





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__						:			Union[str, Any]									=logging.get_logger(__name__)

UpperCAmelCase__						:			Optional[int]									={
    '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__						:			int									=[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]





def      _lowercase					(						_UpperCAmelCase				)						->  List[Any]:
				lowerCamelCase	 ={}
				with open(__lowerCAmelCase			,      """r"""				) as file:
								for line_number, line in enumerate(__lowerCAmelCase				):
												lowerCamelCase	 =line.strip()
												if line:
																lowerCamelCase	 =line.split()
																lowerCamelCase	 =line_number
																lowerCamelCase	 =words[0]
																lowerCamelCase	 =value
				return result





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Optional[Any]:
				for attribute in key.split("""."""				):
								lowerCamelCase	 =getattr(__lowerCAmelCase			,      __lowerCAmelCase				)

				lowerCamelCase	 =None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(__lowerCAmelCase				):
												lowerCamelCase	 =PARAM_MAPPING[full_name.split("""."""				)[-1]]
												lowerCamelCase	 ="""param"""

				if weight_type is not None and weight_type != "param":
								lowerCamelCase	 =getattr(__lowerCAmelCase			,      __lowerCAmelCase				).shape
				elif weight_type is not None and weight_type == "param":
								lowerCamelCase	 =hf_pointer
								for attribute in hf_param_name.split("""."""				):
												lowerCamelCase	 =getattr(__lowerCAmelCase			,      __lowerCAmelCase				)
								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(__lowerCAmelCase			,      __lowerCAmelCase				)
								lowerCamelCase	 =value
				else:
								lowerCamelCase	 =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				)						->  int:
				lowerCamelCase	 =None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(__lowerCAmelCase				):
												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__						:			int									={
    '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				)						->  Union[str, Any]:
				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(__lowerCAmelCase				)[0].split("""."""				)[-2]
																lowerCamelCase	 =mapped_key.replace("""*"""			,      __lowerCAmelCase				)
												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(__lowerCAmelCase			,      __lowerCAmelCase			,      __lowerCAmelCase			,      __lowerCAmelCase			,      __lowerCAmelCase				)
												else:
																set_recursively(__lowerCAmelCase			,      __lowerCAmelCase			,      __lowerCAmelCase			,      __lowerCAmelCase			,      __lowerCAmelCase				)
												return is_used
				return is_used





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Dict:
				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(
												    __lowerCAmelCase			,      __lowerCAmelCase			,      __lowerCAmelCase			,      __lowerCAmelCase			,      hf_model.config.feat_extract_norm == """group"""			,      )
												lowerCamelCase	 =True
								else:
												lowerCamelCase	 =load_wavaveca_layer(__lowerCAmelCase			,      __lowerCAmelCase			,      __lowerCAmelCase				)
								if not is_used:
												unused_weights.append(__lowerCAmelCase				)

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





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				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(__lowerCAmelCase				)





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=None			,      _UpperCAmelCase=None			,      _UpperCAmelCase=True			,      _UpperCAmelCase=False				)						->  Dict:
				if config_path is not None:
								lowerCamelCase	 =WavaVecaConfig.from_pretrained(__lowerCAmelCase				)
				else:
								lowerCamelCase	 =WavaVecaConfig()

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

				elif is_finetuned:
								if dict_path:
												lowerCamelCase	 =Dictionary.load(__lowerCAmelCase				)

												# 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(__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				)
												lowerCamelCase	 =target_dict.indices

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

								lowerCamelCase	 =WavaVecaForCTC(__lowerCAmelCase				)
				else:
								lowerCamelCase	 =WavaVecaForPreTraining(__lowerCAmelCase				)

				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(__lowerCAmelCase				)

								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,      task=__lowerCAmelCase				)

				lowerCamelCase	 =model[0].eval()

				recursively_load_weights(__lowerCAmelCase			,      __lowerCAmelCase			,      not is_finetuned				)

				hf_wavavec.save_pretrained(__lowerCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			List[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''',
							)
							UpperCAmelCase__						:			List[Any]									=parser.parse_args()

							UpperCAmelCase__						:			int									=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 ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
							from .pipeline_kandinsky import KandinskyPipeline
							from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
							from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
							from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
							from .text_encoder import MultilingualCLIP





