
'''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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Any:


		'''simple docstring'''


		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
								snake_case  :       Union[str, Any]     			=						'''lm_head'''

				snake_case  :       Optional[Any]     			=						getattr(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)

		if weight_type is not None:
				snake_case  :       Optional[Any]     			=						getattr(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			).shape
		else:
				snake_case  :       int     			=						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":
				snake_case  :       Optional[Any]     			=						value
		elif weight_type == "weight_g":
				snake_case  :       Any     			=						value
		elif weight_type == "weight_v":
				snake_case  :       int     			=						value
		elif weight_type == "bias":
				snake_case  :       Union[str, Any]     			=						value
		else:
				snake_case  :       str     			=						value

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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		snake_case  :       int     			=						[]
		snake_case  :       Any     			=						fairseq_model.state_dict()

		snake_case  :       Dict     			=						hf_model.unispeech.feature_extractor

		for name, value in fairseq_dict.items():
				snake_case  :       Optional[int]     			=						False
				if "conv_layers" in name:
						load_conv_layer(
						    SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       hf_model.config.feat_extract_norm == '''group''' ,       )
						snake_case  :       Tuple     			=						True
				else:
						for key, mapped_key in MAPPING.items():
								snake_case  :       Any     			=						'''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]:
										snake_case  :       List[str]     			=						True
										if "*" in mapped_key:
												snake_case  :       Dict     			=						name.split(SCREAMING_SNAKE_CASE__			)[0].split('''.'''			)[-2]
												snake_case  :       List[Any]     			=						mapped_key.replace('''*''' ,       SCREAMING_SNAKE_CASE__			)
										if "weight_g" in name:
												snake_case  :       List[Any]     			=						'''weight_g'''
										elif "weight_v" in name:
												snake_case  :       Union[str, Any]     			=						'''weight_v'''
										elif "bias" in name:
												snake_case  :       List[str]     			=						'''bias'''
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												snake_case  :       Tuple     			=						'''weight'''
										else:
												snake_case  :       Dict     			=						None
										set_recursively(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
								continue
				if not is_used:
						unused_weights.append(SCREAMING_SNAKE_CASE__			)

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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[Any]:


		'''simple docstring'''


		snake_case  :       Optional[int]     			=						full_name.split('''conv_layers.'''			)[-1]
		snake_case  :       List[str]     			=						name.split('''.'''			)
		snake_case  :       List[str]     			=						int(items[0]			)
		snake_case  :       Optional[Any]     			=						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.'
						)
						snake_case  :       int     			=						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.'
						)
						snake_case  :       int     			=						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."
						)
						snake_case  :       Any     			=						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.'
						)
						snake_case  :       Dict     			=						value
						logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
		else:
				unused_weights.append(SCREAMING_SNAKE_CASE__			)




@torch.no_grad()
def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=None ,       SCREAMING_SNAKE_CASE__=None ,       SCREAMING_SNAKE_CASE__=True			)  ->							str:


		'''simple docstring'''


		if config_path is not None:
				snake_case  :       Tuple     			=						UniSpeechConfig.from_pretrained(SCREAMING_SNAKE_CASE__			)
		else:
				snake_case  :       Optional[Any]     			=						UniSpeechConfig()

		if is_finetuned:
				if dict_path:
						snake_case  :       List[str]     			=						Dictionary.load_from_json(SCREAMING_SNAKE_CASE__			)

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

						# fairseq has the <pad> and <s> switched
						snake_case  :       Tuple     			=						42
						snake_case  :       List[Any]     			=						43
						with open(SCREAMING_SNAKE_CASE__ ,       '''w''' ,       encoding='''utf-8'''			) as vocab_handle:
								json.dump(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
						snake_case  :       int     			=						WavaVecaPhonemeCTCTokenizer(
						    SCREAMING_SNAKE_CASE__ ,       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=SCREAMING_SNAKE_CASE__ ,       )
						snake_case  :       Tuple     			=						True if config.feat_extract_norm == '''layer''' else False
						snake_case  :       int     			=						WavaVecaFeatureExtractor(
						    feature_size=1 ,       sampling_rate=1_6000 ,       padding_value=0 ,       do_normalize=SCREAMING_SNAKE_CASE__ ,       return_attention_mask=SCREAMING_SNAKE_CASE__ ,       )
						snake_case  :       Union[str, Any]     			=						WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__ ,       tokenizer=SCREAMING_SNAKE_CASE__			)
						processor.save_pretrained(SCREAMING_SNAKE_CASE__			)

				snake_case  :       Optional[int]     			=						UniSpeechForCTC(SCREAMING_SNAKE_CASE__			)
		else:
				snake_case  :       int     			=						UniSpeechForPreTraining(SCREAMING_SNAKE_CASE__			)

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

		snake_case  :       Dict     			=						model[0].eval()

		recursively_load_weights(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)

		hf_unispeech.save_pretrained(SCREAMING_SNAKE_CASE__			)


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 dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						42







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		@register_to_config
		def __init__(      self				:			List[str]  ,	UpperCamelCase__				:			int = 16  ,	UpperCamelCase__				:			int = 88  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			int = 1  ,	UpperCamelCase__				:			float = 0.0  ,	UpperCamelCase__				:			int = 32  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			str = "geglu"  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			bool = True  ,	)						->			List[Any]:






				"""simple docstring"""







				super().__init__()
				snake_case  :       Optional[int]     			=						num_attention_heads
				snake_case  :       int     			=						attention_head_dim
				snake_case  :       Dict     			=						num_attention_heads * attention_head_dim

				snake_case  :       Tuple     			=						in_channels

				snake_case  :       Optional[int]     			=						torch.nn.GroupNorm(num_groups=UpperCamelCase__  ,	num_channels=UpperCamelCase__  ,	eps=1e-6  ,	affine=UpperCamelCase__      )
				snake_case  :       List[Any]     			=						nn.Linear(UpperCamelCase__  ,	UpperCamelCase__      )

				# 3. Define transformers blocks
				snake_case  :       Union[str, Any]     			=						nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	dropout=UpperCamelCase__  ,	cross_attention_dim=UpperCamelCase__  ,	activation_fn=UpperCamelCase__  ,	attention_bias=UpperCamelCase__  ,	double_self_attention=UpperCamelCase__  ,	norm_elementwise_affine=UpperCamelCase__  ,	)
				        for d in range(UpperCamelCase__      )
				    ]      )

				snake_case  :       Any     			=						nn.Linear(UpperCamelCase__  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			Any=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Union[str, Any]=1  ,	UpperCamelCase__				:			Optional[int]=None  ,	UpperCamelCase__				:			bool = True  ,	)						->			Any:






				"""simple docstring"""







				snake_case			,snake_case			,snake_case			,snake_case  :       Tuple     			=						hidden_states.shape
				snake_case  :       List[Any]     			=						batch_frames // num_frames

				snake_case  :       Union[str, Any]     			=						hidden_states

				snake_case  :       Optional[Any]     			=						hidden_states[None, :].reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				snake_case  :       int     			=						hidden_states.permute(0  ,	2  ,	1  ,	3  ,	4      )

				snake_case  :       Tuple     			=						self.norm(UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						hidden_states.permute(0  ,	3  ,	4  ,	2  ,	1      ).reshape(batch_size * height * width  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						self.proj_in(UpperCamelCase__      )

				# 2. Blocks
				for block in self.transformer_blocks:
						snake_case  :       Union[str, Any]     			=						block(
						    UpperCamelCase__  ,	encoder_hidden_states=UpperCamelCase__  ,	timestep=UpperCamelCase__  ,	cross_attention_kwargs=UpperCamelCase__  ,	class_labels=UpperCamelCase__  ,	)

				# 3. Output
				snake_case  :       Dict     			=						self.proj_out(UpperCamelCase__      )
				snake_case  :       Tuple     			=						(
				    hidden_states[None, None, :]
				    .reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				    .permute(0  ,	3  ,	4  ,	1  ,	2      )
				    .contiguous()
				)
				snake_case  :       List[Any]     			=						hidden_states.reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       Any     			=						hidden_states + residual

				if not return_dict:
						return (output,)

				return TransformerTemporalModelOutput(sample=UpperCamelCase__      )


