





from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
						import torch


_lowerCAmelCase 				=     logging.get_logger(__name__)

class    _UpperCAmelCase							(		a__     ):
						a						       =							["pixel_values"]
						def __init__(		self      ,       a__ = True      ,       a__ = None      ,       a__ = PILImageResampling.BILINEAR      ,       a__ = True      ,       a__ = None      ,       a__ = True      ,       a__ = 1 / 255      ,       a__ = True      ,       a__ = None      ,       a__ = None      ,       **a__      ,       ):
									super().__init__(**lowerCamelCase_       )
									A_     :				Tuple			      =	size if size is not None else {"""shortest_edge""": 256}
									A_     :				List[str]			      =	get_size_dict(lowerCamelCase_      ,       default_to_square=lowerCamelCase_       )
									A_     :				Dict			      =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
									A_     :				Optional[Any]			      =	get_size_dict(lowerCamelCase_      ,       param_name="""crop_size"""       )
									A_     :				Any			      =	do_resize
									A_     :				Optional[int]			      =	size
									A_     :				Tuple			      =	resample
									A_     :				int			      =	do_center_crop
									A_     :				Dict			      =	crop_size
									A_     :				str			      =	do_rescale
									A_     :				Union[str, Any]			      =	rescale_factor
									A_     :				int			      =	do_normalize
									A_     :				Optional[int]			      =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									A_     :				List[Any]			      =	image_std if image_std is not None else IMAGENET_STANDARD_STD
						def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__ = PILImageResampling.BICUBIC      ,       a__ = None      ,       **a__      ,       ):
									A_     :				List[Any]			      =	get_size_dict(lowerCamelCase_      ,       default_to_square=lowerCamelCase_       )
									if "shortest_edge" not in size:
												raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""       )
									A_     :				Tuple			      =	get_resize_output_image_size(lowerCamelCase_      ,       size=size["""shortest_edge"""]      ,       default_to_square=lowerCamelCase_       )
									return resize(lowerCamelCase_      ,       size=lowerCamelCase_      ,       resample=lowerCamelCase_      ,       data_format=lowerCamelCase_      ,       **lowerCamelCase_       )
						def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__ = None      ,       **a__      ,       ):
									A_     :				List[Any]			      =	get_size_dict(lowerCamelCase_       )
									if "height" not in size or "width" not in size:
												raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""       )
									return center_crop(lowerCamelCase_      ,       size=(size["""height"""], size["""width"""])      ,       data_format=lowerCamelCase_      ,       **lowerCamelCase_       )
						def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__ = None      ,       **a__       ):
									return rescale(lowerCamelCase_      ,       scale=lowerCamelCase_      ,       data_format=lowerCamelCase_      ,       **lowerCamelCase_       )
						def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__      ,       a__ = None      ,       **a__      ,       ):
									return normalize(lowerCamelCase_      ,       mean=lowerCamelCase_      ,       std=lowerCamelCase_      ,       data_format=lowerCamelCase_      ,       **lowerCamelCase_       )
						def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None      ,       a__ = None      ,       a__ = None      ,       a__ = None      ,       a__ = None      ,       a__ = None      ,       a__ = None      ,       a__ = None      ,       a__ = None      ,       a__ = None      ,       a__ = None      ,       a__ = ChannelDimension.FIRST      ,       **a__      ,       ):
									A_     :				Tuple			      =	do_resize if do_resize is not None else self.do_resize
									A_     :				List[Any]			      =	size if size is not None else self.size
									A_     :				Any			      =	get_size_dict(lowerCamelCase_      ,       default_to_square=lowerCamelCase_       )
									A_     :				Any			      =	resample if resample is not None else self.resample
									A_     :				List[str]			      =	do_center_crop if do_center_crop is not None else self.do_center_crop
									A_     :				Dict			      =	crop_size if crop_size is not None else self.crop_size
									A_     :				Any			      =	get_size_dict(lowerCamelCase_      ,       param_name="""crop_size"""       )
									A_     :				Optional[Any]			      =	do_rescale if do_rescale is not None else self.do_rescale
									A_     :				List[Any]			      =	rescale_factor if rescale_factor is not None else self.rescale_factor
									A_     :				Tuple			      =	do_normalize if do_normalize is not None else self.do_normalize
									A_     :				Optional[int]			      =	image_mean if image_mean is not None else self.image_mean
									A_     :				Dict			      =	image_std if image_std is not None else self.image_std

									A_     :				List[Any]			      =	make_list_of_images(lowerCamelCase_       )

									if not valid_images(lowerCamelCase_       ):
												raise ValueError(
												    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
												    """torch.Tensor, tf.Tensor or jax.ndarray."""       )

									if do_resize and size is None:
												raise ValueError("""Size must be specified if do_resize is True."""       )

									if do_center_crop and crop_size is None:
												raise ValueError("""Crop size must be specified if do_center_crop is True."""       )

									if do_rescale and rescale_factor is None:
												raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

									if do_normalize and (image_mean is None or image_std is None):
												raise ValueError("""Image mean and std must be specified if do_normalize is True."""       )

									# All transformations expect numpy arrays.
									A_     :				List[Any]			      =	[to_numpy_array(lowerCamelCase_       ) for image in images]

									if do_resize:
												A_     :				Union[str, Any]			      =	[self.resize(image=lowerCamelCase_      ,       size=lowerCamelCase_      ,       resample=lowerCamelCase_       ) for image in images]

									if do_center_crop:
												A_     :				int			      =	[self.center_crop(image=lowerCamelCase_      ,       size=lowerCamelCase_       ) for image in images]

									if do_rescale:
												A_     :				Optional[Any]			      =	[self.rescale(image=lowerCamelCase_      ,       scale=lowerCamelCase_       ) for image in images]

									if do_normalize:
												A_     :				Union[str, Any]			      =	[self.normalize(image=lowerCamelCase_      ,       mean=lowerCamelCase_      ,       std=lowerCamelCase_       ) for image in images]

									A_     :				int			      =	[to_channel_dimension_format(lowerCamelCase_      ,       lowerCamelCase_       ) for image in images]

									A_     :				Optional[Any]			      =	{"""pixel_values""": images}
									return BatchFeature(data=lowerCamelCase_      ,       tensor_type=lowerCamelCase_       )






						def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
									A_     :				Optional[int]			      =	outputs.logits

									# Resize logits and compute semantic segmentation maps
									if target_sizes is not None:
												if len(lowerCamelCase_       ) != len(lowerCamelCase_       ):
															raise ValueError(
															    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""       )

												if is_torch_tensor(lowerCamelCase_       ):
															A_     :				Tuple			      =	target_sizes.numpy()

												A_     :				Optional[Any]			      =	[]

												for idx in range(len(lowerCamelCase_       )       ):
															A_     :				List[str]			      =	torch.nn.functional.interpolate(
															    logits[idx].unsqueeze(dim=0       )      ,       size=target_sizes[idx]      ,       mode="""bilinear"""      ,       align_corners=lowerCamelCase_       )
															A_     :				Tuple			      =	resized_logits[0].argmax(dim=0       )
															semantic_segmentation.append(lowerCamelCase_       )
									else:
												A_     :				Union[str, Any]			      =	logits.argmax(dim=1       )
												A_     :				Any			      =	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

									return semantic_segmentation













import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowerCAmelCase 				=     logging.get_logger(__name__)

_lowerCAmelCase 				=     {
    """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""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
_lowerCAmelCase 				=     [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   for attribute in key.split("""."""     ):
      if is_finetuned:
         if attribute in ["quantizer", "project_q", "project_hid"]:
            # those layers are only relevant for pretraining and should be dropped
            return

         if attribute == "ctc_proj":
            # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
            A_     :				Tuple			      =	"""lm_head"""

      A_     :				int			      =	getattr(_lowerCAmelCase  ,_lowerCAmelCase     )

   if weight_type is not None:
      A_     :				List[str]			      =	getattr(_lowerCAmelCase  ,_lowerCAmelCase     ).shape
   else:
      A_     :				Optional[Any]			      =	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_     :				Union[str, Any]			      =	value
   elif weight_type == "weight_g":
      A_     :				Any			      =	value
   elif weight_type == "weight_v":
      A_     :				Tuple			      =	value
   elif weight_type == "bias":
      A_     :				List[Any]			      =	value
   else:
      A_     :				int			      =	value

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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Dict			      =	[]
   A_     :				List[Any]			      =	fairseq_model.state_dict()

   A_     :				Any			      =	hf_model.unispeech.feature_extractor

   for name, value in fairseq_dict.items():
      A_     :				Any			      =	False
      if "conv_layers" in name:
         load_conv_layer(
             _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,hf_model.config.feat_extract_norm == """group"""  ,)
         A_     :				int			      =	True
      else:
         for key, mapped_key in MAPPING.items():
            A_     :				Any			      =	"""unispeech.""" + 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]:
               A_     :				Dict			      =	True
               if "*" in mapped_key:
                  A_     :				Any			      =	name.split(_lowerCAmelCase     )[0].split("""."""     )[-2]
                  A_     :				Optional[int]			      =	mapped_key.replace("""*"""  ,_lowerCAmelCase     )
               if "weight_g" in name:
                  A_     :				Tuple			      =	"""weight_g"""
               elif "weight_v" in name:
                  A_     :				Optional[int]			      =	"""weight_v"""
               elif "bias" in name:
                  A_     :				List[Any]			      =	"""bias"""
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  A_     :				Optional[int]			      =	"""weight"""
               else:
                  A_     :				Dict			      =	None
               set_recursively(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )
            continue
      if not is_used:
         unused_weights.append(_lowerCAmelCase     )

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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	full_name.split("""conv_layers."""     )[-1]
   A_     :				Union[str, Any]			      =	name.split("""."""     )
   A_     :				List[str]			      =	int(items[0]     )
   A_     :				Optional[Any]			      =	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_     :				List[Any]			      =	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_     :				int			      =	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_     :				List[str]			      =	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_     :				Tuple			      =	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     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase=None  ,_lowerCAmelCase=None  ,_lowerCAmelCase=True     ):
   '''simple docstring'''


   if config_path is not None:
      A_     :				Optional[int]			      =	UniSpeechConfig.from_pretrained(_lowerCAmelCase     )
   else:
      A_     :				List[str]			      =	UniSpeechConfig()

   if is_finetuned:
      if dict_path:
         A_     :				str			      =	Dictionary.load_from_json(_lowerCAmelCase     )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         A_     :				List[Any]			      =	target_dict.pad_index
         A_     :				str			      =	target_dict.bos_index
         A_     :				Optional[Any]			      =	target_dict.eos_index
         A_     :				Optional[int]			      =	len(target_dict.symbols     )
         A_     :				str			      =	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     )
         A_     :				Optional[int]			      =	target_dict.indices

         # fairseq has the <pad> and <s> switched
         A_     :				Dict			      =	4_2
         A_     :				Optional[Any]			      =	4_3
         with open(_lowerCAmelCase  ,"""w"""  ,encoding="""utf-8"""     ) as vocab_handle:
            json.dump(_lowerCAmelCase  ,_lowerCAmelCase     )
         A_     :				Tuple			      =	WavaVecaPhonemeCTCTokenizer(
             _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  ,)
         A_     :				Any			      =	True if config.feat_extract_norm == """layer""" else False
         A_     :				List[str]			      =	WavaVecaFeatureExtractor(
             feature_size=1  ,sampling_rate=1_6_0_0_0  ,padding_value=0  ,do_normalize=_lowerCAmelCase  ,return_attention_mask=_lowerCAmelCase  ,)
         A_     :				Optional[int]			      =	WavaVecaProcessor(feature_extractor=_lowerCAmelCase  ,tokenizer=_lowerCAmelCase     )
         processor.save_pretrained(_lowerCAmelCase     )

      A_     :				int			      =	UniSpeechForCTC(_lowerCAmelCase     )
   else:
      A_     :				str			      =	UniSpeechForPreTraining(_lowerCAmelCase     )

   if is_finetuned:
      A_      ,				A_      ,				A_     :				Union[str, Any]			      =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]  ,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""     )[:-1]     ), """w2v_path""": checkpoint_path}     )
   else:
      A_      ,				A_      ,				A_     :				Any			      =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

   A_     :				List[str]			      =	model[0].eval()

   recursively_load_weights(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )

   hf_unispeech.save_pretrained(_lowerCAmelCase     )


if __name__ == "__main__":
      _lowerCAmelCase 				=     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"""
      )
      _lowerCAmelCase 				=     parser.parse_args()
      convert_unispeech_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )






