





import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


_lowerCamelCase 					=      logging.get_logger(__name__)

# General docstring
_lowerCamelCase 					=      'PoolFormerConfig'

# Base docstring
_lowerCamelCase 					=      'sail/poolformer_s12'
_lowerCamelCase 					=      [1, 512, 7, 7]

# Image classification docstring
_lowerCamelCase 					=      'sail/poolformer_s12'
_lowerCamelCase 					=      'tabby, tabby cat'

_lowerCamelCase 					=      [
    'sail/poolformer_s12',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				float = 0.0       , UpperCamelCase__:				bool = False     ):
		if drop_prob == 0.0 or not training:
				return input
		SCREAMING_SNAKE_CASE__								=   1 - drop_prob
		SCREAMING_SNAKE_CASE__								=   (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
		SCREAMING_SNAKE_CASE__								=   keep_prob + torch.rand(UpperCamelCase__       , dtype=input.dtype       , device=input.device     )
		random_tensor.floor_()  # binarize
		SCREAMING_SNAKE_CASE__								=   input.div(UpperCamelCase__     ) * random_tensor
		return output




class 	UpperCamelCase_							(  nn.Module     ):


							def __init__(				self     :Optional[Any]				,					__A     :Optional[float] = None		) -> None:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__								=   drop_prob


							def   _snake_case	(				self     :Any				,					__A     :torch.Tensor		) -> torch.Tensor:



									"""simple docstring"""



									return drop_path(__A				,					self.drop_prob				,					self.training		)


							def   _snake_case	(				self     :Dict		) -> str:



									"""simple docstring"""



									return "p={}".format(self.drop_prob		)




class 	UpperCamelCase_							(  nn.Module     ):


							def __init__(				self     :Dict				,					__A     :Optional[Any]				,					__A     :Dict				,					__A     :List[str]				,					__A     :Optional[Any]				,					__A     :Tuple				,					__A     :Optional[Any]=None		) -> Union[str, Any]:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__								=   patch_size if isinstance(__A				,					collections.abc.Iterable		) else (patch_size, patch_size)
									SCREAMING_SNAKE_CASE__								=   stride if isinstance(__A				,					collections.abc.Iterable		) else (stride, stride)
									SCREAMING_SNAKE_CASE__								=   padding if isinstance(__A				,					collections.abc.Iterable		) else (padding, padding)

									SCREAMING_SNAKE_CASE__								=   nn.Convad(__A				,					__A				,					kernel_size=__A				,					stride=__A				,					padding=__A		)
									SCREAMING_SNAKE_CASE__								=   norm_layer(__A		) if norm_layer else nn.Identity()


							def   _snake_case	(				self     :Dict				,					__A     :Union[str, Any]		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.projection(__A		)
									SCREAMING_SNAKE_CASE__								=   self.norm(__A		)
									return embeddings




class 	UpperCamelCase_							(  nn.GroupNorm     ):


							def __init__(				self     :Dict				,					__A     :Tuple				,					**__A     :Union[str, Any]		) -> Dict:



									"""simple docstring"""



									super().__init__(1				,					__A				,					**__A		)




class 	UpperCamelCase_							(  nn.Module     ):


							def __init__(				self     :List[str]				,					__A     :Optional[int]		) -> Any:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__								=   nn.AvgPoolad(__A				,					stride=1				,					padding=pool_size // 2				,					count_include_pad=__A		)


							def   _snake_case	(				self     :Any				,					__A     :Optional[Any]		) -> Optional[Any]:



									"""simple docstring"""



									return self.pool(__A		) - hidden_states




class 	UpperCamelCase_							(  nn.Module     ):


							def __init__(				self     :Optional[Any]				,					__A     :Tuple				,					__A     :Dict				,					__A     :int				,					__A     :Any		) -> str:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__								=   nn.Convad(__A				,					__A				,					1		)
									SCREAMING_SNAKE_CASE__								=   nn.Convad(__A				,					__A				,					1		)
									SCREAMING_SNAKE_CASE__								=   PoolFormerDropPath(__A		)
									if isinstance(config.hidden_act				,					__A		):
											SCREAMING_SNAKE_CASE__								=   ACTaFN[config.hidden_act]
									else:
											SCREAMING_SNAKE_CASE__								=   config.hidden_act


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Optional[int]		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.conva(__A		)
									SCREAMING_SNAKE_CASE__								=   self.act_fn(__A		)
									SCREAMING_SNAKE_CASE__								=   self.drop(__A		)
									SCREAMING_SNAKE_CASE__								=   self.conva(__A		)
									SCREAMING_SNAKE_CASE__								=   self.drop(__A		)

									return hidden_states




class 	UpperCamelCase_							(  nn.Module     ):


							def __init__(				self     :Any				,					__A     :str				,					__A     :List[str]				,					__A     :Tuple				,					__A     :Dict				,					__A     :Union[str, Any]				,					__A     :int		) -> Optional[int]:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__								=   PoolFormerPooling(__A		)
									SCREAMING_SNAKE_CASE__								=   PoolFormerOutput(__A				,					__A				,					__A				,					__A		)
									SCREAMING_SNAKE_CASE__								=   PoolFormerGroupNorm(__A		)
									SCREAMING_SNAKE_CASE__								=   PoolFormerGroupNorm(__A		)

									# Useful for training neural nets
									SCREAMING_SNAKE_CASE__								=   PoolFormerDropPath(__A		) if drop_path > 0.0 else nn.Identity()
									SCREAMING_SNAKE_CASE__								=   config.use_layer_scale
									if config.use_layer_scale:
											SCREAMING_SNAKE_CASE__								=   nn.Parameter(
											    config.layer_scale_init_value * torch.ones((__A)		)				,					requires_grad=__A		)
											SCREAMING_SNAKE_CASE__								=   nn.Parameter(
											    config.layer_scale_init_value * torch.ones((__A)		)				,					requires_grad=__A		)


							def   _snake_case	(				self     :Optional[Any]				,					__A     :Optional[int]		) -> str:



									"""simple docstring"""



									if self.use_layer_scale:
											SCREAMING_SNAKE_CASE__								=   self.pooling(self.before_norm(__A		)		)
											SCREAMING_SNAKE_CASE__								=   self.layer_scale_a.unsqueeze(-1		).unsqueeze(-1		) * pooling_output
											# First residual connection
											SCREAMING_SNAKE_CASE__								=   hidden_states + self.drop_path(__A		)
											SCREAMING_SNAKE_CASE__								=   ()

											SCREAMING_SNAKE_CASE__								=   self.output(self.after_norm(__A		)		)
											SCREAMING_SNAKE_CASE__								=   self.layer_scale_a.unsqueeze(-1		).unsqueeze(-1		) * layer_output
											# Second residual connection
											SCREAMING_SNAKE_CASE__								=   hidden_states + self.drop_path(__A		)

											SCREAMING_SNAKE_CASE__								=   (output,) + outputs
											return outputs

									else:
											SCREAMING_SNAKE_CASE__								=   self.drop_path(self.pooling(self.before_norm(__A		)		)		)
											# First residual connection
											SCREAMING_SNAKE_CASE__								=   pooling_output + hidden_states
											SCREAMING_SNAKE_CASE__								=   ()

											# Second residual connection inside the PoolFormerOutput block
											SCREAMING_SNAKE_CASE__								=   self.drop_path(self.output(self.after_norm(__A		)		)		)
											SCREAMING_SNAKE_CASE__								=   hidden_states + layer_output

											SCREAMING_SNAKE_CASE__								=   (output,) + outputs
											return outputs




class 	UpperCamelCase_							(  nn.Module     ):


							def __init__(				self     :Union[str, Any]				,					__A     :List[Any]		) -> Union[str, Any]:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__								=   config
									# stochastic depth decay rule
									SCREAMING_SNAKE_CASE__								=   [x.item() for x in torch.linspace(0				,					config.drop_path_rate				,					sum(config.depths		)		)]

									# patch embeddings
									SCREAMING_SNAKE_CASE__								=   []
									for i in range(config.num_encoder_blocks		):
											embeddings.append(
											    PoolFormerEmbeddings(
											        patch_size=config.patch_sizes[i]				,					stride=config.strides[i]				,					padding=config.padding[i]				,					num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]				,					hidden_size=config.hidden_sizes[i]				,					)		)
									SCREAMING_SNAKE_CASE__								=   nn.ModuleList(__A		)

									# Transformer blocks
									SCREAMING_SNAKE_CASE__								=   []
									SCREAMING_SNAKE_CASE__								=   0
									for i in range(config.num_encoder_blocks		):
											# each block consists of layers
											SCREAMING_SNAKE_CASE__								=   []
											if i != 0:
													cur += config.depths[i - 1]
											for j in range(config.depths[i]		):
													layers.append(
													    PoolFormerLayer(
													        __A				,					num_channels=config.hidden_sizes[i]				,					pool_size=config.pool_size				,					hidden_size=config.hidden_sizes[i]				,					intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio		)				,					drop_path=dpr[cur + j]				,					)		)
											blocks.append(nn.ModuleList(__A		)		)

									SCREAMING_SNAKE_CASE__								=   nn.ModuleList(__A		)


							def   _snake_case	(				self     :str				,					__A     :Tuple				,					__A     :Dict=False				,					__A     :Tuple=True		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   () if output_hidden_states else None

									SCREAMING_SNAKE_CASE__								=   pixel_values
									for idx, layers in enumerate(zip(self.patch_embeddings				,					self.block		)		):
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   layers
											# Get patch embeddings from hidden_states
											SCREAMING_SNAKE_CASE__								=   embedding_layer(__A		)
											# Send the embeddings through the blocks
											for _, blk in enumerate(__A		):
													SCREAMING_SNAKE_CASE__								=   blk(__A		)
													SCREAMING_SNAKE_CASE__								=   layer_outputs[0]

											if output_hidden_states:
													SCREAMING_SNAKE_CASE__								=   all_hidden_states + (hidden_states,)

									if not return_dict:
											return tuple(v for v in [hidden_states, all_hidden_states] if v is not None		)

									return BaseModelOutputWithNoAttention(last_hidden_state=__A				,					hidden_states=__A		)




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       PoolFormerConfig
							lowerCamelCase_							     =       "poolformer"
							lowerCamelCase_							     =       "pixel_values"
							lowerCamelCase_							     =       True


							def   _snake_case	(				self     :Optional[Any]				,					__A     :Tuple		) -> Dict:



									"""simple docstring"""



									if isinstance(__A				,					(nn.Linear, nn.Convad)		):
											module.weight.data.normal_(mean=0.0				,					std=self.config.initializer_range		)
											if module.bias is not None:
													module.bias.data.zero_()
									elif isinstance(__A				,					nn.LayerNorm		):
											module.bias.data.zero_()
											module.weight.data.fill_(1.0		)


							def   _snake_case	(				self     :str				,					__A     :Optional[Any]				,					__A     :Union[str, Any]=False		) -> Any:



									"""simple docstring"""



									if isinstance(__A				,					__A		):
											SCREAMING_SNAKE_CASE__								=   value



_lowerCamelCase 					=      R'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

_lowerCamelCase 					=      R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`PoolFormerImageProcessor.__call__`] for details.\n'




@add_start_docstrings(
    "The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top."					,	UpperCamelCase__					,	)
class 	UpperCamelCase_							(  UpperCamelCase__     ):


							def __init__(				self     :Union[str, Any]				,					__A     :Any		) -> int:



									"""simple docstring"""



									super().__init__(__A		)
									SCREAMING_SNAKE_CASE__								=   config

									SCREAMING_SNAKE_CASE__								=   PoolFormerEncoder(__A		)

									# Initialize weights and apply final processing
									self.post_init()


							def   _snake_case	(				self     :Union[str, Any]		) -> Union[str, Any]:



									"""simple docstring"""



									return self.embeddings.patch_embeddings


							@add_start_docstrings_to_model_forward(__A		)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC				,					output_type=__A				,					config_class=_CONFIG_FOR_DOC				,					modality="""vision"""				,					expected_output=_EXPECTED_OUTPUT_SHAPE				,					)
							def   _snake_case	(				self     :Dict				,					__A     :Optional[torch.FloatTensor] = None				,					__A     :Optional[bool] = None				,					__A     :Optional[bool] = None				,					) -> Union[Tuple, BaseModelOutputWithNoAttention]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									SCREAMING_SNAKE_CASE__								=   return_dict if return_dict is not None else self.config.use_return_dict

									if pixel_values is None:
											raise ValueError("""You have to specify pixel_values"""		)

									SCREAMING_SNAKE_CASE__								=   self.encoder(
									    __A				,					output_hidden_states=__A				,					return_dict=__A				,					)
									SCREAMING_SNAKE_CASE__								=   encoder_outputs[0]

									if not return_dict:
											return (sequence_output, None) + encoder_outputs[1:]

									return BaseModelOutputWithNoAttention(
									    last_hidden_state=__A				,					hidden_states=encoder_outputs.hidden_states				,					)




class 	UpperCamelCase_							(  nn.Module     ):


							def __init__(				self     :int				,					__A     :Optional[int]		) -> Tuple:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__								=   nn.Linear(config.hidden_size				,					config.hidden_size		)


							def   _snake_case	(				self     :List[Any]				,					__A     :Dict		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.dense(__A		)
									return output




@add_start_docstrings(
    "\n    PoolFormer Model transformer with an image classification head on top\n    "					,	UpperCamelCase__					,	)
class 	UpperCamelCase_							(  UpperCamelCase__     ):


							def __init__(				self     :str				,					__A     :Union[str, Any]		) -> int:



									"""simple docstring"""



									super().__init__(__A		)
									SCREAMING_SNAKE_CASE__								=   config.num_labels
									SCREAMING_SNAKE_CASE__								=   PoolFormerModel(__A		)

									# Final norm
									SCREAMING_SNAKE_CASE__								=   PoolFormerGroupNorm(config.hidden_sizes[-1]		)
									# Classifier head
									SCREAMING_SNAKE_CASE__								=   (
									    nn.Linear(config.hidden_sizes[-1]				,					config.num_labels		) if config.num_labels > 0 else nn.Identity()
									)

									# Initialize weights and apply final processing
									self.post_init()


							@add_start_docstrings_to_model_forward(__A		)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT				,					output_type=__A				,					config_class=_CONFIG_FOR_DOC				,					expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,					)
							def   _snake_case	(				self     :int				,					__A     :Optional[torch.FloatTensor] = None				,					__A     :Optional[torch.LongTensor] = None				,					__A     :Optional[bool] = None				,					__A     :Optional[bool] = None				,					) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE__								=   self.poolformer(
									    __A				,					output_hidden_states=__A				,					return_dict=__A				,					)

									SCREAMING_SNAKE_CASE__								=   outputs[0]

									SCREAMING_SNAKE_CASE__								=   self.classifier(self.norm(__A		).mean([-2, -1]		)		)

									SCREAMING_SNAKE_CASE__								=   None
									if labels is not None:
											if self.config.problem_type is None:
													if self.num_labels == 1:
															SCREAMING_SNAKE_CASE__								=   """regression"""
													elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
															SCREAMING_SNAKE_CASE__								=   """single_label_classification"""
													else:
															SCREAMING_SNAKE_CASE__								=   """multi_label_classification"""

											if self.config.problem_type == "regression":
													SCREAMING_SNAKE_CASE__								=   MSELoss()
													if self.num_labels == 1:
															SCREAMING_SNAKE_CASE__								=   loss_fct(logits.squeeze()				,					labels.squeeze()		)
													else:
															SCREAMING_SNAKE_CASE__								=   loss_fct(__A				,					__A		)
											elif self.config.problem_type == "single_label_classification":
													SCREAMING_SNAKE_CASE__								=   CrossEntropyLoss()
													SCREAMING_SNAKE_CASE__								=   loss_fct(logits.view(-1				,					self.num_labels		)				,					labels.view(-1		)		)
											elif self.config.problem_type == "multi_label_classification":
													SCREAMING_SNAKE_CASE__								=   BCEWithLogitsLoss()
													SCREAMING_SNAKE_CASE__								=   loss_fct(__A				,					__A		)

									if not return_dict:
											SCREAMING_SNAKE_CASE__								=   (logits,) + outputs[2:]
											return ((loss,) + output) if loss is not None else output

									return ImageClassifierOutputWithNoAttention(loss=__A				,					logits=__A				,					hidden_states=outputs.hidden_states		)




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

UpperCAmelCase_		 =					{
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Union[str, Any]	,   _snake_case	:str	,   _snake_case	:Any	,   _snake_case	:int	,   _snake_case	:List[Any]				)  ->     Optional[int]:
   for attribute in key.split('''.'''				):
      _A      =       getattr(_snake_case	,   _snake_case				)

   if weight_type is not None:
      _A      =       getattr(_snake_case	,   _snake_case				).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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Union[str, Any]	,   _snake_case	:Any	,   _snake_case	:int				)  ->     Any:
   _A      =       []
   _A      =       fairseq_model.state_dict()

   _A      =       hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _A      =       False
      if "conv_layers" in name:
         load_conv_layer(
             _snake_case	,   _snake_case	,   _snake_case	,   _snake_case	,   hf_model.config.feat_extract_norm == '''group'''	,   )
         _A      =       True
      else:
         for key, mapped_key in MAPPING.items():
            _A      =       '''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') 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(_snake_case				)[0].split('''.'''				)[-2]
                  _A      =       mapped_key.replace('''*'''	,   _snake_case				)
               if "weight_g" in name:
                  _A      =       '''weight_g'''
               elif "weight_v" in name:
                  _A      =       '''weight_v'''
               elif "weight" in name:
                  _A      =       '''weight'''
               elif "bias" in name:
                  _A      =       '''bias'''
               else:
                  _A      =       None
               set_recursively(_snake_case	,   _snake_case	,   _snake_case	,   _snake_case	,   _snake_case				)
            continue
      if not is_used:
         unused_weights.append(_snake_case				)

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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Tuple	,   _snake_case	:List[str]	,   _snake_case	:List[str]	,   _snake_case	:Optional[int]	,   _snake_case	:List[Any]				)  ->     Any:
   _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(_snake_case				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:Dict				)  ->     Tuple:
   _A      =       SEWConfig()
   if is_finetuned:
      _A      =       model.wav_encoder.wav_model.cfg
   else:
      _A      =       model.cfg

   _A      =       fs_config.conv_bias
   _A      =       eval(fs_config.conv_feature_layers				)
   _A      =       [x[0] for x in conv_layers]
   _A      =       [x[1] for x in conv_layers]
   _A      =       [x[2] for x in conv_layers]
   _A      =       '''gelu'''
   _A      =       '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
   _A      =       0.0
   _A      =       fs_config.activation_fn.name
   _A      =       fs_config.encoder_embed_dim
   _A      =       0.02
   _A      =       fs_config.encoder_ffn_embed_dim
   _A      =       1E-5
   _A      =       fs_config.encoder_layerdrop
   _A      =       fs_config.encoder_attention_heads
   _A      =       fs_config.conv_pos_groups
   _A      =       fs_config.conv_pos
   _A      =       len(_snake_case				)
   _A      =       fs_config.encoder_layers
   _A      =       fs_config.squeeze_factor

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      _A      =       model.cfg
      _A      =       fs_config.final_dropout
      _A      =       fs_config.layerdrop
   _A      =       fs_config.activation_dropout
   _A      =       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   _A      =       fs_config.attention_dropout
   _A      =       fs_config.dropout_input
   _A      =       fs_config.dropout
   _A      =       fs_config.mask_channel_length
   _A      =       fs_config.mask_channel_prob
   _A      =       fs_config.mask_length
   _A      =       fs_config.mask_prob

   _A      =       '''Wav2Vec2FeatureExtractor'''
   _A      =       '''Wav2Vec2CTCTokenizer'''

   return config

@torch.no_grad()
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[int]	,   _snake_case	:Union[str, Any]	,   _snake_case	:Optional[Any]=None	,   _snake_case	:Optional[int]=None	,   _snake_case	:Dict=True				)  ->     List[Any]:

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

   if config_path is not None:
      _A      =       SEWConfig.from_pretrained(_snake_case				)
   else:
      _A      =       convert_config(model[0]	,   _snake_case				)
   _A      =       model[0].eval()

   _A      =       True if config.feat_extract_norm == '''layer''' else False
   _A      =       WavaVecaFeatureExtractor(
       feature_size=1	,   sampling_rate=16_000	,   padding_value=0	,   do_normalize=_snake_case	,   return_attention_mask=_snake_case	,   )

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

         # 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.pad_index
         _A      =       target_dict.bos_index
         _A      =       target_dict.eos_index
         _A      =       len(target_dict.symbols				)
         _A      =       os.path.join(_snake_case	,   '''vocab.json'''				)
         if not os.path.isdir(_snake_case				):
            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_snake_case				)				)
            return
         os.makedirs(_snake_case	,   exist_ok=_snake_case				)
         with open(_snake_case	,   '''w'''	,   encoding='''utf-8'''				) as vocab_handle:
            json.dump(target_dict.indices	,   _snake_case				)
         _A      =       WavaVecaCTCTokenizer(
             _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=_snake_case	,   )
         _A      =       WavaVecaProcessor(feature_extractor=_snake_case	,   tokenizer=_snake_case				)
         processor.save_pretrained(_snake_case				)

      _A      =       SEWForCTC(_snake_case				)
   else:
      _A      =       SEWModel(_snake_case				)
      feature_extractor.save_pretrained(_snake_case				)

   recursively_load_weights(_snake_case	,   _snake_case	,   _snake_case				)

   hf_model.save_pretrained(_snake_case				)


if __name__ == "__main__":
   UpperCAmelCase_		 =					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(
       """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
   )
   UpperCAmelCase_		 =					parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )



