"""simple docstring"""





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()
__A  =				logging.get_logger(__name__)

__A  =				{
    '''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    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Any      ) ->      Any:



							'''simple docstring'''

							for attribute in key.split("."      ):
														__lowerCamelCase					:		Union[str, Any]		   =     getattr(_lowerCamelCase						,   _lowerCamelCase      )

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

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


def    lowercase_   (   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	List[str]      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     []
							__lowerCamelCase					:		List[Any]		   =     fairseq_model.state_dict()

							__lowerCamelCase					:		Tuple		   =     hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														__lowerCamelCase					:		Dict		   =     False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   hf_model.config.feat_extract_norm == "group"						,   )
																					__lowerCamelCase					:		int		   =     True
														else:
																					for key, mapped_key in MAPPING.items():
																												__lowerCamelCase					:		int		   =     "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]:
																																			__lowerCamelCase					:		List[Any]		   =     True
																																			if "*" in mapped_key:
																																										__lowerCamelCase					:		Union[str, Any]		   =     name.split(_lowerCamelCase      )[0].split("."      )[-2]
																																										__lowerCamelCase					:		List[str]		   =     mapped_key.replace("*"						,   _lowerCamelCase      )
																																			if "weight_g" in name:
																																										__lowerCamelCase					:		List[str]		   =     "weight_g"
																																			elif "weight_v" in name:
																																										__lowerCamelCase					:		List[str]		   =     "weight_v"
																																			elif "weight" in name:
																																										__lowerCamelCase					:		int		   =     "weight"
																																			elif "bias" in name:
																																										__lowerCamelCase					:		str		   =     "bias"
																																			else:
																																										__lowerCamelCase					:		Tuple		   =     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    lowercase_   (   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	str						,   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	List[Any]      ) ->      List[Any]:



							'''simple docstring'''

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


def    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	str      ) ->      int:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     SEWConfig()
							if is_finetuned:
														__lowerCamelCase					:		int		   =     model.wav_encoder.wav_model.cfg
							else:
														__lowerCamelCase					:		Optional[int]		   =     model.cfg

							__lowerCamelCase					:		List[str]		   =     fs_config.conv_bias
							__lowerCamelCase					:		int		   =     eval(fs_config.conv_feature_layers      )
							__lowerCamelCase					:		Tuple		   =     [x[0] for x in conv_layers]
							__lowerCamelCase					:		Any		   =     [x[1] for x in conv_layers]
							__lowerCamelCase					:		Dict		   =     [x[2] for x in conv_layers]
							__lowerCamelCase					:		int		   =     "gelu"
							__lowerCamelCase					:		Tuple		   =     "layer" if fs_config.extractor_mode == "layer_norm" else "group"
							__lowerCamelCase					:		Optional[Any]		   =     0.0
							__lowerCamelCase					:		str		   =     fs_config.activation_fn.name
							__lowerCamelCase					:		Union[str, Any]		   =     fs_config.encoder_embed_dim
							__lowerCamelCase					:		Optional[Any]		   =     0.02
							__lowerCamelCase					:		List[Any]		   =     fs_config.encoder_ffn_embed_dim
							__lowerCamelCase					:		Any		   =     1E-5
							__lowerCamelCase					:		Dict		   =     fs_config.encoder_layerdrop
							__lowerCamelCase					:		List[str]		   =     fs_config.encoder_attention_heads
							__lowerCamelCase					:		Optional[int]		   =     fs_config.conv_pos_groups
							__lowerCamelCase					:		int		   =     fs_config.conv_pos
							__lowerCamelCase					:		Any		   =     len(_lowerCamelCase      )
							__lowerCamelCase					:		int		   =     fs_config.encoder_layers
							__lowerCamelCase					:		Tuple		   =     fs_config.squeeze_factor

							# take care of any params that are overridden by the Wav2VecCtc model
							if is_finetuned:
														__lowerCamelCase					:		Optional[int]		   =     model.cfg
														__lowerCamelCase					:		int		   =     fs_config.final_dropout
														__lowerCamelCase					:		Any		   =     fs_config.layerdrop
							__lowerCamelCase					:		List[Any]		   =     fs_config.activation_dropout
							__lowerCamelCase					:		List[str]		   =     fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
							__lowerCamelCase					:		Union[str, Any]		   =     fs_config.attention_dropout
							__lowerCamelCase					:		Dict		   =     fs_config.dropout_input
							__lowerCamelCase					:		Dict		   =     fs_config.dropout
							__lowerCamelCase					:		Any		   =     fs_config.mask_channel_length
							__lowerCamelCase					:		Union[str, Any]		   =     fs_config.mask_channel_prob
							__lowerCamelCase					:		str		   =     fs_config.mask_length
							__lowerCamelCase					:		Union[str, Any]		   =     fs_config.mask_prob

							__lowerCamelCase					:		int		   =     "Wav2Vec2FeatureExtractor"
							__lowerCamelCase					:		List[str]		   =     "Wav2Vec2CTCTokenizer"

							return config


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	Any						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	Tuple=None						,   _lowerCamelCase:	Tuple=None						,   _lowerCamelCase:	Tuple=True      ) ->      str:



							'''simple docstring'''

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

							if config_path is not None:
														__lowerCamelCase					:		Dict		   =     SEWConfig.from_pretrained(_lowerCamelCase      )
							else:
														__lowerCamelCase					:		Union[str, Any]		   =     convert_config(model[0]						,   _lowerCamelCase      )
							__lowerCamelCase					:		int		   =     model[0].eval()

							__lowerCamelCase					:		Union[str, Any]		   =     True if config.feat_extract_norm == "layer" else False
							__lowerCamelCase					:		Any		   =     WavaVecaFeatureExtractor(
							    feature_size=1						,   sampling_rate=16000						,   padding_value=0						,   do_normalize=_lowerCamelCase						,   return_attention_mask=_lowerCamelCase						,   )

							if is_finetuned:
														if dict_path:
																					__lowerCamelCase					:		Dict		   =     Dictionary.load(_lowerCamelCase      )

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					__lowerCamelCase					:		Dict		   =     target_dict.pad_index
																					__lowerCamelCase					:		Dict		   =     target_dict.bos_index
																					__lowerCamelCase					:		List[str]		   =     target_dict.pad_index
																					__lowerCamelCase					:		str		   =     target_dict.bos_index
																					__lowerCamelCase					:		List[Any]		   =     target_dict.eos_index
																					__lowerCamelCase					:		int		   =     len(target_dict.symbols      )
																					__lowerCamelCase					:		str		   =     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      )
																					with open(_lowerCamelCase						,   "w"						,   encoding="utf-8"      ) as vocab_handle:
																												json.dump(target_dict.indices						,   _lowerCamelCase      )
																					__lowerCamelCase					:		Optional[Any]		   =     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					:		List[str]		   =     WavaVecaProcessor(feature_extractor=_lowerCamelCase						,   tokenizer=_lowerCamelCase      )
																					processor.save_pretrained(_lowerCamelCase      )

														__lowerCamelCase					:		Tuple		   =     SEWForCTC(_lowerCamelCase      )
							else:
														__lowerCamelCase					:		Any		   =     SEWModel(_lowerCamelCase      )
														feature_extractor.save_pretrained(_lowerCamelCase      )

							recursively_load_weights(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )

							hf_model.save_pretrained(_lowerCamelCase      )


if __name__ == "__main__":
				__A  =				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'''
				)
				__A  =				parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)
"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import LevitImageProcessor
class  _snake_case     (					unittest.TestCase		):






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Optional[int]=7							,					UpperCAmelCase      :			Any=3							,					UpperCAmelCase      :			str=18							,					UpperCAmelCase      :			str=30							,					UpperCAmelCase      :			Any=400							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			Optional[Any]=True							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			List[str]=True							,					UpperCAmelCase      :			int=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			Tuple=[0.5, 0.5, 0.5]							,					):
													__lowerCamelCase					:		Any		   =     size if size is not None else {"shortest_edge": 18}
													__lowerCamelCase					:		Dict		   =     crop_size if crop_size is not None else {"height": 18, "width": 18}
													__lowerCamelCase					:		List[str]		   =     parent
													__lowerCamelCase					:		int		   =     batch_size
													__lowerCamelCase					:		Any		   =     num_channels
													__lowerCamelCase					:		Tuple		   =     image_size
													__lowerCamelCase					:		int		   =     min_resolution
													__lowerCamelCase					:		List[Any]		   =     max_resolution
													__lowerCamelCase					:		List[str]		   =     do_resize
													__lowerCamelCase					:		str		   =     size
													__lowerCamelCase					:		Tuple		   =     do_center_crop
													__lowerCamelCase					:		Optional[int]		   =     crop_size
													__lowerCamelCase					:		Optional[Any]		   =     do_normalize
													__lowerCamelCase					:		Optional[Any]		   =     image_mean
													__lowerCamelCase					:		List[Any]		   =     image_std







						def       lowerCamelCase__     (       self      :			int    ):
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "do_center_crop": self.do_center_crop,
													    "size": self.size,
													    "crop_size": self.crop_size,
													}

@require_torch
@require_vision
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			LevitImageProcessor if is_vision_available() else None






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[int]		   =     LevitImageProcessingTester(self    )






						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Dict		   =     self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_mean"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_std"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_normalize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_resize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_center_crop"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"size"    )    )






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		str		   =     self.image_processing_class.from_dict(self.image_processor_dict    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 18}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 18, "width": 18}    )

													__lowerCamelCase					:		Tuple		   =     self.image_processing_class.from_dict(self.image_processor_dict							,					size=42							,					crop_size=84    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 42}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 84, "width": 84}    )






						def       lowerCamelCase__     (       self      :			Dict    ):
													pass






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													__lowerCamelCase					:		List[str]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					Image.Image    )

													# Test not batched input
													__lowerCamelCase					:		Dict		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		int		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													__lowerCamelCase					:		Tuple		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					numpify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					np.ndarray    )

													# Test not batched input
													__lowerCamelCase					:		Optional[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		Dict		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)







						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													# Initialize image_processing
													__lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													__lowerCamelCase					:		List[Any]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					torchify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					torch.Tensor    )

													# Test not batched input
													__lowerCamelCase					:		List[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		List[str]		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)