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()
UpperCamelCase    				=		logging.get_logger(__name__)

UpperCamelCase    				=		{
    '''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''',
}
UpperCamelCase    				=		[
    '''lm_head''',
]

def      lowercase_							(     _lowerCamelCase				:			List[str]						,      _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			Optional[Any]						,      _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			Tuple):


    for attribute in key.split("."):
        lowercase__					:  Any          =							getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE)

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

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

def      lowercase_							(     _lowerCamelCase				:			Union[str, Any]						,      _lowerCamelCase				:			str						,      _lowerCamelCase				:			Tuple):
    lowercase__					:  str          =							[]
    lowercase__					:  Optional[Any]          =							fairseq_model.state_dict()

    if not is_headless:
        lowercase__					:  Tuple          =							hf_model.dataavec_audio.feature_extractor
        lowercase__					:  Optional[Any]          =							hf_model.dataavec_audio.encoder.pos_conv_embed

    else:
        lowercase__					:  str          =							hf_model.feature_extractor
        lowercase__					:  Tuple          =							hf_model.encoder.pos_conv_embed

    for name, value in fairseq_dict.items():
        lowercase__					:  Optional[Any]          =							False
        if "conv_layers" in name:
            load_conv_layer(
                _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      )
            lowercase__					:  Optional[int]          =							True
        elif "pos_conv" in name:
            load_pos_conv_layer(
                _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      )
            lowercase__					:  Optional[Any]          =							True
        else:
            for key, mapped_key in MAPPING.items():
                if not is_headless:
                    lowercase__					:  Any          =							"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]:
                    lowercase__					:  Dict          =							True
                    if "*" in mapped_key:
                        lowercase__					:  int          =							name.split(_SCREAMING_SNAKE_CASE)[0].split(".")[-2]
                        lowercase__					:  Dict          =							mapped_key.replace("*"						,      _SCREAMING_SNAKE_CASE)
                    if "weight_g" in name:
                        lowercase__					:  Union[str, Any]          =							"weight_g"
                    elif "weight_v" in name:
                        lowercase__					:  Any          =							"weight_v"
                    elif "bias" in name:
                        lowercase__					:  int          =							"bias"
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        lowercase__					:  Dict          =							"weight"
                    else:
                        lowercase__					:  List[str]          =							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      lowercase_							(     _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			Tuple):
    lowercase__					:  Any          =							path.split(".")
    return reduce(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE)

def      lowercase_							(     _lowerCamelCase				:			List[Any]						,      _lowerCamelCase				:			Any						,      _lowerCamelCase				:			List[str]						,      _lowerCamelCase				:			Tuple):
    lowercase__					:  Dict          =							access_by_string(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE)
    lowercase__					:  Union[str, Any]          =							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.''')
    lowercase__					:  Dict          =							fsq_value
    logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.''')

def      lowercase_							(     _lowerCamelCase				:			Union[str, Any]						,      _lowerCamelCase				:			str						,      _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			List[Any]):
    lowercase__					:  List[Any]          =							full_name.split("conv_layers.")[-1]
    lowercase__					:  Union[str, Any]          =							name.split(".")
    lowercase__					:  str          =							int(items[0])
    lowercase__					:  str          =							int(items[1])

    lowercase__					:  Union[str, Any]          =							name.split(".")[-1]
    if type_id == 0:
        lowercase__					:  Dict          =							"conv"
    elif type_id == 2:
        lowercase__					:  Dict          =							"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      lowercase_							(     _lowerCamelCase				:			Any						,      _lowerCamelCase				:			Optional[int]						,      _lowerCamelCase				:			Any						,      _lowerCamelCase				:			Optional[int]):
    lowercase__					:  List[Any]          =							full_name.split("pos_conv.")[-1]
    lowercase__					:  Dict          =							name.split(".")
    lowercase__					:  str          =							int(items[0])
    lowercase__					:  Union[str, Any]          =							int(items[1])

    lowercase__					:  int          =							name.split(".")[-1]
    if type_id != 0:
        unused_weights.append(_SCREAMING_SNAKE_CASE)
        return
    else:
        lowercase__					:  List[str]          =							"conv"

    set_weights(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      f'''layers.{layer_id}.{layer_type}.{weight_type}''')

@torch.no_grad()
def      lowercase_							(     _lowerCamelCase				:			Any						,      _lowerCamelCase				:			Optional[int]						,      _lowerCamelCase				:			int=None						,      _lowerCamelCase				:			int=None						,      _lowerCamelCase				:			Optional[Any]=True):


    if config_path is not None:
        lowercase__					:  List[str]          =							DataaVecAudioConfig.from_pretrained(_SCREAMING_SNAKE_CASE)
    else:
        lowercase__					:  int          =							DataaVecAudioConfig()

    if not is_finetuned:
        # Modify final_proj layer name
        lowercase__					:  Optional[int]          =							DataaVecAudioModel(_SCREAMING_SNAKE_CASE)
        lowercase__					:  Union[str, Any]          =							os.path.dirname(_SCREAMING_SNAKE_CASE)

        lowercase__					:  Tuple          =							torch.load(_SCREAMING_SNAKE_CASE)
        lowercase__					:  Any          =							state_dict["model"].pop("final_proj.0.weight")
        lowercase__					:  List[Any]          =							state_dict["model"].pop("final_proj.0.bias")
        lowercase__					:  int          =							os.path.join(_SCREAMING_SNAKE_CASE						,      "converted.pt")
        torch.save(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE)
    else:
        lowercase__					:  List[Any]          =							DataaVecAudioForCTC(_SCREAMING_SNAKE_CASE)
        lowercase__					:  List[str]          =							checkpoint_path

    def load_dataavec(_lowerCamelCase				:			Any):
        lowercase__   ,  lowercase__   ,  lowercase__					:  Any          =							fairseq.checkpoint_utils.load_model_ensemble_and_task([path])
        return model[0].eval()

    lowercase__					:  Tuple          =							load_dataavec(_SCREAMING_SNAKE_CASE)

    recursively_load_weights(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      not is_finetuned)

    lowercase__					:  Dict          =							WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60")

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

    lowercase__					:  Optional[Any]          =							processor(_SCREAMING_SNAKE_CASE						,      return_tensors="pt"						,      padding=_SCREAMING_SNAKE_CASE)

    lowercase__					:  str          =							inputs.input_values
    lowercase__					:  List[str]          =							inputs.attention_mask
    #    input_values = inputs.input_values[:, :-1]
    #    attention_mask = inputs.attention_mask[:, :-1]

    hf_wavavec.eval()
    model.eval()
    if is_finetuned:
        lowercase__					:  List[str]          =							model(source=_SCREAMING_SNAKE_CASE						,      padding_mask=(1 - attention_mask)						,      mask=_SCREAMING_SNAKE_CASE						,      features_only=_SCREAMING_SNAKE_CASE)[
            "encoder_out"
        ].transpose(0						,      1)
        lowercase__					:  Tuple          =							hf_wavavec(_SCREAMING_SNAKE_CASE						,      attention_mask=_SCREAMING_SNAKE_CASE)["logits"]

        lowercase__					:  int          =							torch.argmax(_SCREAMING_SNAKE_CASE						,      dim=-1)
        lowercase__					:  Optional[Any]          =							processor.batch_decode(_SCREAMING_SNAKE_CASE)

        print(f'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}''')
    else:
        lowercase__					:  List[str]          =							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)
        lowercase__					:  Dict          =							hf_wavavec(_SCREAMING_SNAKE_CASE						,      attention_mask=_SCREAMING_SNAKE_CASE)["last_hidden_state"]

    print(our_output.shape						,      their_output.shape)
    lowercase__					:  List[str]          =							torch.max(torch.abs(our_output - their_output)).item()
    print(f'''max_absolute_diff = {max_absolute_diff}''')  # ~ 1e-7
    lowercase__					:  Optional[Any]          =							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__":
      UpperCamelCase    				=		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'''
      )
      UpperCamelCase    				=		parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )










"""simple docstring"""





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class 							_lowerCamelCase   :


						def __init__(self ,				__a ,				__a=sys.maxsize		)			-> Tuple:
								UpperCamelCase =						"bilinear"
								UpperCamelCase =						max_size
								UpperCamelCase =						short_edge_length




						def __call__(self ,				__a		)			-> Dict:
								UpperCamelCase =						[]
								for img in imgs:
										UpperCamelCase ,					UpperCamelCase =						img.shape[:2]
										# later: provide list and randomly choose index for resize
										UpperCamelCase =						np.random.randint(self.short_edge_length[0] ,				self.short_edge_length[1] + 1		)
										if size == 0:
												return img
										UpperCamelCase =						size * 1.0 / min(__a ,				__a		)
										if h < w:
												UpperCamelCase ,					UpperCamelCase =						size, scale * w
										else:
												UpperCamelCase ,					UpperCamelCase =						scale * h, size
										if max(__a ,				__a		) > self.max_size:
												UpperCamelCase =						self.max_size * 1.0 / max(__a ,				__a		)
												UpperCamelCase =						newh * scale
												UpperCamelCase =						neww * scale
										UpperCamelCase =						int(neww + 0.5		)
										UpperCamelCase =						int(newh + 0.5		)

										if img.dtype == np.uinta:
												UpperCamelCase =						Image.fromarray(__a		)
												UpperCamelCase =						pil_image.resize((neww, newh) ,				PILImageResampling.BILINEAR		)
												UpperCamelCase =						np.asarray(__a		)
										else:
												UpperCamelCase =						img.permute(2 ,				0 ,				1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
												UpperCamelCase =						nn.functional.interpolate(
												    __a ,				(newh, neww) ,				mode=self.interp_method ,				align_corners=__a		).squeeze(0		)
										img_augs.append(__a		)

								return img_augs





class 							_lowerCamelCase   :


						def __init__(self ,				__a		)			-> Any:
								UpperCamelCase =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,				cfg.INPUT.MAX_SIZE_TEST		)
								UpperCamelCase =						cfg.INPUT.FORMAT
								UpperCamelCase =						cfg.SIZE_DIVISIBILITY
								UpperCamelCase =						cfg.PAD_VALUE
								UpperCamelCase =						cfg.INPUT.MAX_SIZE_TEST
								UpperCamelCase =						cfg.MODEL.DEVICE
								UpperCamelCase =						torch.tensor(cfg.MODEL.PIXEL_STD		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		) ,				1 ,				1		)
								UpperCamelCase =						torch.tensor(cfg.MODEL.PIXEL_MEAN		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		) ,				1 ,				1		)
								UpperCamelCase =						lambda __a		: (x - self.pixel_mean) / self.pixel_std


						def      snake_case_ (self ,				__a		)			-> List[Any]:
								UpperCamelCase =						tuple(max(__a		) for s in zip(*[img.shape for img in images]		)		)
								UpperCamelCase =						[im.shape[-2:] for im in images]
								UpperCamelCase =						[
								    nn.functional.pad(
								        __a ,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,				value=self.pad_value ,				)
								    for size, im in zip(__a ,				__a		)
								]

								return torch.stack(__a		), torch.tensor(__a		)




						def __call__(self ,				__a ,				__a=False		)			-> Optional[int]:
								with torch.no_grad():
										if not isinstance(__a ,				__a		):
												UpperCamelCase =						[images]
										if single_image:
												assert len(__a		) == 1
										for i in range(len(__a		)		):
												if isinstance(images[i] ,				torch.Tensor		):
														images.insert(__a ,				images.pop(__a		).to(self.device		).float()		)
												elif not isinstance(images[i] ,				torch.Tensor		):
														images.insert(
														    __a ,				torch.as_tensor(img_tensorize(images.pop(__a		) ,				input_format=self.input_format		)		)
														    .to(self.device		)
														    .float() ,				)
            # resize smallest edge
										UpperCamelCase =						torch.tensor([im.shape[:2] for im in images]		)
										UpperCamelCase =						self.aug(__a		)
										# transpose images and convert to torch tensors
										# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
										# now normalize before pad to avoid useless arithmetic
										UpperCamelCase =						[self.normalizer(__a		) for x in images]
										# now pad them to do the following operations
										UpperCamelCase ,					UpperCamelCase =						self.pad(__a		)
										# Normalize

										if self.size_divisibility > 0:
												raise NotImplementedError()
										# pad
										UpperCamelCase =						torch.true_divide(__a ,				__a		)
										if single_image:
												return images[0], sizes[0], scales_yx[0]
										else:
												return images, sizes, scales_yx



def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes






def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		assert torch.isfinite(_SCREAMING_SNAKE_CASE     ).all(), "Box tensor contains infinite or NaN!"
		UpperCamelCase ,					UpperCamelCase =						box_size
		tensor[:, 0].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )
		tensor[:, 1].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )
		tensor[:, 2].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )
		tensor[:, 3].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )






