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

SCREAMING_SNAKE_CASE__		:       Optional[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",
}
SCREAMING_SNAKE_CASE__		:       Tuple       						=       [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]







def      _a					(   lowercase__	:      Any			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					List[str]      =		{}
     with open(lowercase__  ,	'r'			) as file:
          for line_number, line in enumerate(lowercase__			):
               SCREAMING_SNAKE_CASE__					:					Any      =		line.strip()
               if line:
                    SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		line.split()
                    SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		line_number
                    SCREAMING_SNAKE_CASE__					:					Optional[int]      =		words[0]
                    SCREAMING_SNAKE_CASE__					:					str      =		value
     return result







def      _a					(   lowercase__	:      Dict  ,	lowercase__	:      Tuple  ,	lowercase__	:      str  ,	lowercase__	:      Union[str, Any]  ,	lowercase__	:      int			):







     '''simple docstring'''


     for attribute in key.split('.'			):
          SCREAMING_SNAKE_CASE__					:					Tuple      =		getattr(lowercase__  ,	lowercase__			)

     SCREAMING_SNAKE_CASE__					:					List[str]      =		None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(lowercase__			):
               SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		PARAM_MAPPING[full_name.split('.'			)[-1]]
               SCREAMING_SNAKE_CASE__					:					Tuple      =		'param'

     if weight_type is not None and weight_type != "param":
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		getattr(lowercase__  ,	lowercase__			).shape
     elif weight_type is not None and weight_type == "param":
          SCREAMING_SNAKE_CASE__					:					str      =		hf_pointer
          for attribute in hf_param_name.split('.'			):
               SCREAMING_SNAKE_CASE__					:					Tuple      =		getattr(lowercase__  ,	lowercase__			)
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		shape_pointer.shape

          # let's reduce dimension
          SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		value[0]
     else:
          SCREAMING_SNAKE_CASE__					:					Any      =		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":
          SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		value
     elif weight_type == "weight_g":
          SCREAMING_SNAKE_CASE__					:					Dict      =		value
     elif weight_type == "weight_v":
          SCREAMING_SNAKE_CASE__					:					Any      =		value
     elif weight_type == "bias":
          SCREAMING_SNAKE_CASE__					:					int      =		value
     elif weight_type == "param":
          for attribute in hf_param_name.split('.'			):
               SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		getattr(lowercase__  ,	lowercase__			)
          SCREAMING_SNAKE_CASE__					:					int      =		value
     else:
          SCREAMING_SNAKE_CASE__					:					List[str]      =		value

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







def      _a					(   lowercase__	:      Union[str, Any]  ,	lowercase__	:      Optional[int]  ,	lowercase__	:      Any  ,	lowercase__	:      Any  ,	lowercase__	:      List[Any]			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Dict      =		None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(lowercase__			):
               SCREAMING_SNAKE_CASE__					:					List[str]      =		PARAM_MAPPING[full_name.split('.'			)[-1]]
               SCREAMING_SNAKE_CASE__					:					List[str]      =		'param'

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

     SCREAMING_SNAKE_CASE__					:					int      =		value if 'lm_head' in full_key else value[0]


SCREAMING_SNAKE_CASE__		:       str       						=       {
    "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      _a					(   lowercase__	:      Dict  ,	lowercase__	:      str  ,	lowercase__	:      Tuple=None  ,	lowercase__	:      Union[str, Any]=None			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		False
     for key, mapped_key in MAPPING.items():
          SCREAMING_SNAKE_CASE__					:					List[str]      =		'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]:
               SCREAMING_SNAKE_CASE__					:					Dict      =		True
               if "*" in mapped_key:
                    SCREAMING_SNAKE_CASE__					:					List[Any]      =		name.split(lowercase__			)[0].split('.'			)[-2]
                    SCREAMING_SNAKE_CASE__					:					List[str]      =		mapped_key.replace('*'  ,	lowercase__			)
               if "weight_g" in name:
                    SCREAMING_SNAKE_CASE__					:					List[str]      =		'weight_g'
               elif "weight_v" in name:
                    SCREAMING_SNAKE_CASE__					:					Any      =		'weight_v'
               elif "bias" in name:
                    SCREAMING_SNAKE_CASE__					:					Dict      =		'bias'
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    SCREAMING_SNAKE_CASE__					:					int      =		'weight'
               else:
                    SCREAMING_SNAKE_CASE__					:					Optional[int]      =		None
               if hf_dict is not None:
                    rename_dict(lowercase__  ,	lowercase__  ,	lowercase__  ,	lowercase__  ,	lowercase__			)
               else:
                    set_recursively(lowercase__  ,	lowercase__  ,	lowercase__  ,	lowercase__  ,	lowercase__			)
               return is_used
     return is_used







def      _a					(   lowercase__	:      str  ,	lowercase__	:      Optional[int]  ,	lowercase__	:      List[Any]			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					int      =		[]
     SCREAMING_SNAKE_CASE__					:					List[str]      =		fairseq_model.state_dict()

     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          SCREAMING_SNAKE_CASE__					:					str      =		False
          if "conv_layers" in name:
               load_conv_layer(
                   lowercase__  ,	lowercase__  ,	lowercase__  ,	lowercase__  ,	hf_model.config.feat_extract_norm == 'group'  ,	)
               SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		True
          else:
               SCREAMING_SNAKE_CASE__					:					str      =		load_wavaveca_layer(lowercase__  ,	lowercase__  ,	lowercase__			)
          if not is_used:
               unused_weights.append(lowercase__			)

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







def      _a					(   lowercase__	:      Dict  ,	lowercase__	:      Any  ,	lowercase__	:      Optional[int]  ,	lowercase__	:      Dict  ,	lowercase__	:      int			):







     '''simple docstring'''


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







@torch.no_grad()
def      _a					(   lowercase__	:      Tuple  ,	lowercase__	:      Union[str, Any]  ,	lowercase__	:      Optional[int]=None  ,	lowercase__	:      Optional[Any]=None  ,	lowercase__	:      Union[str, Any]=True  ,	lowercase__	:      Dict=False			):







     '''simple docstring'''


     if config_path is not None:
          SCREAMING_SNAKE_CASE__					:					Dict      =		WavaVecaConfig.from_pretrained(lowercase__			)
     else:
          SCREAMING_SNAKE_CASE__					:					Dict      =		WavaVecaConfig()

     if is_seq_class:
          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		read_txt_into_dict(lowercase__			)
          SCREAMING_SNAKE_CASE__					:					str      =		idalabel
          SCREAMING_SNAKE_CASE__					:					str      =		WavaVecaForSequenceClassification(lowercase__			)
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		WavaVecaFeatureExtractor(
              feature_size=1  ,	sampling_rate=1_60_00  ,	padding_value=0  ,	do_normalize=lowercase__  ,	return_attention_mask=lowercase__  ,	)
          feature_extractor.save_pretrained(lowercase__			)

     elif is_finetuned:
          if dict_path:
               SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		Dictionary.load(lowercase__			)

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

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

          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		WavaVecaForCTC(lowercase__			)
     else:
          SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		WavaVecaForPreTraining(lowercase__			)

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

          SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,	task=lowercase__			)

     SCREAMING_SNAKE_CASE__					:					str      =		model[0].eval()

     recursively_load_weights(lowercase__  ,	lowercase__  ,	not is_finetuned			)

     hf_wavavec.save_pretrained(lowercase__			)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:       Dict       						=       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",
      )
      SCREAMING_SNAKE_CASE__		:       Optional[int]       						=       parser.parse_args()

      SCREAMING_SNAKE_CASE__		:       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 collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
						from ..tf_utils import stable_softmax

SCREAMING_SNAKE_CASE:     Optional[int]									= logging.get_logger(__name__)



@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						def __init__(			self	:   Any					,							**snake_case__	:   str  ):

								"""simple docstring"""

								super().__init__(**snake_case__  )

								requires_backends(self					,							'vision'  )
								self.check_model_type(
								    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
								    if self.framework == 'tf'
								    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING  )



						def __call__(			self	:   str					,							snake_case__	:   Union[str, List[str], "Image", List["Image"]]					,							**snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								return super().__call__(snake_case__					,							**snake_case__  )



						def 	__a					(			self	:   str					,							**snake_case__	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{}
								if "candidate_labels" in kwargs:
										SCREAMING_SNAKE_CASE_					      =							kwargs['candidate_labels']
								if "hypothesis_template" in kwargs:
										SCREAMING_SNAKE_CASE_					      =							kwargs['hypothesis_template']

								return preprocess_params, {}, {}



						def 	__a					(			self	:   List[str]					,							snake_case__	:   Optional[int]					,							snake_case__	:   Optional[Any]=None					,							snake_case__	:   List[str]="This is a photo of {}."  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							load_image(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							self.image_processor(images=[image]					,							return_tensors=self.framework  )
								SCREAMING_SNAKE_CASE_					      =							candidate_labels
								SCREAMING_SNAKE_CASE_					      =							[hypothesis_template.format(snake_case__  ) for x in candidate_labels]
								SCREAMING_SNAKE_CASE_					      =							self.tokenizer(snake_case__					,							return_tensors=self.framework					,							padding=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							[text_inputs]
								return inputs



						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							model_inputs.pop('candidate_labels'  )
								SCREAMING_SNAKE_CASE_					      =							model_inputs.pop('text_inputs'  )
								if isinstance(text_inputs[0]					,							snake_case__  ):
										SCREAMING_SNAKE_CASE_					      =							text_inputs[0]
								else:
										# Batching case.
										SCREAMING_SNAKE_CASE_					      =							text_inputs[0][0]

								SCREAMING_SNAKE_CASE_					      =							self.model(**snake_case__					,							**snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							{
								    'candidate_labels': candidate_labels,
								    'logits': outputs.logits_per_image,
								}
								return model_outputs






						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							model_outputs.pop('candidate_labels'  )
								SCREAMING_SNAKE_CASE_					      =							model_outputs['logits'][0]
								if self.framework == "pt":
										SCREAMING_SNAKE_CASE_					      =							logits.softmax(dim=-1  ).squeeze(-1  )
										SCREAMING_SNAKE_CASE_					      =							probs.tolist()
										if not isinstance(snake_case__					,							snake_case__  ):
												SCREAMING_SNAKE_CASE_					      =							[scores]
								elif self.framework == "tf":
										SCREAMING_SNAKE_CASE_					      =							stable_softmax(snake_case__					,							axis=-1  )
										SCREAMING_SNAKE_CASE_					      =							probs.numpy().tolist()
								else:
										raise ValueError(f'''Unsupported framework: {self.framework}'''  )

								SCREAMING_SNAKE_CASE_					      =							[
								    {'score': score, 'label': candidate_label}
								    for score, candidate_label in sorted(zip(snake_case__					,							snake_case__  )					,							key=lambda snake_case__  : -x[0]  )
								]
								return result