
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()
_A						      =				logging.get_logger(__name__)

_A						      =				{
    "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 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							str:
    """simple docstring"""

    for attribute in key.split("."	):
        snake_case				  =      getattr(A__				,	A__	)

    if weight_type is not None:
        snake_case				  =      getattr(A__				,	A__	).shape
    else:
        snake_case				  =      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":
        snake_case				  =      value
    elif weight_type == "weight_g":
        snake_case				  =      value
    elif weight_type == "weight_v":
        snake_case				  =      value
    elif weight_type == "bias":
        snake_case				  =      value
    else:
        snake_case				  =      value

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



def 		lowercase_    (     A__				,	A__				,	A__	)							->							List[str]:
    """simple docstring"""

    snake_case				  =      []
    snake_case				  =      fairseq_model.state_dict()

    snake_case				  =      hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case				  =      False
        if "conv_layers" in name:
            load_conv_layer(
                A__				,	A__				,	A__				,	A__				,	hf_model.config.feat_extract_norm == "group"				,	)
            snake_case				  =      True
        else:
            for key, mapped_key in MAPPING.items():
                snake_case				  =      "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):
                    snake_case				  =      True
                    if "*" in mapped_key:
                        snake_case				  =      name.split(A__	)[0].split("."	)[-2]
                        snake_case				  =      mapped_key.replace("*"				,	A__	)
                    if "weight_g" in name:
                        snake_case				  =      "weight_g"
                    elif "weight_v" in name:
                        snake_case				  =      "weight_v"
                    elif "weight" in name:
                        snake_case				  =      "weight"
                    elif "bias" in name:
                        snake_case				  =      "bias"
                    else:
                        snake_case				  =      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 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      full_name.split("conv_layers."	)[-1]
    snake_case				  =      name.split("."	)
    snake_case				  =      int(items[0]	)
    snake_case				  =      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.'
            )
            snake_case				  =      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.'
            )
            snake_case				  =      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."
            )
            snake_case				  =      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.'
            )
            snake_case				  =      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 		lowercase_    (     A__				,	A__				,	A__=None				,	A__=None				,	A__=True	)							->							Union[str, Any]:
    """simple docstring"""

    if config_path is not None:
        snake_case				  =      HubertConfig.from_pretrained(A__	)
    else:
        snake_case				  =      HubertConfig()

    if is_finetuned:
        if dict_path:
            snake_case				  =      Dictionary.load(A__	)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            snake_case				  =      target_dict.pad_index
            snake_case				  =      target_dict.bos_index
            snake_case				  =      target_dict.eos_index
            snake_case				  =      len(target_dict.symbols	)
            snake_case				  =      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__	)
            snake_case				  =      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__				,	)
            snake_case				  =      True if config.feat_extract_norm == "layer" else False
            snake_case				  =      WavaVecaFeatureExtractor(
                feature_size=1				,	sampling_rate=1_6000				,	padding_value=0				,	do_normalize=A__				,	return_attention_mask=A__				,	)
            snake_case				  =      WavaVecaProcessor(feature_extractor=A__				,	tokenizer=A__	)
            processor.save_pretrained(A__	)

        snake_case				  =      HubertForCTC(A__	)
    else:
        snake_case				  =      HubertModel(A__	)

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

    snake_case				  =      model[0].eval()

    recursively_load_weights(A__				,	A__				,	A__	)

    hf_wavavec.save_pretrained(A__	)


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



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import MobileNetVaImageProcessor







class   lowerCamelCase	(						unittest.TestCase						):





     def __init__(self   :					Optional[int]       ,					_A   :					Dict       ,					_A   :					Optional[int]=7       ,					_A   :					Union[str, Any]=3       ,					_A   :					Tuple=1_8       ,					_A   :					str=3_0       ,					_A   :					Union[str, Any]=4_0_0       ,					_A   :					Optional[Any]=True       ,					_A   :					Union[str, Any]=None       ,					_A   :					str=True       ,					_A   :					Optional[int]=None       ,					)		->		int:
         snake_case				  =      size if size is not None else {"shortest_edge": 2_0}
         snake_case				  =      crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
         snake_case				  =      parent
         snake_case				  =      batch_size
         snake_case				  =      num_channels
         snake_case				  =      image_size
         snake_case				  =      min_resolution
         snake_case				  =      max_resolution
         snake_case				  =      do_resize
         snake_case				  =      size
         snake_case				  =      do_center_crop
         snake_case				  =      crop_size





     def 	UpperCAmelCase(self   :					str     )		->		int:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }







@require_torch
@require_vision
class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Optional[int]     =	MobileNetVaImageProcessor if is_vision_available() else None





     def 	UpperCAmelCase(self   :					Dict     )		->		List[Any]:
         snake_case				  =      MobileNetVaImageProcessingTester(self     )





     @property
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()





     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(_A       ,					"do_resize"     )     )
         self.assertTrue(hasattr(_A       ,					"size"     )     )
         self.assertTrue(hasattr(_A       ,					"do_center_crop"     )     )
         self.assertTrue(hasattr(_A       ,					"crop_size"     )     )





     def 	UpperCAmelCase(self   :					int     )		->		List[Any]:
         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict     )
         self.assertEqual(image_processor.size       ,					{"shortest_edge": 2_0}     )
         self.assertEqual(image_processor.crop_size       ,					{"height": 1_8, "width": 1_8}     )

         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict       ,					size=4_2       ,					crop_size=8_4     )
         self.assertEqual(image_processor.size       ,					{"shortest_edge": 4_2}     )
         self.assertEqual(image_processor.crop_size       ,					{"height": 8_4, "width": 8_4}     )





     def 	UpperCAmelCase(self   :					Any     )		->		Tuple:
         pass





     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					Image.Image     )

         # Test not batched input
         snake_case				  =      image_processing(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processing(_A       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)





     def 	UpperCAmelCase(self   :					Tuple     )		->		List[Any]:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A       ,					numpify=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					np.ndarray     )

         # Test not batched input
         snake_case				  =      image_processing(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processing(_A       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)





     def 	UpperCAmelCase(self   :					List[str]     )		->		List[str]:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A       ,					torchify=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					torch.Tensor     )

         # Test not batched input
         snake_case				  =      image_processing(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processing(_A       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

