

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def   __UpperCAmelCase					(      __a :     str						,__a :     Optional[int]      )     ->	str:

					"""simple docstring"""

					assert isinstance(__a						,__a      )
					assert dataset.num_rows == 4
					assert dataset.num_columns == 1
					assert dataset.column_names == ["text"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('''keep_in_memory'''						,[False, True]      )
def   __UpperCAmelCase					(      __a :     Any						,__a :     Union[str, Any]						,__a :     int      )     ->	Tuple:

					"""simple docstring"""

					_a      :       str   =						tmp_path / '''cache'''
					_a      :       List[Any]   =						{'''text''': '''string'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										_a      :       List[Any]   =						TextDatasetReader(__a						,cache_dir=__a						,keep_in_memory=__a      ).read()
					_check_text_dataset(__a						,__a      )



@pytest.mark.parametrize(
    '''features'''						,[
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]						,)
def   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     Optional[Any]						,__a :     int      )     ->	List[str]:

					"""simple docstring"""

					_a      :       Dict   =						tmp_path / '''cache'''
					_a      :       Optional[Any]   =						{'''text''': '''string'''}
					_a      :       Optional[Any]   =						features.copy() if features else default_expected_features
					_a      :       Tuple   =						(
					    Features({feature: Value(__a      ) for feature, dtype in features.items()}      ) if features is not None else None
					)
					_a      :       List[str]   =						TextDatasetReader(__a						,features=__a						,cache_dir=__a      ).read()
					_check_text_dataset(__a						,__a      )



@pytest.mark.parametrize('''split'''						,[None, NamedSplit('''train'''      ), '''train''', '''test''']      )
def   __UpperCAmelCase					(      __a :     str						,__a :     int						,__a :     List[str]      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       Any   =						tmp_path / '''cache'''
					_a      :       List[str]   =						{'''text''': '''string'''}
					_a      :       str   =						TextDatasetReader(__a						,cache_dir=__a						,split=__a      ).read()
					_check_text_dataset(__a						,__a      )
					assert dataset.split == split if split else "train"



@pytest.mark.parametrize('''path_type'''						,[str, list]      )
def   __UpperCAmelCase					(      __a :     int						,__a :     int						,__a :     str      )     ->	Optional[Any]:

					"""simple docstring"""

					if issubclass(__a						,__a      ):
										_a      :       Tuple   =						text_path
					elif issubclass(__a						,__a      ):
										_a      :       Tuple   =						[text_path]
					_a      :       Union[str, Any]   =						tmp_path / '''cache'''
					_a      :       Any   =						{'''text''': '''string'''}
					_a      :       int   =						TextDatasetReader(__a						,cache_dir=__a      ).read()
					_check_text_dataset(__a						,__a      )



def   __UpperCAmelCase					(      __a :     Tuple						,__a :     Union[str, Any]						,__a :     Optional[int]=("train",)      )     ->	List[Any]:

					"""simple docstring"""

					assert isinstance(__a						,__a      )
					for split in splits:
										_a      :       Union[str, Any]   =						dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 1
										assert dataset.column_names == ["text"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('''keep_in_memory'''						,[False, True]      )
def   __UpperCAmelCase					(      __a :     int						,__a :     int						,__a :     Optional[Any]      )     ->	Any:

					"""simple docstring"""

					_a      :       List[str]   =						tmp_path / '''cache'''
					_a      :       List[str]   =						{'''text''': '''string'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										_a      :       Union[str, Any]   =						TextDatasetReader({'''train''': text_path}						,cache_dir=__a						,keep_in_memory=__a      ).read()
					_check_text_datasetdict(__a						,__a      )



@pytest.mark.parametrize(
    '''features'''						,[
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]						,)
def   __UpperCAmelCase					(      __a :     Dict						,__a :     List[str]						,__a :     Optional[int]      )     ->	Tuple:

					"""simple docstring"""

					_a      :       Optional[Any]   =						tmp_path / '''cache'''
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					_a      :       Optional[Any]   =						{'''text''': '''string'''}
					_a      :       str   =						features.copy() if features else default_expected_features
					_a      :       Dict   =						(
					    Features({feature: Value(__a      ) for feature, dtype in features.items()}      ) if features is not None else None
					)
					_a      :       Union[str, Any]   =						TextDatasetReader({'''train''': text_path}						,features=__a						,cache_dir=__a      ).read()
					_check_text_datasetdict(__a						,__a      )







@pytest.mark.parametrize('''split'''						,[None, NamedSplit('''train'''      ), '''train''', '''test''']      )
def   __UpperCAmelCase					(      __a :     int						,__a :     int						,__a :     Dict      )     ->	Dict:

					"""simple docstring"""

					if split:
										_a      :       Union[str, Any]   =						{split: text_path}
					else:
										_a      :       Any   =						'''train'''
										_a      :       Any   =						{'''train''': text_path, '''test''': text_path}
					_a      :       List[str]   =						tmp_path / '''cache'''
					_a      :       Union[str, Any]   =						{'''text''': '''string'''}
					_a      :       Tuple   =						TextDatasetReader(__a						,cache_dir=__a      ).read()
					_check_text_datasetdict(__a						,__a						,splits=list(path.keys()      )      )
					assert all(dataset[split].split == split for split in path.keys()      )








'''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_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			if "xprophetnet" in prophetnet_checkpoint_path:
						snake_case:    Optional[Any]      							=XLMProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase					)
						snake_case							,  snake_case:    str      							=XLMProphetNetForConditionalGeneration.from_pretrained(
						    __UpperCAmelCase				,							output_loading_info=__UpperCAmelCase					)
			else:
						snake_case:    Optional[int]      							=ProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase					)
						snake_case							,  snake_case:    List[str]      							=ProphetNetForConditionalGeneration.from_pretrained(
						    __UpperCAmelCase				,							output_loading_info=__UpperCAmelCase					)

			snake_case:    str      							=['key_proj', 'value_proj', 'query_proj']

			snake_case:    str      							={
			    '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:    List[str]      							=key.split('.'					)

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

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

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

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

									if attribute.isdigit():
												snake_case:    int      							=model[int(__UpperCAmelCase					)]
												snake_case:    Optional[int]      							=old_model[int(__UpperCAmelCase					)]
									else:
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

												if old_attribute == "":
															snake_case:    Union[str, Any]      							=old_model
												else:
															if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
																		raise ValueError(f'''{old_model} does not have {old_attribute}'''					)
															snake_case:    List[Any]      							=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__":
	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)




