
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:str										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:List[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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_						)		->     Optional[Any]:







				"""simple docstring"""



				for attribute in key.split("."						):
								__A             =			getattr(a_		,       a_						)

				if weight_type is not None:
								__A             =			getattr(a_		,       a_						).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 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     List[Any]:







				"""simple docstring"""



				__A             =			[]
				__A             =			fairseq_model.state_dict()

				__A             =			hf_model.hubert.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(
												    a_		,       a_		,       a_		,       a_		,       hf_model.config.feat_extract_norm == "group"		,       )
												__A             =			True
								else:
												for key, mapped_key in MAPPING.items():
																__A             =			"hubert." + 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] and not is_finetuned):
																				__A             =			True
																				if "*" in mapped_key:
																								__A             =			name.split(a_						)[0].split("."						)[-2]
																								__A             =			mapped_key.replace("*"		,       a_						)
																				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(a_		,       a_		,       a_		,       a_		,       a_						)
																continue
								if not is_used:
												unused_weights.append(a_						)

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


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_						)		->     int:







				"""simple docstring"""



				__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(a_						)


@torch.no_grad()
def 						UpperCAmelCase      (     a_		,       a_		,       a_=None		,       a_=None		,       a_=True						)		->     Union[str, Any]:







				"""simple docstring"""



				if config_path is not None:
								__A             =			HubertConfig.from_pretrained(a_						)
				else:
								__A             =			HubertConfig()

				if is_finetuned:
								if dict_path:
												__A             =			Dictionary.load(a_						)

												# 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.eos_index
												__A             =			len(target_dict.symbols						)
												__A             =			os.path.join(a_		,       "vocab.json"						)
												if not os.path.isdir(a_						):
																logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(a_						)						)
																return
												os.makedirs(a_		,       exist_ok=a_						)
												with open(a_		,       "w"		,       encoding="utf-8"						) as vocab_handle:
																json.dump(target_dict.indices		,       a_						)
												__A             =			WavaVecaCTCTokenizer(
												    a_		,       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=a_		,       )
												__A             =			True if config.feat_extract_norm == "layer" else False
												__A             =			WavaVecaFeatureExtractor(
												    feature_size=1		,       sampling_rate=1_6_0_0_0		,       padding_value=0		,       do_normalize=a_		,       return_attention_mask=a_		,       )
												__A             =			WavaVecaProcessor(feature_extractor=a_		,       tokenizer=a_						)
												processor.save_pretrained(a_						)

								__A             =			HubertForCTC(a_						)
				else:
								__A             =			HubertModel(a_						)

				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]						)

				__A             =			model[0].eval()

				recursively_load_weights(a_		,       a_		,       a_						)

				hf_wavavec.save_pretrained(a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[int]										=    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'
					)
					SCREAMING_SNAKE_CASE							:str										=    parser.parse_args()
					convert_hubert_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"facebook/bart-large-mnli"
	snake_case_    =					(
	    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
	    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
	    "It returns the most likely label in the list of provided `labels` for the input text."
	)
	snake_case_    =					"text_classifier"
	snake_case_    =					AutoTokenizer
	snake_case_    =					AutoModelForSequenceClassification

	snake_case_    =					["text", ["text"]]
	snake_case_    =					["text"]






	def     UpperCamelCase_  (	self :			str			):
					super().setup()
					__A             =			self.model.config
					__A             =			-1
					for idx, label in config.idalabel.items():
									if label.lower().startswith("entail"			):
													__A             =			int(A			)
					if self.entailment_id == -1:
									raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."			)






	def     UpperCamelCase_  (	self :			int							,A :			Optional[Any]							,A :			Dict			):
					__A             =			labels
					return self.pre_processor(
					    [text] * len(A			)							,[f'''This example is {label}''' for label in labels]							,return_tensors="pt"							,padding="max_length"							,)







	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Tuple			):
					__A             =			outputs.logits
					__A             =			torch.argmax(logits[:, 2]			).item()
					return self._labels[label_id]
