

'''simple docstring'''






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

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_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,
)


lowerCAmelCase__   		=      logging.get_logger(__name__)

lowerCAmelCase__   		=      OrderedDict(
    [
        ('''align''', '''EfficientNetImageProcessor'''),
        ('''beit''', '''BeitImageProcessor'''),
        ('''bit''', '''BitImageProcessor'''),
        ('''blip''', '''BlipImageProcessor'''),
        ('''blip-2''', '''BlipImageProcessor'''),
        ('''bridgetower''', '''BridgeTowerImageProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
        ('''clip''', '''CLIPImageProcessor'''),
        ('''clipseg''', '''ViTImageProcessor'''),
        ('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
        ('''convnext''', '''ConvNextImageProcessor'''),
        ('''convnextv2''', '''ConvNextImageProcessor'''),
        ('''cvt''', '''ConvNextImageProcessor'''),
        ('''data2vec-vision''', '''BeitImageProcessor'''),
        ('''deformable_detr''', '''DeformableDetrImageProcessor'''),
        ('''deit''', '''DeiTImageProcessor'''),
        ('''deta''', '''DetaImageProcessor'''),
        ('''detr''', '''DetrImageProcessor'''),
        ('''dinat''', '''ViTImageProcessor'''),
        ('''donut-swin''', '''DonutImageProcessor'''),
        ('''dpt''', '''DPTImageProcessor'''),
        ('''efficientformer''', '''EfficientFormerImageProcessor'''),
        ('''efficientnet''', '''EfficientNetImageProcessor'''),
        ('''flava''', '''FlavaImageProcessor'''),
        ('''focalnet''', '''BitImageProcessor'''),
        ('''git''', '''CLIPImageProcessor'''),
        ('''glpn''', '''GLPNImageProcessor'''),
        ('''groupvit''', '''CLIPImageProcessor'''),
        ('''imagegpt''', '''ImageGPTImageProcessor'''),
        ('''instructblip''', '''BlipImageProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
        ('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
        ('''levit''', '''LevitImageProcessor'''),
        ('''mask2former''', '''Mask2FormerImageProcessor'''),
        ('''maskformer''', '''MaskFormerImageProcessor'''),
        ('''mgp-str''', '''ViTImageProcessor'''),
        ('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
        ('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevitv2''', '''MobileViTImageProcessor'''),
        ('''nat''', '''ViTImageProcessor'''),
        ('''oneformer''', '''OneFormerImageProcessor'''),
        ('''owlvit''', '''OwlViTImageProcessor'''),
        ('''perceiver''', '''PerceiverImageProcessor'''),
        ('''pix2struct''', '''Pix2StructImageProcessor'''),
        ('''poolformer''', '''PoolFormerImageProcessor'''),
        ('''regnet''', '''ConvNextImageProcessor'''),
        ('''resnet''', '''ConvNextImageProcessor'''),
        ('''sam''', '''SamImageProcessor'''),
        ('''segformer''', '''SegformerImageProcessor'''),
        ('''swiftformer''', '''ViTImageProcessor'''),
        ('''swin''', '''ViTImageProcessor'''),
        ('''swin2sr''', '''Swin2SRImageProcessor'''),
        ('''swinv2''', '''ViTImageProcessor'''),
        ('''table-transformer''', '''DetrImageProcessor'''),
        ('''timesformer''', '''VideoMAEImageProcessor'''),
        ('''tvlt''', '''TvltImageProcessor'''),
        ('''upernet''', '''SegformerImageProcessor'''),
        ('''van''', '''ConvNextImageProcessor'''),
        ('''videomae''', '''VideoMAEImageProcessor'''),
        ('''vilt''', '''ViltImageProcessor'''),
        ('''vit''', '''ViTImageProcessor'''),
        ('''vit_hybrid''', '''ViTHybridImageProcessor'''),
        ('''vit_mae''', '''ViTImageProcessor'''),
        ('''vit_msn''', '''ViTImageProcessor'''),
        ('''xclip''', '''CLIPImageProcessor'''),
        ('''yolos''', '''YolosImageProcessor'''),
    ]
)

lowerCAmelCase__   		=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)


def 						_A	(       A__ ):






 """simple docstring"""



 for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
  if class_name in extractors:
   __lowercase				    =					model_type_to_module_name(A__ )

   __lowercase				    =					importlib.import_module(F".{module_name}"	,							'''transformers.models''' )
   try:
    return getattr(A__	,							A__ )
   except AttributeError:
    continue

 for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
  if getattr(A__	,							'''__name__'''	,							A__ ) == 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				    =					importlib.import_module('''transformers''' )
 if hasattr(A__	,							A__ ):
  return getattr(A__	,							A__ )

 return None


def 						_A	(       A__	,							A__ = None	,							A__ = False	,							A__ = False	,							A__ = None	,							A__ = None	,							A__ = None	,							A__ = False	,							**A__	,							):






 """simple docstring"""



 __lowercase				    =					get_file_from_repo(
     A__	,							A__	,							cache_dir=A__	,							force_download=A__	,							resume_download=A__	,							proxies=A__	,							use_auth_token=A__	,							revision=A__	,							local_files_only=A__	,							)
 if resolved_config_file is None:
  logger.info(
      '''Could not locate the image processor configuration file, will try to use the model config instead.''' )
  return {}

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






class      lowercase_      :




  """simple docstring"""




  def __init__( self  :	int     ):
   raise EnvironmentError(
       '''AutoImageProcessor is designed to be instantiated '''
       '''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''     )






  @classmethod
  @replace_list_option_in_docstrings(lowercase__     )
  def    SCREAMING_SNAKE_CASE ( cls  :	str   ,lowercase__  :	str   ,**lowercase__  :	int     ):
   __lowercase				    =					kwargs.pop('''config'''   ,lowercase__     )
   __lowercase				    =					kwargs.pop('''trust_remote_code'''   ,lowercase__     )
   __lowercase				    =					True

   __lowercase    ,      __lowercase				    =					ImageProcessingMixin.get_image_processor_dict(lowercase__   ,**lowercase__     )
   __lowercase				    =					config_dict.get('''image_processor_type'''   ,lowercase__     )
   __lowercase				    =					None
   if "AutoImageProcessor" in config_dict.get('''auto_map'''   ,{}     ):
    __lowercase				    =					config_dict['''auto_map''']['''AutoImageProcessor''']

   # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
   # and if so, infer the image processor class from there.
   if image_processor_class is None and image_processor_auto_map is None:
    __lowercase				    =					config_dict.pop('''feature_extractor_type'''   ,lowercase__     )
    if feature_extractor_class is not None:
     logger.warning(
         '''Could not find image processor class in the image processor config or the model config. Loading'''
         ''' based on pattern matching with the model\'s feature extractor configuration.'''     )
     __lowercase				    =					feature_extractor_class.replace('''FeatureExtractor'''   ,'''ImageProcessor'''     )
    if "AutoFeatureExtractor" in config_dict.get('''auto_map'''   ,{}     ):
     __lowercase				    =					config_dict['''auto_map''']['''AutoFeatureExtractor''']
     __lowercase				    =					feature_extractor_auto_map.replace('''FeatureExtractor'''   ,'''ImageProcessor'''     )
     logger.warning(
         '''Could not find image processor auto map in the image processor config or the model config.'''
         ''' Loading based on pattern matching with the model\'s feature extractor configuration.'''     )

        # If we don't find the image processor class in the image processor config, let's try the model config.
   if image_processor_class is None and image_processor_auto_map is None:
    if not isinstance(lowercase__   ,lowercase__     ):
     __lowercase				    =					AutoConfig.from_pretrained(lowercase__   ,**lowercase__     )
    # It could be in `config.image_processor_type``
    __lowercase				    =					getattr(lowercase__   ,'''image_processor_type'''   ,lowercase__     )
    if hasattr(lowercase__   ,'''auto_map'''     ) and "AutoImageProcessor" in config.auto_map:
     __lowercase				    =					config.auto_map['''AutoImageProcessor''']

   if image_processor_class is not None:
    __lowercase				    =					image_processor_class_from_name(lowercase__     )

   __lowercase				    =					image_processor_auto_map is not None
   __lowercase				    =					image_processor_class is not None or type(lowercase__     ) in IMAGE_PROCESSOR_MAPPING
   __lowercase				    =					resolve_trust_remote_code(
       lowercase__   ,lowercase__   ,lowercase__   ,lowercase__     )

   if has_remote_code and trust_remote_code:
    __lowercase				    =					get_class_from_dynamic_module(
        lowercase__   ,lowercase__   ,**lowercase__     )
    __lowercase				    =					kwargs.pop('''code_revision'''   ,lowercase__     )
    if os.path.isdir(lowercase__     ):
     image_processor_class.register_for_auto_class()
    return image_processor_class.from_dict(lowercase__   ,**lowercase__     )
   elif image_processor_class is not None:
    return image_processor_class.from_dict(lowercase__   ,**lowercase__     )
   # Last try: we use the IMAGE_PROCESSOR_MAPPING.
   elif type(lowercase__     ) in IMAGE_PROCESSOR_MAPPING:
    __lowercase				    =					IMAGE_PROCESSOR_MAPPING[type(lowercase__     )]
    return image_processor_class.from_dict(lowercase__   ,**lowercase__     )

   raise ValueError(
       F"Unrecognized image processor in {pretrained_model_name_or_path}. Should have a "
       F"`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following "
       F"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()     )}"     )






  @staticmethod
  def    SCREAMING_SNAKE_CASE ( lowercase__  :	Tuple   ,lowercase__  :	Any     ):
   IMAGE_PROCESSOR_MAPPING.register(lowercase__   ,lowercase__     )



import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np


def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					np.array:

		"""simple docstring"""






		_UpperCamelCase         =						F'{sampling_rate}'
		_UpperCamelCase         =						"""1"""
		_UpperCamelCase         =						"""f32le"""
		_UpperCamelCase         =						[
		    """ffmpeg""",
		    """-i""",
		    """pipe:0""",
		    """-ac""",
		    ac,
		    """-ar""",
		    ar,
		    """-f""",
		    format_for_conversion,
		    """-hide_banner""",
		    """-loglevel""",
		    """quiet""",
		    """pipe:1""",
		]

		try:
				with subprocess.Popen(lowerCAmelCase    ,	stdin=subprocess.PIPE    ,	stdout=subprocess.PIPE			) as ffmpeg_process:
						_UpperCamelCase         =						ffmpeg_process.communicate(lowerCAmelCase			)
		except FileNotFoundError as error:
				raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""			) from error
		_UpperCamelCase         =						output_stream[0]
		_UpperCamelCase         =						np.frombuffer(lowerCAmelCase    ,	np.floataa			)
		if audio.shape[0] == 0:
				raise ValueError("""Malformed soundfile"""			)
		return audio


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = "f32le"    ,	)		->					Union[str, Any]:

		"""simple docstring"""






		_UpperCamelCase         =						F'{sampling_rate}'
		_UpperCamelCase         =						"""1"""
		if format_for_conversion == "s16le":
				_UpperCamelCase         =						2
		elif format_for_conversion == "f32le":
				_UpperCamelCase         =						4
		else:
				raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'			)

		_UpperCamelCase         =						platform.system()
		if system == "Linux":
				_UpperCamelCase         =						"""alsa"""
				_UpperCamelCase         =						"""default"""
		elif system == "Darwin":
				_UpperCamelCase         =						"""avfoundation"""
				_UpperCamelCase         =						""":0"""
		elif system == "Windows":
				_UpperCamelCase         =						"""dshow"""
				_UpperCamelCase         =						"""default"""

		_UpperCamelCase         =						[
		    """ffmpeg""",
		    """-f""",
		    format_,
		    """-i""",
		    input_,
		    """-ac""",
		    ac,
		    """-ar""",
		    ar,
		    """-f""",
		    format_for_conversion,
		    """-fflags""",
		    """nobuffer""",
		    """-hide_banner""",
		    """-loglevel""",
		    """quiet""",
		    """pipe:1""",
		]
		_UpperCamelCase         =						int(round(sampling_rate * chunk_length_s			)			) * size_of_sample
		_UpperCamelCase         =						_ffmpeg_stream(lowerCAmelCase    ,	lowerCAmelCase			)
		for item in iterator:
				yield item


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = "f32le"    ,	)		->					Any:

		"""simple docstring"""






		if stream_chunk_s is not None:
				_UpperCamelCase         =						stream_chunk_s
		else:
				_UpperCamelCase         =						chunk_length_s

		_UpperCamelCase         =						ffmpeg_microphone(lowerCAmelCase    ,	lowerCAmelCase    ,	format_for_conversion=lowerCAmelCase			)
		if format_for_conversion == "s16le":
				_UpperCamelCase         =						np.intaa
				_UpperCamelCase         =						2
		elif format_for_conversion == "f32le":
				_UpperCamelCase         =						np.floataa
				_UpperCamelCase         =						4
		else:
				raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'			)

		if stride_length_s is None:
				_UpperCamelCase         =						chunk_length_s / 6
		_UpperCamelCase         =						int(round(sampling_rate * chunk_length_s			)			) * size_of_sample
		if isinstance(lowerCAmelCase    ,	(int, float)			):
				_UpperCamelCase         =						[stride_length_s, stride_length_s]

		_UpperCamelCase         =						int(round(sampling_rate * stride_length_s[0]			)			) * size_of_sample
		_UpperCamelCase         =						int(round(sampling_rate * stride_length_s[1]			)			) * size_of_sample
		_UpperCamelCase         =						datetime.datetime.now()
		_UpperCamelCase         =						datetime.timedelta(seconds=lowerCAmelCase			)
		for item in chunk_bytes_iter(lowerCAmelCase    ,	lowerCAmelCase    ,	stride=(stride_left, stride_right)    ,	stream=lowerCAmelCase			):
				# Put everything back in numpy scale
				_UpperCamelCase         =						np.frombuffer(item["""raw"""]    ,	dtype=lowerCAmelCase			)
				_UpperCamelCase         =						(
				    item["""stride"""][0] // size_of_sample,
				    item["""stride"""][1] // size_of_sample,
				)
				_UpperCamelCase         =						sampling_rate
				audio_time += delta
				if datetime.datetime.now() > audio_time + 1_0 * delta:
						# We're late !! SKIP
						continue
				yield item


def 				__A(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = False			)		->					str:

		"""simple docstring"""






		_UpperCamelCase         =						b""""""
		_UpperCamelCase     ,						_UpperCamelCase         =						stride
		if stride_left + stride_right >= chunk_len:
				raise ValueError(
				    F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'			)
		_UpperCamelCase         =						0
		for raw in iterator:
				acc += raw
				if stream and len(lowerCAmelCase			) < chunk_len:
						_UpperCamelCase         =						(_stride_left, 0)
						yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
				else:
						while len(lowerCAmelCase			) >= chunk_len:
								# We are flushing the accumulator
								_UpperCamelCase         =						(_stride_left, stride_right)
								_UpperCamelCase         =						{"""raw""": acc[:chunk_len], """stride""": stride}
								if stream:
										_UpperCamelCase         =						False
								yield item
								_UpperCamelCase         =						stride_left
								_UpperCamelCase         =						acc[chunk_len - stride_left - stride_right :]
    # Last chunk
		if len(lowerCAmelCase			) > stride_left:
				_UpperCamelCase         =						{"""raw""": acc, """stride""": (_stride_left, 0)}
				if stream:
						_UpperCamelCase         =						False
				yield item







def 				__A(lowerCAmelCase    ,	lowerCAmelCase			)		->					Tuple:

		"""simple docstring"""






		_UpperCamelCase         =						2**2_4  # 16Mo
		try:
				with subprocess.Popen(lowerCAmelCase    ,	stdout=subprocess.PIPE    ,	bufsize=lowerCAmelCase			) as ffmpeg_process:
						while True:
								_UpperCamelCase         =						ffmpeg_process.stdout.read(lowerCAmelCase			)
								if raw == b"":
										break
								yield raw
		except FileNotFoundError as error:
				raise ValueError("""ffmpeg was not found but is required to stream audio files from filename"""			) from error



