

import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


lowerCAmelCase       :				Optional[Any]         =					logging.get_logger(__name__)

lowerCAmelCase       :				Tuple         =					OrderedDict(
    [
        ('align', 'AlignProcessor'),
        ('altclip', 'AltCLIPProcessor'),
        ('bark', 'BarkProcessor'),
        ('blip', 'BlipProcessor'),
        ('blip-2', 'Blip2Processor'),
        ('bridgetower', 'BridgeTowerProcessor'),
        ('chinese_clip', 'ChineseCLIPProcessor'),
        ('clap', 'ClapProcessor'),
        ('clip', 'CLIPProcessor'),
        ('clipseg', 'CLIPSegProcessor'),
        ('flava', 'FlavaProcessor'),
        ('git', 'GitProcessor'),
        ('groupvit', 'CLIPProcessor'),
        ('hubert', 'Wav2Vec2Processor'),
        ('instructblip', 'InstructBlipProcessor'),
        ('layoutlmv2', 'LayoutLMv2Processor'),
        ('layoutlmv3', 'LayoutLMv3Processor'),
        ('markuplm', 'MarkupLMProcessor'),
        ('mctct', 'MCTCTProcessor'),
        ('mgp-str', 'MgpstrProcessor'),
        ('oneformer', 'OneFormerProcessor'),
        ('owlvit', 'OwlViTProcessor'),
        ('pix2struct', 'Pix2StructProcessor'),
        ('sam', 'SamProcessor'),
        ('sew', 'Wav2Vec2Processor'),
        ('sew-d', 'Wav2Vec2Processor'),
        ('speech_to_text', 'Speech2TextProcessor'),
        ('speech_to_text_2', 'Speech2Text2Processor'),
        ('speecht5', 'SpeechT5Processor'),
        ('trocr', 'TrOCRProcessor'),
        ('tvlt', 'TvltProcessor'),
        ('unispeech', 'Wav2Vec2Processor'),
        ('unispeech-sat', 'Wav2Vec2Processor'),
        ('vilt', 'ViltProcessor'),
        ('vision-text-dual-encoder', 'VisionTextDualEncoderProcessor'),
        ('wav2vec2', 'Wav2Vec2Processor'),
        ('wav2vec2-conformer', 'Wav2Vec2Processor'),
        ('wavlm', 'Wav2Vec2Processor'),
        ('whisper', 'WhisperProcessor'),
        ('xclip', 'XCLIPProcessor'),
    ]
)

lowerCAmelCase       :				List[str]         =					_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)

def 				A_					(							a					):

      """simple docstring"""



      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
            if class_name in processors:
                  SCREAMING_SNAKE_CASE_			:					int          =       model_type_to_module_name(a					)

                  SCREAMING_SNAKE_CASE_			:					str          =       importlib.import_module(f".{module_name}"   ,				'transformers.models'					)
                  try:
                        return getattr(a   ,				a					)
                  except AttributeError:
                        continue

      for processor in PROCESSOR_MAPPING._extra_content.values():
            if getattr(a   ,				'__name__'   ,				a					) == class_name:
                  return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
      SCREAMING_SNAKE_CASE_			:					str          =       importlib.import_module('transformers'					)
      if hasattr(a   ,				a					):
            return getattr(a   ,				a					)

      return None



class 	_A						:

  def __init__(			self   ):





        """simple docstring"""







        raise EnvironmentError(
            'AutoProcessor is designed to be instantiated '
            'using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'   )

  @classmethod
  @replace_list_option_in_docstrings(_SCREAMING_SNAKE_CASE   )
  def 	UpperCAmelCase      (			cls	,   _SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       kwargs.pop('config'	,   _SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					str          =       kwargs.pop('trust_remote_code'	,   _SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					str          =       True

        SCREAMING_SNAKE_CASE_			:					List[Any]          =       None
        SCREAMING_SNAKE_CASE_			:					str          =       None

        # First, let's see if we have a preprocessor config.
        # Filter the kwargs for `get_file_from_repo`.
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       {
            key: kwargs[key] for key in inspect.signature(_SCREAMING_SNAKE_CASE   ).parameters.keys() if key in kwargs
        }
        # Let's start by checking whether the processor class is saved in an image processor
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       get_file_from_repo(
            _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )
        if preprocessor_config_file is not None:
              SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			:					str          =       ImageProcessingMixin.get_image_processor_dict(_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )
              SCREAMING_SNAKE_CASE_			:					int          =       config_dict.get('processor_class'	,   _SCREAMING_SNAKE_CASE   )
              if "AutoProcessor" in config_dict.get('auto_map'	,   {}   ):
                    SCREAMING_SNAKE_CASE_			:					Any          =       config_dict['auto_map']['AutoProcessor']

        # If not found, let's check whether the processor class is saved in a feature extractor config
        if preprocessor_config_file is not None and processor_class is None:
              SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			:					Any          =       FeatureExtractionMixin.get_feature_extractor_dict(_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )
              SCREAMING_SNAKE_CASE_			:					Optional[int]          =       config_dict.get('processor_class'	,   _SCREAMING_SNAKE_CASE   )
              if "AutoProcessor" in config_dict.get('auto_map'	,   {}   ):
                    SCREAMING_SNAKE_CASE_			:					Dict          =       config_dict['auto_map']['AutoProcessor']

        if processor_class is None:
              # Next, let's check whether the processor class is saved in a tokenizer
              SCREAMING_SNAKE_CASE_			:					str          =       get_file_from_repo(
                  _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )
              if tokenizer_config_file is not None:
                    with open(_SCREAMING_SNAKE_CASE	,   encoding='utf-8'   ) as reader:
                          SCREAMING_SNAKE_CASE_			:					Any          =       json.load(_SCREAMING_SNAKE_CASE   )

                    SCREAMING_SNAKE_CASE_			:					Any          =       config_dict.get('processor_class'	,   _SCREAMING_SNAKE_CASE   )
                    if "AutoProcessor" in config_dict.get('auto_map'	,   {}   ):
                          SCREAMING_SNAKE_CASE_			:					Tuple          =       config_dict['auto_map']['AutoProcessor']

        if processor_class is None:
              # Otherwise, load config, if it can be loaded.
              if not isinstance(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):
                    SCREAMING_SNAKE_CASE_			:					List[Any]          =       AutoConfig.from_pretrained(
                        _SCREAMING_SNAKE_CASE	,   trust_remote_code=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )

              # And check if the config contains the processor class.
              SCREAMING_SNAKE_CASE_			:					List[str]          =       getattr(_SCREAMING_SNAKE_CASE	,   'processor_class'	,   _SCREAMING_SNAKE_CASE   )
              if hasattr(_SCREAMING_SNAKE_CASE	,   'auto_map'   ) and "AutoProcessor" in config.auto_map:
                    SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       config.auto_map['AutoProcessor']

        if processor_class is not None:
              SCREAMING_SNAKE_CASE_			:					List[Any]          =       processor_class_from_name(_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					Tuple          =       processor_auto_map is not None
        SCREAMING_SNAKE_CASE_			:					List[str]          =       processor_class is not None or type(_SCREAMING_SNAKE_CASE   ) in PROCESSOR_MAPPING
        SCREAMING_SNAKE_CASE_			:					int          =       resolve_trust_remote_code(
            _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )

        if has_remote_code and trust_remote_code:
              SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       get_class_from_dynamic_module(
                  _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )
              SCREAMING_SNAKE_CASE_			:					List[Any]          =       kwargs.pop('code_revision'	,   _SCREAMING_SNAKE_CASE   )
              if os.path.isdir(_SCREAMING_SNAKE_CASE   ):
                    processor_class.register_for_auto_class()
              return processor_class.from_pretrained(
                  _SCREAMING_SNAKE_CASE	,   trust_remote_code=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )
        elif processor_class is not None:
              return processor_class.from_pretrained(
                  _SCREAMING_SNAKE_CASE	,   trust_remote_code=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )
        # Last try: we use the PROCESSOR_MAPPING.
        elif type(_SCREAMING_SNAKE_CASE   ) in PROCESSOR_MAPPING:
              return PROCESSOR_MAPPING[type(_SCREAMING_SNAKE_CASE   )].from_pretrained(_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )

        # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
        # tokenizer.
        try:
              return AutoTokenizer.from_pretrained(
                  _SCREAMING_SNAKE_CASE	,   trust_remote_code=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )
        except Exception:
              try:
                    return AutoImageProcessor.from_pretrained(
                        _SCREAMING_SNAKE_CASE	,   trust_remote_code=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )
              except Exception:
                    pass

              try:
                    return AutoFeatureExtractor.from_pretrained(
                        _SCREAMING_SNAKE_CASE	,   trust_remote_code=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )
              except Exception:
                    pass

        raise ValueError(
            f"Unrecognized processing class in {pretrained_model_name_or_path}. Can't instantiate a processor, a "
            'tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'
            'the files of at least one of those processing classes.'   )



  @staticmethod
  def 	UpperCAmelCase      (			_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        PROCESSOR_MAPPING.register(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )









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


lowerCAmelCase       :				Optional[int]         =					logging.get_logger(__name__)  # pylint: disable=invalid-name


lowerCAmelCase       :				Any         =					'lfs-multipart-upload'
class 	_A						(					__magic_name__):

  @staticmethod
  def 	UpperCAmelCase      (			_SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       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=_SCREAMING_SNAKE_CASE	,   help='Local path to repository you want to configure.'   )
        enable_parser.set_defaults(func=lambda _SCREAMING_SNAKE_CASE   : LfsEnableCommand(_SCREAMING_SNAKE_CASE   )   )

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

class 	_A						:

  def __init__(			self	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       args



  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







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

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Any          =       json.dumps(a					) + '\n'
      sys.stdout.write(a					)
      sys.stdout.flush()

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[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 	_A						(					__magic_name__):

  def __init__(			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       filepath
        SCREAMING_SNAKE_CASE_			:					Tuple          =       seek_from
        SCREAMING_SNAKE_CASE_			:					Tuple          =       read_limit
        SCREAMING_SNAKE_CASE_			:					int          =       0

  def __enter__(			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       open(self.filepath	,   'rb'   )
        self.f.seek(self.seek_from   )
        return self

  def __len__(			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Tuple          =       os.fstat(self.f.fileno()   ).st_size
        return min(self.read_limit	,   total_length - self.seek_from   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE=-1   ):





        """simple docstring"""







        if self.n_seen >= self.read_limit:
              return b""
        SCREAMING_SNAKE_CASE_			:					str          =       self.read_limit - self.n_seen
        SCREAMING_SNAKE_CASE_			:					Any          =       self.f.read(remaining_amount if n < 0 else min(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )   )
        self.n_seen += len(_SCREAMING_SNAKE_CASE   )
        return data

  def __iter__(			self   ):





        """simple docstring"""







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



  def __exit__(			self	,   *_SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        self.f.close()

class 	_A						:

  def __init__(			self	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[Any]          =       args



  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[Any]          =       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:
              SCREAMING_SNAKE_CASE_			:					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   )

              SCREAMING_SNAKE_CASE_			:					int          =       msg['oid']
              SCREAMING_SNAKE_CASE_			:					int          =       msg['path']
              SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       msg['action']['href']
              SCREAMING_SNAKE_CASE_			:					str          =       msg['action']['header']
              SCREAMING_SNAKE_CASE_			:					Any          =       int(header.pop('chunk_size'   )   )
              SCREAMING_SNAKE_CASE_			:					List[str]          =       list(header.values()   )

              SCREAMING_SNAKE_CASE_			:					List[str]          =       []
              for i, presigned_url in enumerate(_SCREAMING_SNAKE_CASE   ):
                    with FileSlice(_SCREAMING_SNAKE_CASE	,   seek_from=i * chunk_size	,   read_limit=_SCREAMING_SNAKE_CASE   ) as data:
                          SCREAMING_SNAKE_CASE_			:					int          =       requests.put(_SCREAMING_SNAKE_CASE	,   data=_SCREAMING_SNAKE_CASE   )
                          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.

              SCREAMING_SNAKE_CASE_			:					Dict          =       requests.post(
                  _SCREAMING_SNAKE_CASE	,   json={
                      'oid': oid,
                      'parts': parts,
                  }	,   )
              r.raise_for_status()

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






