

"""simple docstring"""
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


_A						=     logging.get_logger(__name__)  # pylint: disable=invalid-name


_A						=     """lfs-multipart-upload"""

class 			lowerCamelCase  (					lowerCAmelCase__      ):


  '''simple docstring'''







  @staticmethod
  def    _a			(_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       List[str]						=						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=_lowerCamelCase       ,					help="""Local path to repository you want to configure."""  )
        enable_parser.set_defaults(func=lambda _lowerCamelCase  : LfsEnableCommand(_lowerCamelCase  )  )

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




class 			lowerCamelCase  :


  '''simple docstring'''







  def __init__(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       Dict						=						args




  def    _a			(self  ):


        """simple docstring"""

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




def 				a__	(	lowerCAmelCase						)  ->   Dict:
      UpperCAmelCase__ :       Optional[int]						=						json.dumps(lowerCAmelCase						) + """\n"""
      sys.stdout.write(lowerCAmelCase						)
      sys.stdout.flush()




def 				a__	(	)  ->   Optional[Dict]:
      UpperCAmelCase__ :       Any						=						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  (					lowerCAmelCase__      ):


  '''simple docstring'''







  def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       Union[str, Any]						=						filepath
        UpperCAmelCase__ :       str						=						seek_from
        UpperCAmelCase__ :       List[str]						=						read_limit
        UpperCAmelCase__ :       List[Any]						=						0



  def __enter__(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Tuple						=						open(self.filepath       ,					"""rb"""  )
        self.f.seek(self.seek_from  )
        return self



  def __len__(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Union[str, Any]						=						os.fstat(self.f.fileno()  ).st_size
        return min(self.read_limit       ,					total_length - self.seek_from  )



  def    _a			(self       ,					_lowerCamelCase=-1  ):


        """simple docstring"""

        if self.n_seen >= self.read_limit:
              return b""
        UpperCAmelCase__ :       List[Any]						=						self.read_limit - self.n_seen
        UpperCAmelCase__ :       Optional[Any]						=						self.f.read(remaining_amount if n < 0 else min(_lowerCamelCase       ,					_lowerCamelCase  )  )
        self.n_seen += len(_lowerCamelCase  )
        return data



  def __iter__(self  ):


        """simple docstring"""

        yield self.read(n=4 * 1024 * 1024  )




  def __exit__(self       ,					*_lowerCamelCase  ):


        """simple docstring"""

        self.f.close()




class 			lowerCamelCase  :


  '''simple docstring'''







  def __init__(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       str						=						args




  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Tuple						=						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:
              UpperCAmelCase__ :       List[str]						=						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  )

              UpperCAmelCase__ :       List[str]						=						msg["""oid"""]
              UpperCAmelCase__ :       Dict						=						msg["""path"""]
              UpperCAmelCase__ :       Dict						=						msg["""action"""]["""href"""]
              UpperCAmelCase__ :       List[Any]						=						msg["""action"""]["""header"""]
              UpperCAmelCase__ :       str						=						int(header.pop("""chunk_size"""  )  )
              UpperCAmelCase__ :       List[str]						=						list(header.values()  )

              UpperCAmelCase__ :       Tuple						=						[]
              for i, presigned_url in enumerate(_lowerCamelCase  ):
                    with FileSlice(_lowerCamelCase       ,					seek_from=i * chunk_size       ,					read_limit=_lowerCamelCase  ) as data:
                          UpperCAmelCase__ :       List[Any]						=						requests.put(_lowerCamelCase       ,					data=_lowerCamelCase  )
                          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.

              UpperCAmelCase__ :       List[Any]						=						requests.post(
                  _lowerCamelCase       ,					json={
                      """oid""": oid,
                      """parts""": parts,
                  }       ,					)
              r.raise_for_status()

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




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A						=     {
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""GPT2TokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     [
        """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """GPT2DoubleHeadsModel""",
        """GPT2ForQuestionAnswering""",
        """GPT2ForSequenceClassification""",
        """GPT2ForTokenClassification""",
        """GPT2LMHeadModel""",
        """GPT2Model""",
        """GPT2PreTrainedModel""",
        """load_tf_weights_in_gpt2""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     [
        """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFGPT2DoubleHeadsModel""",
        """TFGPT2ForSequenceClassification""",
        """TFGPT2LMHeadModel""",
        """TFGPT2MainLayer""",
        """TFGPT2Model""",
        """TFGPT2PreTrainedModel""",
    ]

try:
    if not is_keras_nlp_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""TFGPT2Tokenizer"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
    from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
    from .tokenization_gpta import GPTaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_fast import GPTaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpta import (
            GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTaDoubleHeadsModel,
            GPTaForQuestionAnswering,
            GPTaForSequenceClassification,
            GPTaForTokenClassification,
            GPTaLMHeadModel,
            GPTaModel,
            GPTaPreTrainedModel,
            load_tf_weights_in_gpta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_gpta import (
            TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFGPTaDoubleHeadsModel,
            TFGPTaForSequenceClassification,
            TFGPTaLMHeadModel,
            TFGPTaMainLayer,
            TFGPTaModel,
            TFGPTaPreTrainedModel,
        )

    try:
        if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_tf import TFGPTaTokenizer

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
    import sys

    _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

