
import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


__magic_name__  :  List[str]     			=   logging.get_logger(__name__)  # pylint: disable=invalid-name


__magic_name__  :  List[str]     			=   'lfs-multipart-upload'


class 							lowerCamelCase       (		__snake_case					):



  """simple docstring"""





  @staticmethod
  def 					lowercase_						(					__UpperCamelCase					):
      A_								=					parser.add_parser(
          "lfs-enable-largefiles"						,    help=(
              "Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."
          )						,    )
      enable_parser.add_argument("path"						,    type=__UpperCamelCase						,    help="Local path to repository you want to configure."					)
      enable_parser.set_defaults(func=lambda __UpperCamelCase					: LfsEnableCommand(__UpperCamelCase					)					)

      A_								=					parser.add_parser(
          __UpperCamelCase						,    help=(
              "Deprecated: use `huggingface-cli` instead. "
              "Command will get called by git-lfs, do not call it directly."
          )						,    )
      upload_parser.set_defaults(func=lambda __UpperCamelCase					: LfsUploadCommand(__UpperCamelCase					)					)



class 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase					):
      A_								=					args

  def 					lowercase_						(					self					):
      warnings.warn(
          "Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."					)
      A_								=					os.path.abspath(self.args.path					)
      if not os.path.isdir(__UpperCamelCase					):
          print("This does not look like a valid git repo."					)
          exit(1					)
      subprocess.run(
          "git config lfs.customtransfer.multipart.path transformers-cli".split()						,    check=__UpperCamelCase						,    cwd=__UpperCamelCase					)
      subprocess.run(
          f'git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'.split()						,    check=__UpperCamelCase						,    cwd=__UpperCamelCase						,    )
      print("Local repo set up for largefiles"					)





def        lowerCAmelCase       (				snake_case__   :      Dict )->			Tuple:
    A_								=					json.dumps(snake_case__ ) + "\n"
    sys.stdout.write(snake_case__ )
    sys.stdout.flush()





def        lowerCAmelCase       (				)->			Optional[Dict]:
    A_								=					json.loads(sys.stdin.readline().strip() )

    if "terminate" in (msg.get("type" ), msg.get("event" )):
        # terminate message received
        return None

    if msg.get("event" ) not in ("download", "upload"):
        logger.critical("Received unexpected message" )
        sys.exit(1 )

    return msg


class 							lowerCamelCase       (		__snake_case					):



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					filepath
      A_								=					seek_from
      A_								=					read_limit
      A_								=					0

  def __enter__(					self					):
      A_								=					open(self.filepath						,    "rb"					)
      self.f.seek(self.seek_from					)
      return self

  def __len__(					self					):
      A_								=					os.fstat(self.f.fileno()					).st_size
      return min(self.read_limit						,    total_length - self.seek_from					)

  def 					lowercase_						(					self						,    __UpperCamelCase=-1					):
      if self.n_seen >= self.read_limit:
          return b""
      A_								=					self.read_limit - self.n_seen
      A_								=					self.f.read(remaining_amount if n < 0 else min(__UpperCamelCase						,    __UpperCamelCase					)					)
      self.n_seen += len(__UpperCamelCase					)
      return data

  def __iter__(					self					):
      yield self.read(n=4 * 1024 * 1024					)

  def __exit__(					self						,    *__UpperCamelCase					):
      self.f.close()



class 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase					):
      A_								=					args

  def 					lowercase_						(					self					):
      # Immediately after invoking a custom transfer process, git-lfs
      # sends initiation data to the process over stdin.
      # This tells the process useful information about the configuration.
      A_								=					json.loads(sys.stdin.readline().strip()					)
      if not (init_msg.get("event"					) == "init" and init_msg.get("operation"					) == "upload"):
          write_msg({"error": {"code": 32, "message": "Wrong lfs init operation"}}					)
          sys.exit(1					)

      # The transfer process should use the information it needs from the
      # initiation structure, and also perform any one-off setup tasks it
      # needs to do. It should then respond on stdout with a simple empty
      # confirmation structure, as follows:
      write_msg({}					)

      # After the initiation exchange, git-lfs will send any number of
      # transfer requests to the stdin of the transfer process, in a serial sequence.
      while True:
          A_								=					read_msg()
          if msg is None:
              # When all transfers have been processed, git-lfs will send
              # a terminate event to the stdin of the transfer process.
              # On receiving this message the transfer process should
              # clean up and terminate. No response is expected.
              sys.exit(0					)

          A_								=					msg["oid"]
          A_								=					msg["path"]
          A_								=					msg["action"]["href"]
          A_								=					msg["action"]["header"]
          A_								=					int(header.pop("chunk_size"					)					)
          A_								=					list(header.values()					)

          A_								=					[]
          for i, presigned_url in enumerate(__UpperCamelCase					):
              with FileSlice(__UpperCamelCase						,    seek_from=i * chunk_size						,    read_limit=__UpperCamelCase					) as data:
                  A_								=					requests.put(__UpperCamelCase						,    data=__UpperCamelCase					)
                  r.raise_for_status()
                  parts.append(
                      {
                          "etag": r.headers.get("etag"					),
                          "partNumber": i + 1,
                      }					)
                  # In order to support progress reporting while data is uploading / downloading,
                  # the transfer process should post messages to stdout
                  write_msg(
                      {
                          "event": "progress",
                          "oid": oid,
                          "bytesSoFar": (i + 1) * chunk_size,
                          "bytesSinceLast": chunk_size,
                      }					)
                  # Not precise but that's ok.

          A_								=					requests.post(
              __UpperCamelCase						,    json={
                  "oid": oid,
                  "parts": parts,
              }						,    )
          r.raise_for_status()

          write_msg({"event": "complete", "oid": oid}					)








import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			List[str]:
    A_								=					[
        "encoder.version",
        "decoder.version",
        "model.encoder.version",
        "model.decoder.version",
        "_float_tensor",
        "decoder.output_projection.weight",
    ]
    for k in ignore_keys:
        state_dict.pop(snake_case__				,					snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    A_	,	A_								=					emb.weight.shape
    A_								=					nn.Linear(snake_case__				,					snake_case__				,					bias=snake_case__ )
    A_								=					emb.weight.data
    return lin_layer





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      Optional[int]="uclanlp/plbart-base"				,					snake_case__   :      Any=False				,					snake_case__   :      str=False )->			str:
    A_								=					torch.load(snake_case__				,					map_location="cpu" )["model"]
    remove_ignore_keys_(snake_case__ )
    A_								=					state_dict["encoder.embed_tokens.weight"].shape[0]

    A_								=					PLBartConfig.from_pretrained(snake_case__				,					vocab_size=snake_case__ )

    A_								=					state_dict["decoder.embed_tokens.weight"]
    if not classification:
        A_								=					PLBartForConditionalGeneration(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        if finetuned:
            A_								=					make_linear_from_emb(model.model.shared )

    else:
        A_								=					{}
        for key, value in state_dict.copy().items():
            if key.startswith("classification_heads.sentence_classification_head" ):
                A_								=					value
                state_dict.pop(snake_case__ )
        A_								=					PLBartForSequenceClassification(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        model.classification_head.load_state_dict(snake_case__ )

    return model


if __name__ == "__main__":
     __magic_name__  :  int     			=   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='model.pt on local filesystem.')
     parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument(
         '--hf_config',
         default='uclanlp/plbart-base',
         type=str,
         help='Which huggingface architecture to use: plbart-base',
     )
     parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
     parser.add_argument(
         '--classification', action='store_true', help='whether the model is a classification checkpoint'
     )
     __magic_name__  :  Union[str, Any]     			=   parser.parse_args()
     __magic_name__  :  List[str]     			=   convert_fairseq_plbart_checkpoint_from_disk(
         args.fairseq_path,
         hf_config_path=args.hf_config,
         finetuned=args.finetuned,
         classification=args.classification,
     )
     model.save_pretrained(args.pytorch_dump_folder_path)






