']

				__a			:    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"]:
								__a			:    List[str]      							=	key.split('''.''')

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

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

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

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

												if attribute.isdigit():
																__a			:    Optional[int]      							=	model[int(a_)]
																__a			:    List[str]      							=	old_model[int(a_)]
												else:
																__a			:    Dict      							=	getattr(a_ ,							a_)

																if old_attribute == "":
																				__a			:    Dict      							=	old_model
																else:
																				if not hasattr(a_ ,							a_):
																								raise ValueError(F"""{old_model} does not have {old_attribute}""")
																				__a			:    Any      							=	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)







import warnings
from typing import List, Optional, Union

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



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				["""image_processor""", """tokenizer"""]
      snake_case_	=				"""FlavaImageProcessor"""
      snake_case_	=				("""BertTokenizer""", """BertTokenizerFast""")


      def __init__(						self       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    **__UpperCamelCase						)->						Tuple:
           __lowerCAmelCase             =       None
           if "feature_extractor" in kwargs:
                warnings.warn(
                    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                    " instead."       ,    __UpperCamelCase       ,    )
                __lowerCAmelCase             =       kwargs.pop("feature_extractor"						)

           __lowerCAmelCase             =       image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                raise ValueError("You need to specify an `image_processor`."						)
           if tokenizer is None:
                raise ValueError("You need to specify a `tokenizer`."						)

           super().__init__(__UpperCamelCase       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       self.image_processor


      def __call__(						self       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = True       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = None       ,    __UpperCamelCase = 0       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = True       ,    __UpperCamelCase = None       ,    **__UpperCamelCase       ,    )->						int:

           if text is None and images is None:
                raise ValueError("You have to specify either text or images. Both cannot be none."						)

           if text is not None:
                __lowerCAmelCase             =       self.tokenizer(
                    text=__UpperCamelCase       ,    add_special_tokens=__UpperCamelCase       ,    padding=__UpperCamelCase       ,    truncation=__UpperCamelCase       ,    max_length=__UpperCamelCase       ,    stride=__UpperCamelCase       ,    pad_to_multiple_of=__UpperCamelCase       ,    return_token_type_ids=__UpperCamelCase       ,    return_attention_mask=__UpperCamelCase       ,    return_overflowing_tokens=__UpperCamelCase       ,    return_special_tokens_mask=__UpperCamelCase       ,    return_offsets_mapping=__UpperCamelCase       ,    return_length=__UpperCamelCase       ,    verbose=__UpperCamelCase       ,    return_tensors=__UpperCamelCase       ,    **__UpperCamelCase       ,    )
           if images is not None:
                __lowerCAmelCase             =       self.image_processor(
                    __UpperCamelCase       ,    return_image_mask=__UpperCamelCase       ,    return_codebook_pixels=__UpperCamelCase       ,    return_tensors=__UpperCamelCase       ,    **__UpperCamelCase       ,    )

           if text is not None and images is not None:
                encoding.update(__UpperCamelCase						)
                return encoding
           elif text is not None:
                return encoding
           else:
                return BatchEncoding(data=dict(**__UpperCamelCase						)       ,    tensor_type=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						int:
           return self.tokenizer.batch_decode(*__UpperCamelCase       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						Optional[int]:
           return self.tokenizer.decode(*__UpperCamelCase       ,    **__UpperCamelCase						)


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


      @property
      def 			__UpperCAmelCase		(						self						)->						Dict:
           warnings.warn(
               "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."       ,    __UpperCamelCase       ,    )
           return self.image_processor_class


      @property
      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           warnings.warn(
               "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,    __UpperCamelCase       ,    )
           return self.image_processor






