"""simple docstring"""




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()
__lowerCAmelCase    :							Any													=						logging.get_logger(__name__)

__lowerCAmelCase    :							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",
}
__lowerCAmelCase    :							str													=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		for attribute in key.split("""."""     ):
				lowerCAmelCase__        =   getattr(lowerCamelCase__			,  lowerCamelCase__     )

		if weight_type is not None:
				lowerCAmelCase__        =   getattr(lowerCamelCase__			,  lowerCamelCase__     ).shape
		else:
				lowerCAmelCase__        =   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":
				lowerCAmelCase__        =   value
		elif weight_type == "weight_g":
				lowerCAmelCase__        =   value
		elif weight_type == "weight_v":
				lowerCAmelCase__        =   value
		elif weight_type == "bias":
				lowerCAmelCase__        =   value
		elif weight_type == "running_mean":
				lowerCAmelCase__        =   value
		elif weight_type == "running_var":
				lowerCAmelCase__        =   value
		elif weight_type == "num_batches_tracked":
				lowerCAmelCase__        =   value
		elif weight_type == "inv_freq":
				lowerCAmelCase__        =   value
		else:
				lowerCAmelCase__        =   value

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







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   []
		lowerCAmelCase__        =   fairseq_model.state_dict()

		lowerCAmelCase__        =   hf_model.wavaveca_conformer.feature_extractor

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

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







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   full_name.split("""conv_layers."""     )[-1]
		lowerCAmelCase__        =   name.split("""."""     )
		lowerCAmelCase__        =   int(items[0]     )
		lowerCAmelCase__        =   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."""     )
						lowerCAmelCase__        =   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."""     )
						lowerCAmelCase__        =   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."""     )
						lowerCAmelCase__        =   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."""     )
						lowerCAmelCase__        =   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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__=True     ):





		"""simple docstring"""




		if config_path is not None:
				lowerCAmelCase__        =   WavaVecaConformerConfig.from_pretrained(lowerCamelCase__			,  hidden_act="""swish"""     )
		else:
				lowerCAmelCase__        =   WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				lowerCAmelCase__        =   """rotary"""

		if is_finetuned:
				if dict_path:
						lowerCAmelCase__        =   Dictionary.load(lowerCamelCase__     )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						lowerCAmelCase__        =   target_dict.pad_index
						lowerCAmelCase__        =   target_dict.bos_index
						lowerCAmelCase__        =   target_dict.eos_index
						lowerCAmelCase__        =   len(target_dict.symbols     )
						lowerCAmelCase__        =   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__     )
						lowerCAmelCase__        =   target_dict.indices

						# fairseq has the <pad> and <s> switched
						lowerCAmelCase__        =   0
						lowerCAmelCase__        =   1
						with open(lowerCamelCase__			,  """w"""			,  encoding="""utf-8"""     ) as vocab_handle:
								json.dump(lowerCamelCase__			,  lowerCamelCase__     )
						lowerCAmelCase__        =   WavaVecaCTCTokenizer(
						    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__			,  )
						lowerCAmelCase__        =   True if config.feat_extract_norm == """layer""" else False
						lowerCAmelCase__        =   WavaVecaFeatureExtractor(
						    feature_size=1			,  sampling_rate=1_6000			,  padding_value=0			,  do_normalize=lowerCamelCase__			,  return_attention_mask=lowerCamelCase__			,  )
						lowerCAmelCase__        =   WavaVecaProcessor(feature_extractor=lowerCamelCase__			,  tokenizer=lowerCamelCase__     )
						processor.save_pretrained(lowerCamelCase__     )

				lowerCAmelCase__        =   WavaVecaConformerForCTC(lowerCamelCase__     )
		else:
				lowerCAmelCase__        =   WavaVecaConformerForPreTraining(lowerCamelCase__     )

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

				lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,  task=lowerCamelCase__     )

		lowerCAmelCase__        =   model[0].eval()

		recursively_load_weights(lowerCamelCase__			,  lowerCamelCase__			,  not is_finetuned     )

		hf_wavavec.save_pretrained(lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							Optional[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"
			)
			__lowerCAmelCase    :							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
			)





"""simple docstring"""




import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	str			=    LayoutLMTokenizer
					UpperCamelCase_ :	List[Any]			=    LayoutLMTokenizerFast
					UpperCamelCase_ :	Dict			=    True
					UpperCamelCase_ :	Any			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							super().setUp()

							lowerCAmelCase__        =   [
							    """[UNK]""",
							    """[CLS]""",
							    """[SEP]""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							lowerCAmelCase__        =   os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["""vocab_file"""]     )
							with open(self.vocab_file  ,						"""w"""  ,						encoding="""utf-8"""     ) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						**snake_case__						:     Union[str, Any]     ):
							return LayoutLMTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   """UNwant\u00E9d,running"""
							lowerCAmelCase__        =   """unwanted, running"""
							return input_text, output_text




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.tokenizer_class(self.vocab_file     )

							lowerCAmelCase__        =   tokenizer.tokenize("""UNwant\u00E9d,running"""     )
							self.assertListEqual(snake_case__  ,						["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]     )
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__     )  ,						[7, 4, 5, 10, 8, 9]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							pass




