


import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# 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 ...utils import CONFIG_NAME, 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,
)


SCREAMING_SNAKE_CASE			: str				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: int				   =					OrderedDict(
    [
        ("audio-spectrogram-transformer", "ASTFeatureExtractor"),
        ("beit", "BeitFeatureExtractor"),
        ("chinese_clip", "ChineseCLIPFeatureExtractor"),
        ("clap", "ClapFeatureExtractor"),
        ("clip", "CLIPFeatureExtractor"),
        ("clipseg", "ViTFeatureExtractor"),
        ("conditional_detr", "ConditionalDetrFeatureExtractor"),
        ("convnext", "ConvNextFeatureExtractor"),
        ("cvt", "ConvNextFeatureExtractor"),
        ("data2vec-audio", "Wav2Vec2FeatureExtractor"),
        ("data2vec-vision", "BeitFeatureExtractor"),
        ("deformable_detr", "DeformableDetrFeatureExtractor"),
        ("deit", "DeiTFeatureExtractor"),
        ("detr", "DetrFeatureExtractor"),
        ("dinat", "ViTFeatureExtractor"),
        ("donut-swin", "DonutFeatureExtractor"),
        ("dpt", "DPTFeatureExtractor"),
        ("encodec", "EncodecFeatureExtractor"),
        ("flava", "FlavaFeatureExtractor"),
        ("glpn", "GLPNFeatureExtractor"),
        ("groupvit", "CLIPFeatureExtractor"),
        ("hubert", "Wav2Vec2FeatureExtractor"),
        ("imagegpt", "ImageGPTFeatureExtractor"),
        ("layoutlmv2", "LayoutLMv2FeatureExtractor"),
        ("layoutlmv3", "LayoutLMv3FeatureExtractor"),
        ("levit", "LevitFeatureExtractor"),
        ("maskformer", "MaskFormerFeatureExtractor"),
        ("mctct", "MCTCTFeatureExtractor"),
        ("mobilenet_v1", "MobileNetV1FeatureExtractor"),
        ("mobilenet_v2", "MobileNetV2FeatureExtractor"),
        ("mobilevit", "MobileViTFeatureExtractor"),
        ("nat", "ViTFeatureExtractor"),
        ("owlvit", "OwlViTFeatureExtractor"),
        ("perceiver", "PerceiverFeatureExtractor"),
        ("poolformer", "PoolFormerFeatureExtractor"),
        ("regnet", "ConvNextFeatureExtractor"),
        ("resnet", "ConvNextFeatureExtractor"),
        ("segformer", "SegformerFeatureExtractor"),
        ("sew", "Wav2Vec2FeatureExtractor"),
        ("sew-d", "Wav2Vec2FeatureExtractor"),
        ("speech_to_text", "Speech2TextFeatureExtractor"),
        ("speecht5", "SpeechT5FeatureExtractor"),
        ("swiftformer", "ViTFeatureExtractor"),
        ("swin", "ViTFeatureExtractor"),
        ("swinv2", "ViTFeatureExtractor"),
        ("table-transformer", "DetrFeatureExtractor"),
        ("timesformer", "VideoMAEFeatureExtractor"),
        ("tvlt", "TvltFeatureExtractor"),
        ("unispeech", "Wav2Vec2FeatureExtractor"),
        ("unispeech-sat", "Wav2Vec2FeatureExtractor"),
        ("van", "ConvNextFeatureExtractor"),
        ("videomae", "VideoMAEFeatureExtractor"),
        ("vilt", "ViltFeatureExtractor"),
        ("vit", "ViTFeatureExtractor"),
        ("vit_mae", "ViTFeatureExtractor"),
        ("vit_msn", "ViTFeatureExtractor"),
        ("wav2vec2", "Wav2Vec2FeatureExtractor"),
        ("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
        ("wavlm", "Wav2Vec2FeatureExtractor"),
        ("whisper", "WhisperFeatureExtractor"),
        ("xclip", "CLIPFeatureExtractor"),
        ("yolos", "YolosFeatureExtractor"),
    ]
)

SCREAMING_SNAKE_CASE			: Optional[int]				   =					_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Optional[int]:
    for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
        if class_name in extractors:
            _lowercase			:				Tuple 						=						model_type_to_module_name(lowerCamelCase_					)

            _lowercase			:				Optional[int] 						=						importlib.import_module(F'''.{module_name}'''				,					'transformers.models'					)
            try:
                return getattr(lowerCamelCase_				,					lowerCamelCase_					)
            except AttributeError:
                continue

    for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
        if getattr(lowerCamelCase_				,					'__name__'				,					lowerCamelCase_					) == class_name:
            return extractor

    # 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.
    _lowercase			:				str 						=						importlib.import_module('transformers'					)
    if hasattr(lowerCamelCase_				,					lowerCamelCase_					):
        return getattr(lowerCamelCase_				,					lowerCamelCase_					)

    return None



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_ = None				,					lowerCamelCase_ = False				,					lowerCamelCase_ = False				,					lowerCamelCase_ = None				,					lowerCamelCase_ = None				,					lowerCamelCase_ = None				,					lowerCamelCase_ = False				,					**lowerCamelCase_				,					)       ->		Dict:
    _lowercase			:				List[Any] 						=						get_file_from_repo(
        lowerCamelCase_				,					lowerCamelCase_				,					cache_dir=lowerCamelCase_				,					force_download=lowerCamelCase_				,					resume_download=lowerCamelCase_				,					proxies=lowerCamelCase_				,					use_auth_token=lowerCamelCase_				,					revision=lowerCamelCase_				,					local_files_only=lowerCamelCase_				,					)
    if resolved_config_file is None:
        logger.info(
            'Could not locate the feature extractor configuration file, will try to use the model config instead.'					)
        return {}

    with open(lowerCamelCase_				,					encoding='utf-8'					) as reader:
        return json.load(lowerCamelCase_					)






class _lowerCamelCase:
       def __init__(   self)			->  Union[str, Any]:




           """simple docstring"""

           raise EnvironmentError(
               'AutoFeatureExtractor is designed to be instantiated '
               'using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.')
       @classmethod
       @replace_list_option_in_docstrings(lowerCamelCase)
       def      UpperCamelCase	(   cls, lowerCamelCase, **lowerCamelCase)			->  int:




           """simple docstring"""

           _lowercase			:				str 						=						kwargs.pop('config', lowerCamelCase)
           _lowercase			:				List[Any] 						=						kwargs.pop('trust_remote_code', lowerCamelCase)
           _lowercase			:				List[Any] 						=						True

           _lowercase						,							_lowercase			:				List[str] 						=						FeatureExtractionMixin.get_feature_extractor_dict(lowerCamelCase, **lowerCamelCase)
           _lowercase			:				Tuple 						=						config_dict.get('feature_extractor_type', lowerCamelCase)
           _lowercase			:				List[Any] 						=						None
           if "AutoFeatureExtractor" in config_dict.get('auto_map', {}):
               _lowercase			:				Union[str, Any] 						=						config_dict['auto_map']['AutoFeatureExtractor']

           # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
           if feature_extractor_class is None and feature_extractor_auto_map is None:
               if not isinstance(lowerCamelCase, lowerCamelCase):
                   _lowercase			:				Dict 						=						AutoConfig.from_pretrained(lowerCamelCase, **lowerCamelCase)
               # It could be in `config.feature_extractor_type``
               _lowercase			:				Optional[int] 						=						getattr(lowerCamelCase, 'feature_extractor_type', lowerCamelCase)
               if hasattr(lowerCamelCase, 'auto_map') and "AutoFeatureExtractor" in config.auto_map:
                   _lowercase			:				Union[str, Any] 						=						config.auto_map['AutoFeatureExtractor']

           if feature_extractor_class is not None:
               _lowercase			:				str 						=						feature_extractor_class_from_name(lowerCamelCase)

           _lowercase			:				Tuple 						=						feature_extractor_auto_map is not None
           _lowercase			:				int 						=						feature_extractor_class is not None or type(lowerCamelCase) in FEATURE_EXTRACTOR_MAPPING
           _lowercase			:				int 						=						resolve_trust_remote_code(
               lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)

           if has_remote_code and trust_remote_code:
               _lowercase			:				Dict 						=						get_class_from_dynamic_module(
                   lowerCamelCase, lowerCamelCase, **lowerCamelCase)
               _lowercase			:				Optional[int] 						=						kwargs.pop('code_revision', lowerCamelCase)
               if os.path.isdir(lowerCamelCase):
                   feature_extractor_class.register_for_auto_class()
               return feature_extractor_class.from_dict(lowerCamelCase, **lowerCamelCase)
           elif feature_extractor_class is not None:
               return feature_extractor_class.from_dict(lowerCamelCase, **lowerCamelCase)
           # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
           elif type(lowerCamelCase) in FEATURE_EXTRACTOR_MAPPING:
               _lowercase			:				str 						=						FEATURE_EXTRACTOR_MAPPING[type(lowerCamelCase)]
               return feature_extractor_class.from_dict(lowerCamelCase, **lowerCamelCase)

           raise ValueError(
               F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
               F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
               F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys())}''')




       @staticmethod
       def      UpperCamelCase	(   lowerCamelCase, lowerCamelCase)			->  int:




           """simple docstring"""

           FEATURE_EXTRACTOR_MAPPING.register(lowerCamelCase, lowerCamelCase)






import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length



def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:
    _lowercase			:				List[str] 						=						int(lowerCamelCase_					)
    _lowercase						,							_lowercase						,							_lowercase			:				Optional[Any] 						=						t // 3600, (t // 60) % 60, t % 60
    return F'''{h}:{m:02d}:{s:02d}''' if h != 0 else F'''{m:02d}:{s:02d}'''



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=300					)       ->		Dict:
    # docstyle-ignore
    return F'''
    <div>
      {prefix}
      <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
      {label}
    </div>
    '''



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Any:
    _lowercase			:				int 						=						'<table border="1" class="dataframe">\n'
    html_code += """  <thead>\n <tr style="text-align: left;">\n"""
    for i in items[0]:
        html_code += F'''      <th>{i}</th>\n'''
    html_code += "    </tr>\n  </thead>\n  <tbody>\n"
    for line in items[1:]:
        html_code += "    <tr>\n"
        for elt in line:
            _lowercase			:				Any 						=						F'''{elt:.6f}''' if isinstance(lowerCamelCase_				,					lowerCamelCase_					) else str(lowerCamelCase_					)
            html_code += F'''      <td>{elt}</td>\n'''
        html_code += "    </tr>\n"
    html_code += "  </tbody>\n</table><p>"
    return html_code






class _lowerCamelCase:
       lowercase_					:      str       					=     5
       lowercase_					:      str       					=     0.2
       def __init__(   self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = 3_00, )			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						total
           _lowercase			:				Optional[int] 						=						'' if prefix is None else prefix
           _lowercase			:				Tuple 						=						leave
           _lowercase			:				str 						=						parent
           _lowercase			:				str 						=						width
           _lowercase			:				List[Any] 						=						None
           _lowercase			:				List[str] 						=						None
           _lowercase			:				Tuple 						=						None
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = False, lowerCamelCase = None)			->  Dict:




           """simple docstring"""

           _lowercase			:				Any 						=						value
           if comment is not None:
               _lowercase			:				Union[str, Any] 						=						comment
           if self.last_value is None:
               _lowercase			:				Dict 						=						time.time()
               _lowercase			:				Tuple 						=						value
               _lowercase			:				str 						=						None
               _lowercase			:				Optional[int] 						=						self.warmup
               _lowercase			:				Optional[Any] 						=						1
               self.update_bar(lowerCamelCase)
           elif value <= self.last_value and not force_update:
               return
           elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for, self.total):
               if self.first_calls > 0:
                   self.first_calls -= 1
               _lowercase			:				List[str] 						=						time.time()
               _lowercase			:				Tuple 						=						current_time - self.start_time
               # We could have value = self.start_value if the update is called twixe with the same start value.
               if value > self.start_value:
                   _lowercase			:				Dict 						=						self.elapsed_time / (value - self.start_value)
               else:
                   _lowercase			:				int 						=						None
               if value >= self.total:
                   _lowercase			:				Dict 						=						self.total
                   _lowercase			:				List[str] 						=						None
                   if not self.leave:
                       self.close()
               elif self.average_time_per_item is not None:
                   _lowercase			:				Optional[int] 						=						self.average_time_per_item * (self.total - value)
               self.update_bar(lowerCamelCase)
               _lowercase			:				int 						=						value
               _lowercase			:				Tuple 						=						current_time
               if self.average_time_per_item is None:
                   _lowercase			:				str 						=						1
               else:
                   _lowercase			:				int 						=						max(int(self.update_every / self.average_time_per_item), 1)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=None)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						' ' * (len(str(self.total)) - len(str(lowerCamelCase))) + str(lowerCamelCase)
           if self.elapsed_time is None:
               _lowercase			:				int 						=						F'''[{spaced_value}/{self.total} : < :'''
           elif self.predicted_remaining is None:
               _lowercase			:				Union[str, Any] 						=						F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time)}'''
           else:
               _lowercase			:				Union[str, Any] 						=						(
                   F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time)} <'''
                   F''' {format_time(self.predicted_remaining)}'''
               )
               self.label += F''', {1/self.average_time_per_item:.2f} it/s'''
           self.label += "]" if self.comment is None or len(self.comment) == 0 else F''', {self.comment}]'''
           self.display()
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Any 						=						html_progress_bar(self.value, self.total, self.prefix, self.label, self.width)
           if self.parent is not None:
               # If this is a child bar, the parent will take care of the display.
               self.parent.display()
               return
           if self.output is None:
               _lowercase			:				Optional[Any] 						=						disp.display(disp.HTML(self.html_code), display_id=lowerCamelCase)
           else:
               self.output.update(disp.HTML(self.html_code))




       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           if self.parent is None and self.output is not None:
               self.output.update(disp.HTML(''))






class _lowerCamelCase(		_a  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase=None)			->  int:




           """simple docstring"""

           super().__init__(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						None if column_names is None else [column_names]
           _lowercase			:				Any 						=						None
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Any 						=						html_progress_bar(self.value, self.total, self.prefix, self.label, self.width)
           if self.inner_table is not None:
               self.html_code += text_to_html_table(self.inner_table)
           if self.child_bar is not None:
               self.html_code += self.child_bar.html_code
           if self.output is None:
               _lowercase			:				Dict 						=						disp.display(disp.HTML(self.html_code), display_id=lowerCamelCase)
           else:
               self.output.update(disp.HTML(self.html_code))
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Dict:




           """simple docstring"""

           if self.inner_table is None:
               _lowercase			:				Dict 						=						[list(values.keys()), list(values.values())]
           else:
               _lowercase			:				Tuple 						=						self.inner_table[0]
               if len(self.inner_table) == 1:
                   # We give a chance to update the column names at the first iteration
                   for key in values.keys():
                       if key not in columns:
                           columns.append(lowerCamelCase)
                   _lowercase			:				str 						=						columns
               self.inner_table.append([values[c] for c in columns])
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=None, lowerCamelCase=3_00)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				List[str] 						=						NotebookProgressBar(lowerCamelCase, prefix=lowerCamelCase, parent=self, width=lowerCamelCase)
           return self.child_bar




       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						None
           self.display()






class _lowerCamelCase(		_a  ):
       def __init__(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						None
           _lowercase			:				Dict 						=						None
           _lowercase			:				Dict 						=						False
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase)			->  Dict:




           """simple docstring"""

           _lowercase			:				Dict 						=						'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
           _lowercase			:				Dict 						=						0
           _lowercase			:				Tuple 						=						0
           _lowercase			:				int 						=						[self.first_column] + ['Training Loss']
           if args.evaluation_strategy != IntervalStrategy.NO:
               column_names.append('Validation Loss')
           _lowercase			:				Union[str, Any] 						=						NotebookTrainingTracker(state.max_steps, lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Any 						=						int(state.epoch) if int(state.epoch) == state.epoch else F'''{state.epoch:.2f}'''
           self.training_tracker.update(
               state.global_step + 1, comment=F'''Epoch {epoch}/{state.num_train_epochs}''', force_update=self._force_next_update, )
           _lowercase			:				str 						=						False
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=None, **lowerCamelCase)			->  Any:




           """simple docstring"""

           if not has_length(lowerCamelCase):
               return
           if self.prediction_bar is None:
               if self.training_tracker is not None:
                   _lowercase			:				Optional[int] 						=						self.training_tracker.add_child(len(lowerCamelCase))
               else:
                   _lowercase			:				Optional[int] 						=						NotebookProgressBar(len(lowerCamelCase))
               self.prediction_bar.update(1)
           else:
               self.prediction_bar.update(self.prediction_bar.value + 1)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase)			->  Optional[int]:




           """simple docstring"""

           if self.prediction_bar is not None:
               self.prediction_bar.close()
           _lowercase			:				Any 						=						None
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=None, **lowerCamelCase)			->  List[Any]:




           """simple docstring"""

           if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
               _lowercase			:				Dict 						=						{'Training Loss': logs['loss']}
               # First column is necessarily Step sine we're not in epoch eval strategy
               _lowercase			:				List[Any] 						=						state.global_step
               self.training_tracker.write_line(lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=None, **lowerCamelCase)			->  List[str]:




           """simple docstring"""

           if self.training_tracker is not None:
               _lowercase			:				Tuple 						=						{'Training Loss': 'No log', 'Validation Loss': 'No log'}
               for log in reversed(state.log_history):
                   if "loss" in log:
                       _lowercase			:				int 						=						log['loss']
                       break

               if self.first_column == "Epoch":
                   _lowercase			:				Union[str, Any] 						=						int(state.epoch)
               else:
                   _lowercase			:				Optional[Any] 						=						state.global_step
               _lowercase			:				str 						=						'eval'
               for k in metrics:
                   if k.endswith('_loss'):
                       _lowercase			:				str 						=						re.sub(R'\_loss$', '', lowerCamelCase)
               _lowercase			:				Tuple 						=						metrics.pop('total_flos', lowerCamelCase)
               _lowercase			:				List[str] 						=						metrics.pop('epoch', lowerCamelCase)
               _lowercase			:				List[Any] 						=						metrics.pop(F'''{metric_key_prefix}_runtime''', lowerCamelCase)
               _lowercase			:				Dict 						=						metrics.pop(F'''{metric_key_prefix}_samples_per_second''', lowerCamelCase)
               _lowercase			:				Tuple 						=						metrics.pop(F'''{metric_key_prefix}_steps_per_second''', lowerCamelCase)
               _lowercase			:				List[str] 						=						metrics.pop(F'''{metric_key_prefix}_jit_compilation_time''', lowerCamelCase)
               for k, v in metrics.items():
                   if k == F'''{metric_key_prefix}_loss''':
                       _lowercase			:				Union[str, Any] 						=						v
                   else:
                       _lowercase			:				Optional[Any] 						=						k.split('_')
                       _lowercase			:				Optional[int] 						=						' '.join([part.capitalize() for part in splits[1:]])
                       _lowercase			:				Tuple 						=						v
               self.training_tracker.write_line(lowerCamelCase)
               self.training_tracker.remove_child()
               _lowercase			:				str 						=						None
               # Evaluation takes a long time so we should force the next update.
               _lowercase			:				Optional[Any] 						=						True




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           self.training_tracker.update(
               state.global_step, comment=F'''Epoch {int(state.epoch)}/{state.num_train_epochs}''', force_update=lowerCamelCase)
           _lowercase			:				Any 						=						None


