


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
a			:			int												=					logging.get_logger(__name__)

a			:			Any												=					{
    '''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''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''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''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
a			:			str												=					[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = {}
			with open(_UpperCamelCase							,      '''r'''	) as file:
						for line_number, line in enumerate(_UpperCamelCase	):
									__lowercase			       = line.strip()
									if line:
												__lowercase			       = line.split()
												__lowercase			       = line_number
												__lowercase			       = words[0]
												__lowercase			       = value
			return result


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			for attribute in key.split('''.'''	):
						__lowercase			       = getattr(_UpperCamelCase							,      _UpperCamelCase	)

			__lowercase			       = None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(_UpperCamelCase	):
									__lowercase			       = PARAM_MAPPING[full_name.split('''.'''	)[-1]]
									__lowercase			       = '''param'''

			if weight_type is not None and weight_type != "param":
						__lowercase			       = getattr(_UpperCamelCase							,      _UpperCamelCase	).shape
			elif weight_type is not None and weight_type == "param":
						__lowercase			       = hf_pointer
						for attribute in hf_param_name.split('''.'''	):
									__lowercase			       = getattr(_UpperCamelCase							,      _UpperCamelCase	)
						__lowercase			       = shape_pointer.shape

						# let's reduce dimension
						__lowercase			       = value[0]
			else:
						__lowercase			       = 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":
						__lowercase			       = value
			elif weight_type == "weight_g":
						__lowercase			       = value
			elif weight_type == "weight_v":
						__lowercase			       = value
			elif weight_type == "bias":
						__lowercase			       = value
			elif weight_type == "param":
						for attribute in hf_param_name.split('''.'''	):
									__lowercase			       = getattr(_UpperCamelCase							,      _UpperCamelCase	)
						__lowercase			       = value
			else:
						__lowercase			       = value

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


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = None
			for param_key in PARAM_MAPPING.keys():
						if full_name.endswith(_UpperCamelCase	):
									__lowercase			       = PARAM_MAPPING[full_name.split('''.'''	)[-1]]
									__lowercase			       = '''param'''

			if weight_type is not None and weight_type != "param":
						__lowercase			       = '''.'''.join([key, weight_type]	)
			elif weight_type is not None and weight_type == "param":
						__lowercase			       = '''.'''.join([key, hf_param_name]	)
			else:
						__lowercase			       = key

			__lowercase			       = value if '''lm_head''' in full_key else value[0]


a			:			Optional[Any]												=					{
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = False
			for key, mapped_key in MAPPING.items():
						__lowercase			       = '''wav2vec2.''' + 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]:
									__lowercase			       = True
									if "*" in mapped_key:
												__lowercase			       = name.split(_UpperCamelCase	)[0].split('''.'''	)[-2]
												__lowercase			       = mapped_key.replace('''*'''							,      _UpperCamelCase	)
									if "weight_g" in name:
												__lowercase			       = '''weight_g'''
									elif "weight_v" in name:
												__lowercase			       = '''weight_v'''
									elif "bias" in name:
												__lowercase			       = '''bias'''
									elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												__lowercase			       = '''weight'''
									else:
												__lowercase			       = None
									if hf_dict is not None:
												rename_dict(_UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	)
									else:
												set_recursively(_UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	)
									return is_used
			return is_used


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = []
			__lowercase			       = fairseq_model.state_dict()

			__lowercase			       = hf_model.wavaveca.feature_extractor

			for name, value in fairseq_dict.items():
						__lowercase			       = False
						if "conv_layers" in name:
									load_conv_layer(
									    _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      hf_model.config.feat_extract_norm == '''group'''							,      )
									__lowercase			       = True
						else:
									__lowercase			       = load_wavaveca_layer(_UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	)
						if not is_used:
									unused_weights.append(_UpperCamelCase	)

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


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = full_name.split('''conv_layers.'''	)[-1]
			__lowercase			       = name.split('''.'''	)
			__lowercase			       = int(items[0]	)
			__lowercase			       = 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.'	)
									__lowercase			       = 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.'	)
									__lowercase			       = 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.'	)
									__lowercase			       = 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.'	)
									__lowercase			       = value
									logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'	)
			else:
						unused_weights.append(_UpperCamelCase	)


@torch.no_grad()
def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None							,      _UpperCamelCase=None							,      _UpperCamelCase=True							,      _UpperCamelCase=False	):


			'''simple docstring'''







			if config_path is not None:
						__lowercase			       = WavaVecaConfig.from_pretrained(_UpperCamelCase	)
			else:
						__lowercase			       = WavaVecaConfig()

			if is_seq_class:
						__lowercase			       = read_txt_into_dict(_UpperCamelCase	)
						__lowercase			       = idalabel
						__lowercase			       = WavaVecaForSequenceClassification(_UpperCamelCase	)
						__lowercase			       = WavaVecaFeatureExtractor(
						    feature_size=1							,      sampling_rate=1_60_00							,      padding_value=0							,      do_normalize=_UpperCamelCase							,      return_attention_mask=_UpperCamelCase							,      )
						feature_extractor.save_pretrained(_UpperCamelCase	)

			elif is_finetuned:
						if dict_path:
									__lowercase			       = Dictionary.load(_UpperCamelCase	)

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

									# fairseq has the <pad> and <s> switched
									__lowercase			       = 0
									__lowercase			       = 1
									with open(_UpperCamelCase							,      '''w'''							,      encoding='''utf-8'''	) as vocab_handle:
												json.dump(_UpperCamelCase							,      _UpperCamelCase	)
									__lowercase			       = WavaVecaCTCTokenizer(
									    _UpperCamelCase							,      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=_UpperCamelCase							,      )
									__lowercase			       = True if config.feat_extract_norm == '''layer''' else False
									__lowercase			       = WavaVecaFeatureExtractor(
									    feature_size=1							,      sampling_rate=1_60_00							,      padding_value=0							,      do_normalize=_UpperCamelCase							,      return_attention_mask=_UpperCamelCase							,      )
									__lowercase			       = WavaVecaProcessor(feature_extractor=_UpperCamelCase							,      tokenizer=_UpperCamelCase	)
									processor.save_pretrained(_UpperCamelCase	)

						__lowercase			       = WavaVecaForCTC(_UpperCamelCase	)
			else:
						__lowercase			       = WavaVecaForPreTraining(_UpperCamelCase	)

			if is_finetuned or is_seq_class:
						__lowercase       ,			__lowercase       ,			__lowercase			       = fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]							,      arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
			else:
						__lowercase			       = argparse.Namespace(task='''audio_pretraining'''	)
						__lowercase			       = fairseq.tasks.setup_task(_UpperCamelCase	)

						__lowercase       ,			__lowercase       ,			__lowercase			       = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,      task=_UpperCamelCase	)

			__lowercase			       = model[0].eval()

			recursively_load_weights(_UpperCamelCase							,      _UpperCamelCase							,      not is_finetuned	)

			hf_wavavec.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
	a			:			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'''
	)
	parser.add_argument(
	    '''--is_seq_class''',
	    action='''store_true''',
	    help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
	)
	a			:			Union[str, Any]												=					parser.parse_args()

	a			:			str												=					not args.not_finetuned and not args.is_seq_class
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.dict_path,
	    is_finetuned,
	    args.is_seq_class,
	)






import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_=7			,							snake_case_=3			,							snake_case_=1_8			,							snake_case_=3_0			,							snake_case_=4_0_0			,							snake_case_=True			,							snake_case_=None			,							snake_case_=True			,							snake_case_=False			,							snake_case_=True			,							snake_case_=True			,							snake_case_=[0.5, 0.5, 0.5]			,							snake_case_=[0.5, 0.5, 0.5]			,							) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = parent
								__lowercase			       = batch_size
								__lowercase			       = num_channels
								__lowercase			       = image_size
								__lowercase			       = min_resolution
								__lowercase			       = max_resolution
								__lowercase			       = do_resize
								__lowercase			       = size if size is not None else {'''height''': 1_8, '''width''': 2_0}
								__lowercase			       = do_thumbnail
								__lowercase			       = do_align_axis
								__lowercase			       = do_pad
								__lowercase			       = do_normalize
								__lowercase			       = image_mean
								__lowercase			       = image_std






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_thumbnail": self.do_thumbnail,
								    "do_align_long_axis": self.do_align_axis,
								    "do_pad": self.do_pad,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}




@require_torch
@require_vision
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					DonutImageProcessor if is_vision_available() else None






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = DonutImageProcessingTester(self    )






					@property
					def A   (					self    ) ->      str:







								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								self.assertTrue(hasattr(snake_case_			,							'''do_resize'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''size'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_thumbnail'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_align_long_axis'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_pad'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_normalize'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''image_mean'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''image_std'''    )    )






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict    )
								self.assertEqual(image_processor.size			,							{'''height''': 1_8, '''width''': 2_0}    )

								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict			,							size=4_2    )
								self.assertEqual(image_processor.size			,							{'''height''': 4_2, '''width''': 4_2}    )

								# Previous config had dimensions in (width, height) order
								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict			,							size=(4_2, 8_4)    )
								self.assertEqual(image_processor.size			,							{'''height''': 8_4, '''width''': 4_2}    )






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								pass






					@is_flaky()
					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random PIL images
								__lowercase			       = prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case_    )
								for image in image_inputs:
											self.assertIsInstance(snake_case_			,							Image.Image    )

								# Test not batched input
								__lowercase			       = 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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = image_processing(snake_case_			,							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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)






					@is_flaky()
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random numpy tensors
								__lowercase			       = prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case_			,							numpify=snake_case_    )
								for image in image_inputs:
											self.assertIsInstance(snake_case_			,							np.ndarray    )

								# Test not batched input
								__lowercase			       = 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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = image_processing(snake_case_			,							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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)






					@is_flaky()
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random PyTorch tensors
								__lowercase			       = prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case_			,							torchify=snake_case_    )
								for image in image_inputs:
											self.assertIsInstance(snake_case_			,							torch.Tensor    )

								# Test not batched input
								__lowercase			       = 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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = image_processing(snake_case_			,							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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)


