


"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
lowercase_			     =  [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     Any			) ->      Dict:
		for attribute in key.split('''.'''			):
				if is_finetuned:
						if attribute in ["quantizer", "project_q", "project_hid"]:
								# those layers are only relevant for pretraining and should be dropped
								return

						if attribute == "ctc_proj":
								# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
								__a           =			'''lm_head'''

				__a           =			getattr(lowerCAmelCase__    ,					lowerCAmelCase__			)

		if weight_type is not None:
				__a           =			getattr(lowerCAmelCase__    ,					lowerCAmelCase__			).shape
		else:
				__a           =			hf_pointer.shape

		assert hf_shape == value.shape, (
		    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":
				__a           =			value
		elif weight_type == "weight_g":
				__a           =			value
		elif weight_type == "weight_v":
				__a           =			value
		elif weight_type == "bias":
				__a           =			value
		else:
				__a           =			value

		logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''			)

def        lowercase							(		lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     Tuple			) ->      str:
		__a           =			[]
		__a           =			fairseq_model.state_dict()

		__a           =			hf_model.unispeech.feature_extractor

		for name, value in fairseq_dict.items():
				__a           =			False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					hf_model.config.feat_extract_norm == '''group'''    ,					)
						__a           =			True
				else:
						for key, mapped_key in MAPPING.items():
								__a           =			'''unispeech.''' + 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]:
										__a           =			True
										if "*" in mapped_key:
												__a           =			name.split(lowerCAmelCase__			)[0].split('''.'''			)[-2]
												__a           =			mapped_key.replace('''*'''    ,					lowerCAmelCase__			)
										if "weight_g" in name:
												__a           =			'''weight_g'''
										elif "weight_v" in name:
												__a           =			'''weight_v'''
										elif "bias" in name:
												__a           =			'''bias'''
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												__a           =			'''weight'''
										else:
												__a           =			None
										set_recursively(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
								continue
				if not is_used:
						unused_weights.append(lowerCAmelCase__			)

		logger.warning(f'''Unused weights: {unused_weights}'''			)

def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     List[Any]    ,					lowerCAmelCase__ :     int			) ->      Optional[int]:
		__a           =			full_name.split('''conv_layers.'''			)[-1]
		__a           =			name.split('''.'''			)
		__a           =			int(items[0]			)
		__a           =			int(items[1]			)

		if type_id == 0:
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
						    f'''{full_name} has size {value.shape}, but'''
						    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
						)
						__a           =			value
						logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''			)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
						    f'''{full_name} has size {value.shape}, but'''
						    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
						)
						__a           =			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:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
						    f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
						    " found."
						)
						__a           =			value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
						    f'''{full_name} has size {value.shape}, but'''
						    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
						)
						__a           =			value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
		else:
				unused_weights.append(lowerCAmelCase__			)

@torch.no_grad()
def        lowercase							(		lowerCAmelCase__ :     List[Any]    ,					lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Optional[int]=None    ,					lowerCAmelCase__ :     List[str]=None    ,					lowerCAmelCase__ :     Dict=True			) ->      List[str]:
		if config_path is not None:
				__a           =			UniSpeechConfig.from_pretrained(lowerCAmelCase__			)
		else:
				__a           =			UniSpeechConfig()

		if is_finetuned:
				if dict_path:
						__a           =			Dictionary.load_from_json(lowerCAmelCase__			)

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

						# fairseq has the <pad> and <s> switched
						__a           =			42
						__a           =			43
						with open(lowerCAmelCase__    ,					'''w'''    ,					encoding='''utf-8'''			) as vocab_handle:
								json.dump(lowerCAmelCase__    ,					lowerCAmelCase__			)
						__a           =			WavaVecaPhonemeCTCTokenizer(
						    lowerCAmelCase__    ,					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=lowerCAmelCase__    ,					)
						__a           =			True if config.feat_extract_norm == '''layer''' else False
						__a           =			WavaVecaFeatureExtractor(
						    feature_size=1    ,					sampling_rate=16000    ,					padding_value=0    ,					do_normalize=lowerCAmelCase__    ,					return_attention_mask=lowerCAmelCase__    ,					)
						__a           =			WavaVecaProcessor(feature_extractor=lowerCAmelCase__    ,					tokenizer=lowerCAmelCase__			)
						processor.save_pretrained(lowerCAmelCase__			)

				__a           =			UniSpeechForCTC(lowerCAmelCase__			)
		else:
				__a           =			UniSpeechForPreTraining(lowerCAmelCase__			)

		if is_finetuned:
				__a      ,     __a      ,     __a           =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]    ,					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''			)[:-1]			), '''w2v_path''': checkpoint_path}			)
		else:
				__a      ,     __a      ,     __a           =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

		__a           =			model[0].eval()

		recursively_load_weights(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)

		hf_unispeech.save_pretrained(lowerCAmelCase__			)


if __name__ == "__main__":
				lowercase_			     =  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"
				)
				lowercase_			     =  parser.parse_args()
				convert_unispeech_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)










"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]										=				'beit'

		def __init__(  self							, _a=8_192							, _a=768							, _a=12							, _a=12							, _a=3_072							, _a="gelu"							, _a=0.0							, _a=0.0							, _a=0.02							, _a=1E-12							, _a=224							, _a=16							, _a=3							, _a=False							, _a=False							, _a=False							, _a=False							, _a=0.1							, _a=0.1							, _a=True							, _a=[3, 5, 7, 11]							, _a=[1, 2, 3, 6]							, _a=True							, _a=0.4							, _a=256							, _a=1							, _a=False							, _a=255							, **_a							, ):
				super().__init__(**_a      )

				__a           =			vocab_size
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			intermediate_size
				__a           =			hidden_act
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			initializer_range
				__a           =			layer_norm_eps

				__a           =			image_size
				__a           =			patch_size
				__a           =			num_channels
				__a           =			use_mask_token
				__a           =			use_absolute_position_embeddings
				__a           =			use_relative_position_bias
				__a           =			use_shared_relative_position_bias
				__a           =			layer_scale_init_value
				__a           =			drop_path_rate
				__a           =			use_mean_pooling
				# decode head attributes (semantic segmentation)
				__a           =			out_indices
				__a           =			pool_scales
				# auxiliary head attributes (semantic segmentation)
				__a           =			use_auxiliary_head
				__a           =			auxiliary_loss_weight
				__a           =			auxiliary_channels
				__a           =			auxiliary_num_convs
				__a           =			auxiliary_concat_input
				__a           =			semantic_loss_ignore_index







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[Any]										=				version.parse('1.11'       )

		@property
		def 			__UpperCAmelCase       (  self      ):
				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )





		@property
		def 			__UpperCAmelCase       (  self      ):
				return 1E-4






