# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available



def lowerCamelCase  (       SCREAMING_SNAKE_CASE=None     ):
    '''simple docstring'''







    if subparsers is not None:
        __UpperCamelCase					:List[Any]        =		subparsers.add_parser('''env'''     )
    else:
        __UpperCamelCase					:List[str]        =		argparse.ArgumentParser('''Accelerate env command'''     )

    parser.add_argument(
        '''--config_file'''  ,      default=SCREAMING_SNAKE_CASE  ,      help='''The config file to use for the default values in the launching script.'''     )

    if subparsers is not None:
        parser.set_defaults(func=SCREAMING_SNAKE_CASE     )
    return parser



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







    __UpperCamelCase					:Tuple        =		torch.__version__
    __UpperCamelCase					:Optional[int]        =		torch.cuda.is_available()
    __UpperCamelCase					:Tuple        =		is_xpu_available()
    __UpperCamelCase					:Optional[Any]        =		is_npu_available()

    __UpperCamelCase					:int        =		'''Not found'''
    # Get the default from the config file.
    if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase					:List[str]        =		load_config_from_file(args.config_file     ).to_dict()

    __UpperCamelCase					:List[str]        =		{
        '''`Accelerate` version''': version,
        '''Platform''': platform.platform(),
        '''Python version''': platform.python_version(),
        '''Numpy version''': np.__version__,
        '''PyTorch version (GPU?)''': f"""{pt_version} ({pt_cuda_available})""",
        '''PyTorch XPU available''': str(SCREAMING_SNAKE_CASE     ),
        '''PyTorch NPU available''': str(SCREAMING_SNAKE_CASE     ),
        '''System RAM''': f"""{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB""",
    }
    if pt_cuda_available:
        __UpperCamelCase					:Optional[Any]        =		torch.cuda.get_device_name()

    print('''\nCopy-and-paste the text below in your GitHub issue\n'''     )
    print('''\n'''.join([f"""- {prop}: {val}""" for prop, val in info.items()]     )     )

    print('''- `Accelerate` default config:''' if args.config_file is None else '''- `Accelerate` config passed:'''     )
    __UpperCamelCase					:str        =		(
        '''\n'''.join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]     )
        if isinstance(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
        else f"""\t{accelerate_config}"""
    )
    print(SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Tuple        =		accelerate_config

    return info



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







    __UpperCamelCase					:Union[str, Any]        =		env_command_parser()
    __UpperCamelCase					:List[Any]        =		parser.parse_args()
    env_command(SCREAMING_SNAKE_CASE     )
    return 0


if __name__ == "__main__":
    raise SystemExit(main())






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)





