

import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


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

SCREAMING_SNAKE_CASE		:List[str]				  =    {
    """post_extract_proj""": """feature_projection.projection""",
    """models.0.layer_norm""": """feature_projection.layer_norm""",
    """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""",
}
SCREAMING_SNAKE_CASE		:Optional[Any]				  =    [
    """lm_head""",
]




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Dict:



    """simple docstring"""






    for attribute in key.split("." ):
        UpperCamelCase_      =	getattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )

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

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




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






    UpperCamelCase_      =	[]
    UpperCamelCase_      =	fairseq_model.state_dict()

    if not is_headless:
        UpperCamelCase_      =	hf_model.dataavec_audio.feature_extractor
        UpperCamelCase_      =	hf_model.dataavec_audio.encoder.pos_conv_embed

    else:
        UpperCamelCase_      =	hf_model.feature_extractor
        UpperCamelCase_      =	hf_model.encoder.pos_conv_embed

    for name, value in fairseq_dict.items():
        UpperCamelCase_      =	False
        if "conv_layers" in name:
            load_conv_layer(
                SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	)
            UpperCamelCase_      =	True
        elif "pos_conv" in name:
            load_pos_conv_layer(
                SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	)
            UpperCamelCase_      =	True
        else:
            for key, mapped_key in MAPPING.items():
                if not is_headless:
                    UpperCamelCase_      =	"data2vec_audio." + 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]:
                    UpperCamelCase_      =	True
                    if "*" in mapped_key:
                        UpperCamelCase_      =	name.split(SCREAMING_SNAKE_CASE_ )[0].split("." )[-2]
                        UpperCamelCase_      =	mapped_key.replace("*" ,	SCREAMING_SNAKE_CASE_ )
                    if "weight_g" in name:
                        UpperCamelCase_      =	"weight_g"
                    elif "weight_v" in name:
                        UpperCamelCase_      =	"weight_v"
                    elif "bias" in name:
                        UpperCamelCase_      =	"bias"
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        UpperCamelCase_      =	"weight"
                    else:
                        UpperCamelCase_      =	None
                    set_recursively(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )
                continue
        if not is_used:
            unused_weights.append(SCREAMING_SNAKE_CASE_ )

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




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Any:



    """simple docstring"""






    UpperCamelCase_      =	path.split("." )
    return reduce(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Optional[int]:



    """simple docstring"""






    UpperCamelCase_      =	access_by_string(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )
    UpperCamelCase_      =	hf_weight.data

    if fsq_value.shape != hf_value.shape:
        raise ValueError(f"{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found." )
    UpperCamelCase_      =	fsq_value
    logger.info(f"{full_name} was correctly initialized from {hf_weight_path}." )




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Dict:



    """simple docstring"""






    UpperCamelCase_      =	full_name.split("conv_layers." )[-1]
    UpperCamelCase_      =	name.split("." )
    UpperCamelCase_      =	int(items[0] )
    UpperCamelCase_      =	int(items[1] )

    UpperCamelCase_      =	name.split("." )[-1]
    if type_id == 0:
        UpperCamelCase_      =	"conv"
    elif type_id == 2:
        UpperCamelCase_      =	"layer_norm"
    else:
        unused_weights.append(SCREAMING_SNAKE_CASE_ )
        return

    set_weights(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	f"conv_layers.{layer_id}.{layer_type}.{weight_type}" )




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	List[str]:



    """simple docstring"""






    UpperCamelCase_      =	full_name.split("pos_conv." )[-1]
    UpperCamelCase_      =	name.split("." )
    UpperCamelCase_      =	int(items[0] )
    UpperCamelCase_      =	int(items[1] )

    UpperCamelCase_      =	name.split("." )[-1]
    if type_id != 0:
        unused_weights.append(SCREAMING_SNAKE_CASE_ )
        return
    else:
        UpperCamelCase_      =	"conv"

    set_weights(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	f"layers.{layer_id}.{layer_type}.{weight_type}" )




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



    """simple docstring"""






    if config_path is not None:
        UpperCamelCase_      =	DataaVecAudioConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
    else:
        UpperCamelCase_      =	DataaVecAudioConfig()

    if not is_finetuned:
        # Modify final_proj layer name
        UpperCamelCase_      =	DataaVecAudioModel(SCREAMING_SNAKE_CASE_ )
        UpperCamelCase_      =	os.path.dirname(SCREAMING_SNAKE_CASE_ )

        UpperCamelCase_      =	torch.load(SCREAMING_SNAKE_CASE_ )
        UpperCamelCase_      =	state_dict["model"].pop("final_proj.0.weight" )
        UpperCamelCase_      =	state_dict["model"].pop("final_proj.0.bias" )
        UpperCamelCase_      =	os.path.join(SCREAMING_SNAKE_CASE_ ,	"converted.pt" )
        torch.save(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )
    else:
        UpperCamelCase_      =	DataaVecAudioForCTC(SCREAMING_SNAKE_CASE_ )
        UpperCamelCase_      =	checkpoint_path

    def load_dataavec(SCREAMING_SNAKE_CASE_ ):
        UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	fairseq.checkpoint_utils.load_model_ensemble_and_task([path] )
        return model[0].eval()

    UpperCamelCase_      =	load_dataavec(SCREAMING_SNAKE_CASE_ )

    recursively_load_weights(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	not is_finetuned )

    UpperCamelCase_      =	WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60" )

    UpperCamelCase_      =	load_dataset("patrickvonplaten/librispeech_asr_dummy" ,	"clean" ,	split="validation" )
    UpperCamelCase_      =	[x["array"] for x in ds[:4]["audio"]]

    UpperCamelCase_      =	processor(SCREAMING_SNAKE_CASE_ ,	return_tensors="pt" ,	padding=SCREAMING_SNAKE_CASE_ )

    UpperCamelCase_      =	inputs.input_values
    UpperCamelCase_      =	inputs.attention_mask
    #    input_values = inputs.input_values[:, :-1]
    #    attention_mask = inputs.attention_mask[:, :-1]

    hf_wavavec.eval()
    model.eval()
    if is_finetuned:
        UpperCamelCase_      =	model(source=SCREAMING_SNAKE_CASE_ ,	padding_mask=(1 - attention_mask) ,	mask=SCREAMING_SNAKE_CASE_ ,	features_only=SCREAMING_SNAKE_CASE_ )[
            "encoder_out"
        ].transpose(0 ,	1 )
        UpperCamelCase_      =	hf_wavavec(SCREAMING_SNAKE_CASE_ ,	attention_mask=SCREAMING_SNAKE_CASE_ )["logits"]

        UpperCamelCase_      =	torch.argmax(SCREAMING_SNAKE_CASE_ ,	dim=-1 )
        UpperCamelCase_      =	processor.batch_decode(SCREAMING_SNAKE_CASE_ )

        print(f"Expected Output: {ds[:4]['text']}, Pred: {output_string}" )
    else:
        UpperCamelCase_      =	model(source=SCREAMING_SNAKE_CASE_ ,	padding_mask=(1 - attention_mask) ,	mask=SCREAMING_SNAKE_CASE_ ,	features_only=SCREAMING_SNAKE_CASE_ )[
            "layer_results"
        ][-1][0].transpose(0 ,	1 )
        UpperCamelCase_      =	hf_wavavec(SCREAMING_SNAKE_CASE_ ,	attention_mask=SCREAMING_SNAKE_CASE_ )["last_hidden_state"]

    print(our_output.shape ,	their_output.shape )
    UpperCamelCase_      =	torch.max(torch.abs(our_output - their_output ) ).item()
    print(f"max_absolute_diff = {max_absolute_diff}" )  # ~ 1e-7
    UpperCamelCase_      =	torch.allclose(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	atol=1E-3 )
    print("Do both models output the same tensors?" ,	"🔥" if success else "💩" )
    if not success:
        raise Exception("Something went wRoNg" )

    hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_ )

    if is_finetuned:
        processor.save_pretrained(SCREAMING_SNAKE_CASE_ )
    else:
        processor.feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:List[str]				  =    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		:Tuple				  =    parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )



import unittest

from transformers.image_processing_utils import get_size_dict






class 	__magic_name__				(       unittest.TestCase							):






     def   UpperCAmelCase_							(							self  )->    int:
         # Test a dict with the wrong keys raises an error
         UpperCamelCase_      =	{"wrong_key": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         UpperCamelCase_      =	{"height": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         UpperCamelCase_      =	{"width": 224, "shortest_edge": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         # Test a dict with the correct keys is returned as is
         UpperCamelCase_      =	{"height": 224, "width": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	_lowercase  )

         UpperCamelCase_      =	{"shortest_edge": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224}  )

         UpperCamelCase_      =	{"longest_edge": 224, "shortest_edge": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	{"longest_edge": 224, "shortest_edge": 224}  )

         # Test a single int value which  represents (size, size)
         UpperCamelCase_      =	get_size_dict(224  )
         self.assertEqual(_lowercase		,	{"height": 224, "width": 224}  )

         # Test a single int value which represents the shortest edge
         UpperCamelCase_      =	get_size_dict(224		,	default_to_square=_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224}  )

         # Test a tuple of ints which represents (height, width)
         UpperCamelCase_      =	get_size_dict((150, 200)  )
         self.assertEqual(_lowercase		,	{"height": 150, "width": 200}  )

         # Test a tuple of ints which represents (width, height)
         UpperCamelCase_      =	get_size_dict((150, 200)		,	height_width_order=_lowercase  )
         self.assertEqual(_lowercase		,	{"height": 200, "width": 150}  )

         # Test an int representing the shortest edge and max_size which represents the longest edge
         UpperCamelCase_      =	get_size_dict(224		,	max_size=256		,	default_to_square=_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224, "longest_edge": 256}  )

         # Test int with default_to_square=True and max_size fails
         with self.assertRaises(_lowercase  ):
             get_size_dict(224		,	max_size=256		,	default_to_square=_lowercase  )
