




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()
UpperCAmelCase_						=logging.get_logger(__name__)

UpperCAmelCase_						={
    """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""",
}
UpperCAmelCase_						=[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]



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



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case							):
							for attribute in key.split('''.'''							):
														lowerCAmelCase   =		getattr(_snake_case						, _snake_case							)

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

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

														# let's reduce dimension
														lowerCAmelCase   =		value[0]
							else:
														lowerCAmelCase   =		hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
														    F""" {value.shape} for {full_name}"""							)

							if weight_type == "weight":
														lowerCAmelCase   =		value
							elif weight_type == "weight_g":
														lowerCAmelCase   =		value
							elif weight_type == "weight_v":
														lowerCAmelCase   =		value
							elif weight_type == "bias":
														lowerCAmelCase   =		value
							elif weight_type == "param":
														for attribute in hf_param_name.split('''.'''							):
																					lowerCAmelCase   =		getattr(_snake_case						, _snake_case							)
														lowerCAmelCase   =		value
							else:
														lowerCAmelCase   =		value

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



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(_snake_case							):
																					lowerCAmelCase   =		PARAM_MAPPING[full_name.split('''.'''							)[-1]]
																					lowerCAmelCase   =		'''param'''

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

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


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



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		[]
							lowerCAmelCase   =		fairseq_model.state_dict()

							lowerCAmelCase   =		hf_model.wavaveca.feature_extractor

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

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



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		full_name.split('''conv_layers.'''							)[-1]
							lowerCAmelCase   =		name.split('''.'''							)
							lowerCAmelCase   =		int(items[0]							)
							lowerCAmelCase   =		int(items[1]							)

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""							)
																					lowerCAmelCase   =		value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""							)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""							)
																					lowerCAmelCase   =		value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""							)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""							)
																					lowerCAmelCase   =		value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""							)
																					lowerCAmelCase   =		value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
							else:
														unused_weights.append(_snake_case							)



@torch.no_grad()
def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case=None						, _snake_case=None						, _snake_case=True						, _snake_case=False							):
							if config_path is not None:
														lowerCAmelCase   =		WavaVecaConfig.from_pretrained(_snake_case							)
							else:
														lowerCAmelCase   =		WavaVecaConfig()

							if is_seq_class:
														lowerCAmelCase   =		read_txt_into_dict(_snake_case							)
														lowerCAmelCase   =		idalabel
														lowerCAmelCase   =		WavaVecaForSequenceClassification(_snake_case							)
														lowerCAmelCase   =		WavaVecaFeatureExtractor(
														    feature_size=1						, sampling_rate=16000						, padding_value=0						, do_normalize=_snake_case						, return_attention_mask=_snake_case						, )
														feature_extractor.save_pretrained(_snake_case							)

							elif is_finetuned:
														if dict_path:
																					lowerCAmelCase   =		Dictionary.load(_snake_case							)

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

																					# fairseq has the <pad> and <s> switched
																					lowerCAmelCase   =		0
																					lowerCAmelCase   =		1
																					with open(_snake_case						, '''w'''						, encoding='''utf-8'''							) as vocab_handle:
																												json.dump(_snake_case						, _snake_case							)
																					lowerCAmelCase   =		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						, )
																					lowerCAmelCase   =		True if config.feat_extract_norm == '''layer''' else False
																					lowerCAmelCase   =		WavaVecaFeatureExtractor(
																					    feature_size=1						, sampling_rate=16000						, padding_value=0						, do_normalize=_snake_case						, return_attention_mask=_snake_case						, )
																					lowerCAmelCase   =		WavaVecaProcessor(feature_extractor=_snake_case						, tokenizer=_snake_case							)
																					processor.save_pretrained(_snake_case							)

														lowerCAmelCase   =		WavaVecaForCTC(_snake_case							)
							else:
														lowerCAmelCase   =		WavaVecaForPreTraining(_snake_case							)

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

														lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						, task=_snake_case							)

							lowerCAmelCase   =		model[0].eval()

							recursively_load_weights(_snake_case						, _snake_case						, not is_finetuned							)

							hf_wavavec.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(
		    """--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""",
		)
		UpperCAmelCase_						=parser.parse_args()

		UpperCAmelCase_						=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,
		)









from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   ,     lowerCAmelCase   =		analyze_text(_snake_case							)
							lowerCAmelCase   =		list(''' ''' + ascii_lowercase							)
							# what is our total sum of probabilities.
							lowerCAmelCase   =		sum(single_char_strings.values()							)

							# one length string
							lowerCAmelCase   =		0
							# for each alpha we go in our dict and if it is in it we calculate entropy
							for ch in my_alphas:
														if ch in single_char_strings:
																					lowerCAmelCase   =		single_char_strings[ch]
																					lowerCAmelCase   =		my_str / all_sum
																					my_fir_sum += prob * math.loga(_snake_case							)  # entropy formula.

    # print entropy
							print(F"""{round(-1 * my_fir_sum							):.1f}"""							)

							# two len string
							lowerCAmelCase   =		sum(two_char_strings.values()							)
							lowerCAmelCase   =		0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					lowerCAmelCase   =		cha + cha
																					if sequence in two_char_strings:
																												lowerCAmelCase   =		two_char_strings[sequence]
																												lowerCAmelCase   =		int(_snake_case							) / all_sum
																												my_sec_sum += prob * math.loga(_snake_case							)

    # print second entropy
							print(F"""{round(-1 * my_sec_sum							):.1f}"""							)

							# print the difference between them
							print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}"""							)



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		Counter()  # type: ignore
							lowerCAmelCase   =		Counter()  # type: ignore
							single_char_strings[text[-1]] += 1

							# first case when we have space at start.
							two_char_strings[" " + text[0]] += 1
							for i in range(0						, len(_snake_case							) - 1							):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings



def       UpperCAmelCase       (     ):
							import doctest

							doctest.testmod()
							# text = (
							#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
							#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
							#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
							#     "beloved evident through no service elderly is. Blind there if every no so "
							#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
							#     "of message cottage windows do besides against uncivil.  Delightful "
							#     "unreserved impossible few estimating men favourable see entreaties. She "
							#     "propriety immediate was improving. He or entrance humoured likewise "
							#     "moderate. Much nor game son say feel. Fat make met can must form into "
							#     "gate. Me we offending prevailed discovery. "
							# )

							# calculate_prob(text)


if __name__ == "__main__":
		main()



