






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase					:		Dict	  =						logging.get_logger(__name__)

__UpperCamelCase					:		List[str]	  =						{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
__UpperCamelCase					:		Optional[Any]	  =						[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
def      a_ (      _A      ,   _A      ,   _A      ,   _A      ,   _A  )			->			Optional[int]:







				"""simple docstring"""
				for attribute in key.split('.'  ):
								snake_case__									=     getattr(_A      ,   _A  )

				if weight_type is not None:
								snake_case__									=     getattr(_A      ,   _A  ).shape
				else:
								snake_case__									=     hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
								    f''' {value.shape} for {full_name}'''  )

				if weight_type == "weight":
								snake_case__									=     value
				elif weight_type == "weight_g":
								snake_case__									=     value
				elif weight_type == "weight_v":
								snake_case__									=     value
				elif weight_type == "bias":
								snake_case__									=     value
				elif weight_type == "running_mean":
								snake_case__									=     value
				elif weight_type == "running_var":
								snake_case__									=     value
				elif weight_type == "num_batches_tracked":
								snake_case__									=     value
				elif weight_type == "inv_freq":
								snake_case__									=     value
				else:
								snake_case__									=     value

				logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''  )
def      a_ (      _A      ,   _A      ,   _A  )			->			Any:







				"""simple docstring"""
				snake_case__									=     []
				snake_case__									=     fairseq_model.state_dict()

				snake_case__									=     hf_model.wavaveca_conformer.feature_extractor

				for name, value in fairseq_dict.items():
								snake_case__									=     False
								if "conv_layers" in name:
												load_conv_layer(
												    _A      ,   _A      ,   _A      ,   _A      ,   hf_model.config.feat_extract_norm == 'group'      ,   )
												snake_case__									=     True
								else:
												for key, mapped_key in MAPPING.items():
																snake_case__									=     'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																if key in name or key.split('w2v_model.'  )[-1] == name.split('.'  )[0]:
																				snake_case__									=     True
																				if "*" in mapped_key:
																								snake_case__									=     name.split(_A  )[0].split('.'  )[-2]
																								snake_case__									=     mapped_key.replace('*'      ,   _A  )
																				if "pos_bias_u" in name:
																								snake_case__									=     None
																				elif "pos_bias_v" in name:
																								snake_case__									=     None
																				elif "weight_g" in name:
																								snake_case__									=     'weight_g'
																				elif "weight_v" in name:
																								snake_case__									=     'weight_v'
																				elif "bias" in name:
																								snake_case__									=     'bias'
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								snake_case__									=     'weight'
																				elif "running_mean" in name:
																								snake_case__									=     'running_mean'
																				elif "inv_freq" in name:
																								snake_case__									=     'inv_freq'
																				elif "running_var" in name:
																								snake_case__									=     'running_var'
																				elif "num_batches_tracked" in name:
																								snake_case__									=     'num_batches_tracked'
																				else:
																								snake_case__									=     None
																				set_recursively(_A      ,   _A      ,   _A      ,   _A      ,   _A  )
																continue
								if not is_used:
												unused_weights.append(_A  )

				logger.warning(f'''Unused weights: {unused_weights}'''  )
def      a_ (      _A      ,   _A      ,   _A      ,   _A      ,   _A  )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     full_name.split('conv_layers.'  )[-1]
				snake_case__									=     name.split('.'  )
				snake_case__									=     int(items[0]  )
				snake_case__									=     int(items[1]  )

				if type_id == 0:
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																raise ValueError(
																    f'''{full_name} has size {value.shape}, but'''
																    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''  )
												snake_case__									=     value
												logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''  )
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																raise ValueError(
																    f'''{full_name} has size {value.shape}, but'''
																    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''  )
												snake_case__									=     value
												logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''  )
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																raise ValueError(
																    f'''{full_name} has size {value.shape}, but'''
																    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''  )
												snake_case__									=     value
												logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																raise ValueError(
																    f'''{full_name} has size {value.shape}, but'''
																    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''  )
												snake_case__									=     value
												logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
				else:
								unused_weights.append(_A  )
@torch.no_grad()
def      a_ (      _A      ,   _A      ,   _A=None      ,   _A=None      ,   _A=True  )			->			Tuple:







				"""simple docstring"""
				if config_path is not None:
								snake_case__									=     WavaVecaConformerConfig.from_pretrained(_A      ,   hidden_act='swish'  )
				else:
								snake_case__									=     WavaVecaConformerConfig()

				if "rope" in checkpoint_path:
								snake_case__									=     'rotary'

				if is_finetuned:
								if dict_path:
												snake_case__									=     Dictionary.load(_A  )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												snake_case__									=     target_dict.pad_index
												snake_case__									=     target_dict.bos_index
												snake_case__									=     target_dict.eos_index
												snake_case__									=     len(target_dict.symbols  )
												snake_case__									=     os.path.join(_A      ,   'vocab.json'  )
												if not os.path.isdir(_A  ):
																logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_A  )  )
																return
												os.makedirs(_A      ,   exist_ok=_A  )
												snake_case__									=     target_dict.indices

												# fairseq has the <pad> and <s> switched
												snake_case__									=     0
												snake_case__									=     1
												with open(_A      ,   'w'      ,   encoding='utf-8'  ) as vocab_handle:
																json.dump(_A      ,   _A  )
												snake_case__									=     WavaVecaCTCTokenizer(
												    _A      ,   unk_token=target_dict.unk_word      ,   pad_token=target_dict.pad_word      ,   bos_token=target_dict.bos_word      ,   eos_token=target_dict.eos_word      ,   word_delimiter_token='|'      ,   do_lower_case=_A      ,   )
												snake_case__									=     True if config.feat_extract_norm == 'layer' else False
												snake_case__									=     WavaVecaFeatureExtractor(
												    feature_size=1      ,   sampling_rate=16000      ,   padding_value=0      ,   do_normalize=_A      ,   return_attention_mask=_A      ,   )
												snake_case__									=     WavaVecaProcessor(feature_extractor=_A      ,   tokenizer=_A  )
												processor.save_pretrained(_A  )

								snake_case__									=     WavaVecaConformerForCTC(_A  )
				else:
								snake_case__									=     WavaVecaConformerForPreTraining(_A  )

				if is_finetuned:
								snake_case__							,						snake_case__							,						snake_case__									=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]      ,   arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  )}  )
				else:
								snake_case__									=     argparse.Namespace(task='audio_pretraining'  )
								snake_case__									=     fairseq.tasks.setup_task(_A  )

								snake_case__							,						snake_case__							,						snake_case__									=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,   task=_A  )

				snake_case__									=     model[0].eval()

				recursively_load_weights(_A      ,   _A      ,   not is_finetuned  )

				hf_wavavec.save_pretrained(_A  )


if __name__ == "__main__":
							__UpperCamelCase					:		Union[str, Any]	  =						argparse.ArgumentParser()
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
							parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
							)
							__UpperCamelCase					:		int	  =						parser.parse_args()
							convert_wavaveca_conformer_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)












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,
)


__UpperCamelCase					:		List[Any]	  =						logging.get_logger(__name__)

__UpperCamelCase					:		str	  =						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"""),
    ]
)

__UpperCamelCase					:		Union[str, Any]	  =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def      a_ (      _A  )			->			Optional[int]:







				"""simple docstring"""
				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												snake_case__									=     model_type_to_module_name(_A  )

												snake_case__									=     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.
				snake_case__									=     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      ,   )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     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   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      Optional[int]						)		->		Union[str, Any]:
											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(UpperCamelCase						)
							def lowerCAmelCase_ (     cls:      int       ,	UpperCamelCase:      int       ,	**UpperCamelCase:      str						)		->		Optional[Any]:
											snake_case__									=     kwargs.pop('config'       ,	UpperCamelCase						)
											snake_case__									=     kwargs.pop('trust_remote_code'       ,	UpperCamelCase						)
											snake_case__									=     True

											snake_case__							,						snake_case__									=     ImageProcessingMixin.get_image_processor_dict(UpperCamelCase       ,	**UpperCamelCase						)
											snake_case__									=     config_dict.get('image_processor_type'       ,	UpperCamelCase						)
											snake_case__									=     None
											if "AutoImageProcessor" in config_dict.get('auto_map'       ,	{}						):
															snake_case__									=     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:
															snake_case__									=     config_dict.pop('feature_extractor_type'       ,	UpperCamelCase						)
															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.'						)
																			snake_case__									=     feature_extractor_class.replace('FeatureExtractor'       ,	'ImageProcessor'						)
															if "AutoFeatureExtractor" in config_dict.get('auto_map'       ,	{}						):
																			snake_case__									=     config_dict['auto_map']['AutoFeatureExtractor']
																			snake_case__									=     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(UpperCamelCase       ,	UpperCamelCase						):
																			snake_case__									=     AutoConfig.from_pretrained(UpperCamelCase       ,	**UpperCamelCase						)
															# It could be in `config.image_processor_type``
															snake_case__									=     getattr(UpperCamelCase       ,	'image_processor_type'       ,	UpperCamelCase						)
															if hasattr(UpperCamelCase       ,	'auto_map'						) and "AutoImageProcessor" in config.auto_map:
																			snake_case__									=     config.auto_map['AutoImageProcessor']

											if image_processor_class is not None:
															snake_case__									=     image_processor_class_from_name(UpperCamelCase						)

											snake_case__									=     image_processor_auto_map is not None
											snake_case__									=     image_processor_class is not None or type(UpperCamelCase						) in IMAGE_PROCESSOR_MAPPING
											snake_case__									=     resolve_trust_remote_code(
											    UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)

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

											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 lowerCAmelCase_ (     UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      int						)		->		Optional[Any]:
											IMAGE_PROCESSOR_MAPPING.register(UpperCamelCase       ,	UpperCamelCase						)




