import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


__lowercase										= '''<<<<<<< This should probably be modified because it mentions: '''

__lowercase										= '''=======
>>>>>>>
'''

__lowercase										= [
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

__lowercase										= [
    # (pattern, replacement)
    # Order is important here for some replacements
    (r'''tfds\.core''', r'''datasets'''),
    (r'''tf\.io\.gfile\.GFile''', r'''open'''),
    (r'''tf\.([\w\d]+)''', r'''datasets.Value(\'\1\')'''),
    (r'''tfds\.features\.Text\(\)''', r'''datasets.Value(\'string\')'''),
    (r'''tfds\.features\.Text\(''', r'''datasets.Value(\'string\'),'''),
    (r'''features\s*=\s*tfds.features.FeaturesDict\(''', r'''features=datasets.Features('''),
    (r'''tfds\.features\.FeaturesDict\(''', r'''dict('''),
    (r'''The TensorFlow Datasets Authors''', r'''The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'''),
    (r'''tfds\.''', r'''datasets.'''),
    (r'''dl_manager\.manual_dir''', r'''self.config.data_dir'''),
    (r'''self\.builder_config''', r'''self.config'''),
]



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    return ConvertCommand(args.tfds_path  ,      args.datasets_directory     )


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    @staticmethod
    def       UpperCamelCase__       (     __lowercase)	->     Optional[Any]:
        __UpperCamelCase					:Union[str, Any]        =		parser.add_parser(
            '''convert'''			,			help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''			,			)
        train_parser.add_argument(
            '''--tfds_path'''			,			type=__lowercase			,			required=__lowercase			,			help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''			,			)
        train_parser.add_argument(
            '''--datasets_directory'''			,			type=__lowercase			,			required=__lowercase			,			help='''Path to the HuggingFace Datasets folder.''')
        train_parser.set_defaults(func=__lowercase)







    def __init__(     self			,			__lowercase			,			__lowercase			,			*__lowercase)	->     str:
        __UpperCamelCase					:Any        =		get_logger('''datasets-cli/converting''')

        __UpperCamelCase					:Any        =		tfds_path
        __UpperCamelCase					:List[Any]        =		datasets_directory







    def       UpperCamelCase__       (     self)	->     List[str]:
        if os.path.isdir(self._tfds_path):
            __UpperCamelCase					:Any        =		os.path.abspath(self._tfds_path)
        elif os.path.isfile(self._tfds_path):
            __UpperCamelCase					:Tuple        =		os.path.dirname(self._tfds_path)
        else:
            raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.''')

        __UpperCamelCase					:Tuple        =		os.path.abspath(self._datasets_directory)

        self._logger.info(f"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}""")

        __UpperCamelCase					:str        =		[]
        __UpperCamelCase					:Dict        =		[]
        __UpperCamelCase					:Optional[int]        =		{}

        if os.path.isdir(self._tfds_path):
            __UpperCamelCase					:Tuple        =		os.listdir(__lowercase)
        else:
            __UpperCamelCase					:Union[str, Any]        =		[os.path.basename(self._tfds_path)]

        for f_name in file_names:
            self._logger.info(f"""Looking at file {f_name}""")
            __UpperCamelCase					:List[str]        =		os.path.join(__lowercase			,			__lowercase)
            __UpperCamelCase					:Optional[Any]        =		os.path.join(__lowercase			,			__lowercase)

            if not os.path.isfile(__lowercase) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
                self._logger.info('''Skipping file''')
                continue

            with open(__lowercase			,			encoding='''utf-8''') as f:
                __UpperCamelCase					:Tuple        =		f.readlines()

            __UpperCamelCase					:Optional[Any]        =		[]
            __UpperCamelCase					:Optional[Any]        =		False
            __UpperCamelCase					:str        =		False
            __UpperCamelCase					:Any        =		[]
            for line in lines:
                __UpperCamelCase					:Union[str, Any]        =		line

                # Convert imports
                if "import tensorflow.compat.v2 as tf" in out_line:
                    continue
                elif "@tfds.core" in out_line:
                    continue
                elif "builder=self" in out_line:
                    continue
                elif "import tensorflow_datasets.public_api as tfds" in out_line:
                    __UpperCamelCase					:Dict        =		'''import datasets\n'''
                elif "import tensorflow" in out_line:
                    # order is important here
                    __UpperCamelCase					:Union[str, Any]        =		''''''
                    continue
                elif "from absl import logging" in out_line:
                    __UpperCamelCase					:Optional[Any]        =		'''from datasets import logging\n'''
                elif "getLogger" in out_line:
                    __UpperCamelCase					:List[str]        =		out_line.replace('''getLogger'''			,			'''get_logger''')
                elif any(expression in out_line for expression in TO_HIGHLIGHT):
                    __UpperCamelCase					:List[Any]        =		True
                    __UpperCamelCase					:List[Any]        =		list(filter(lambda __lowercase: e in out_line			,			__lowercase))
                    out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__lowercase) + '''\n''')
                    out_lines.append(__lowercase)
                    out_lines.append(__lowercase)
                    continue
                else:
                    for pattern, replacement in TO_CONVERT:
                        __UpperCamelCase					:Dict        =		re.sub(__lowercase			,			__lowercase			,			__lowercase)

                # Take care of saving utilities (to later move them together with main script)
                if "tensorflow_datasets" in out_line:
                    __UpperCamelCase					:Optional[Any]        =		re.match(r'''from\stensorflow_datasets.*import\s([^\.\r\n]+)'''			,			__lowercase)
                    tfds_imports.extend(imp.strip() for imp in match.group(1).split(''','''))
                    __UpperCamelCase					:Tuple        =		'''from . import ''' + match.group(1)

                # Check we have not forget anything
                if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
                    raise ValueError(f"""Error converting {out_line.strip()}""")

                if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
                    __UpperCamelCase					:int        =		True
                out_lines.append(__lowercase)

            if is_builder or "wmt" in f_name:
                # We create a new directory for each dataset
                __UpperCamelCase					:int        =		f_name.replace('''.py'''			,			'''''')
                __UpperCamelCase					:Tuple        =		os.path.join(__lowercase			,			__lowercase)
                __UpperCamelCase					:Tuple        =		os.path.join(__lowercase			,			__lowercase)
                os.makedirs(__lowercase			,			exist_ok=__lowercase)
                self._logger.info(f"""Adding directory {output_dir}""")
                imports_to_builder_map.update({imp: output_dir for imp in tfds_imports})
            else:
                # Utilities will be moved at the end
                utils_files.append(__lowercase)

            if needs_manual_update:
                with_manual_update.append(__lowercase)

            with open(__lowercase			,			'''w'''			,			encoding='''utf-8''') as f:
                f.writelines(__lowercase)
            self._logger.info(f"""Converted in {output_file}""")

        for utils_file in utils_files:
            try:
                __UpperCamelCase					:Union[str, Any]        =		os.path.basename(__lowercase)
                __UpperCamelCase					:int        =		imports_to_builder_map[f_name.replace('''.py'''			,			'''''')]
                self._logger.info(f"""Moving {dest_folder} to {utils_file}""")
                shutil.copy(__lowercase			,			__lowercase)
            except KeyError:
                self._logger.error(f"""Cannot find destination folder for {utils_file}. Please copy manually.""")

        if with_manual_update:
            for file_path in with_manual_update:
                self._logger.warning(
                    f"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'.""")






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


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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if "xprophetnet" in prophetnet_checkpoint_path:
        __UpperCamelCase					:Dict        =		XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE     )
        __UpperCamelCase  ,	__UpperCamelCase					:int        =		XLMProphetNetForConditionalGeneration.from_pretrained(
            SCREAMING_SNAKE_CASE  ,      output_loading_info=SCREAMING_SNAKE_CASE     )
    else:
        __UpperCamelCase					:Union[str, Any]        =		ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE     )
        __UpperCamelCase  ,	__UpperCamelCase					:Union[str, Any]        =		ProphetNetForConditionalGeneration.from_pretrained(
            SCREAMING_SNAKE_CASE  ,      output_loading_info=SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Dict        =		['''key_proj''', '''value_proj''', '''query_proj''']

    __UpperCamelCase					:Optional[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"]:
        __UpperCamelCase					:Tuple        =		key.split('''.'''     )

        if attributes[0] == "lm_head":
            __UpperCamelCase					:Union[str, Any]        =		prophet
            __UpperCamelCase					:Any        =		prophet_old
        else:
            __UpperCamelCase					:Any        =		prophet.prophetnet
            __UpperCamelCase					:int        =		prophet_old.model

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

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

                elif attribute == "key_proj":
                    __UpperCamelCase					:List[Any]        =		nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]     )
                    __UpperCamelCase					:Dict        =		nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]     )
                elif attribute == "value_proj":
                    __UpperCamelCase					:Tuple        =		nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]     )
                    __UpperCamelCase					:Dict        =		nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]     )
                __UpperCamelCase					:Optional[int]        =		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] == 512, "We want 512 position_embeddings."
                __UpperCamelCase					:Optional[int]        =		nn.Parameter(old_model.embed_positions.weight[:512, :]     )
                __UpperCamelCase					:List[Any]        =		True
                break

            if attribute.isdigit():
                __UpperCamelCase					:List[Any]        =		model[int(SCREAMING_SNAKE_CASE     )]
                __UpperCamelCase					:Optional[int]        =		old_model[int(SCREAMING_SNAKE_CASE     )]
            else:
                __UpperCamelCase					:Optional[Any]        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

                if old_attribute == "":
                    __UpperCamelCase					:Any        =		old_model
                else:
                    if not hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
                        raise ValueError(f"""{old_model} does not have {old_attribute}"""     )
                    __UpperCamelCase					:Any        =		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__":
    __lowercase										= 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.'''
    )
    __lowercase										= parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





