



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()
_A       =					logging.get_logger(__name__)

_A       =					{
    "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",
}
_A       =					[
    "lm_head",
]






def        lowerCamelCase__    (    __lowerCAmelCase  :  Any   ,							__lowerCAmelCase  :  List[str]   ,							__lowerCAmelCase  :  Any   ,							__lowerCAmelCase  :  Union[str, Any]   ,							__lowerCAmelCase  :  Dict    ):




			"""simple docstring"""


			for attribute in key.split("."    ):
						lowerCAmelCase_      =     getattr(__lowerCAmelCase   ,							__lowerCAmelCase    )

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

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






def        lowerCamelCase__    (    __lowerCAmelCase  :  Tuple   ,							__lowerCAmelCase  :  List[str]   ,							__lowerCAmelCase  :  List[Any]    ):




			"""simple docstring"""


			lowerCAmelCase_      =     []
			lowerCAmelCase_      =     fairseq_model.state_dict()

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

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

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

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






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  Any    ):




			"""simple docstring"""


			lowerCAmelCase_      =     path.split("."    )
			return reduce(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase    )






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  List[str]   ,							__lowerCAmelCase  :  Any   ,							__lowerCAmelCase  :  Optional[Any]    ):




			"""simple docstring"""


			lowerCAmelCase_      =     access_by_string(__lowerCAmelCase   ,							__lowerCAmelCase    )
			lowerCAmelCase_      =     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."""    )
			lowerCAmelCase_      =     fsq_value
			logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""    )






def        lowerCamelCase__    (    __lowerCAmelCase  :  Optional[Any]   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  Optional[int]   ,							__lowerCAmelCase  :  str    ):




			"""simple docstring"""


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

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

			set_weights(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""    )






def        lowerCamelCase__    (    __lowerCAmelCase  :  List[Any]   ,							__lowerCAmelCase  :  Dict   ,							__lowerCAmelCase  :  Tuple   ,							__lowerCAmelCase  :  Tuple    ):




			"""simple docstring"""


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

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

			set_weights(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							F"""layers.{layer_id}.{layer_type}.{weight_type}"""    )






@torch.no_grad()
def        lowerCamelCase__    (    __lowerCAmelCase  :  Dict   ,							__lowerCAmelCase  :  str   ,							__lowerCAmelCase  :  List[str]=None   ,							__lowerCAmelCase  :  int=None   ,							__lowerCAmelCase  :  str=True    ):




			"""simple docstring"""


			if config_path is not None:
						lowerCAmelCase_      =     DataaVecAudioConfig.from_pretrained(__lowerCAmelCase    )
			else:
						lowerCAmelCase_      =     DataaVecAudioConfig()

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

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

			def load_dataavec(__lowerCAmelCase  :  List[Any]    ):
						lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_      =     fairseq.checkpoint_utils.load_model_ensemble_and_task([path]    )
						return model[0].eval()

			lowerCAmelCase_      =     load_dataavec(__lowerCAmelCase    )

			recursively_load_weights(__lowerCAmelCase   ,							__lowerCAmelCase   ,							not is_finetuned    )

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

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

			lowerCAmelCase_      =     processor(__lowerCAmelCase   ,							return_tensors="pt"   ,							padding=__lowerCAmelCase    )

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

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

						lowerCAmelCase_      =     torch.argmax(__lowerCAmelCase   ,							dim=-1    )
						lowerCAmelCase_      =     processor.batch_decode(__lowerCAmelCase    )

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

			print(our_output.shape   ,							their_output.shape    )
			lowerCAmelCase_      =     torch.max(torch.abs(our_output - their_output    )    ).item()
			print(F"""max_absolute_diff = {max_absolute_diff}"""    )  # ~ 1e-7
			lowerCAmelCase_      =     torch.allclose(__lowerCAmelCase   ,							__lowerCAmelCase   ,							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(__lowerCAmelCase    )

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


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_wavaveca_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)





import math





class      _lowerCAmelCase :




				def __init__( self    ,    _UpperCamelCase					)    ->				Dict:
							lowerCAmelCase_      =     len(_UpperCamelCase					)
							lowerCAmelCase_      =     [0] * (
							    4 * self.N
							)  # approximate the overall size of segment tree with array N
							self.build(1    ,    0    ,    self.N - 1					)




				def 					__a						( self    ,    _UpperCamelCase					)    ->				List[str]:
							return idx * 2




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							return idx * 2 + 1




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				int:  # noqa: E741
							if l == r:
										lowerCAmelCase_      =     A[l]
							else:
										lowerCAmelCase_      =     (l + r) // 2
										self.build(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase					)
										self.build(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase					)
										lowerCAmelCase_      =     max(self.st[self.left(_UpperCamelCase					)]    ,    self.st[self.right(_UpperCamelCase					)]					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				int:
							return self.update_recursive(1    ,    0    ,    self.N - 1    ,    a - 1    ,    b - 1    ,    _UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				str:  # noqa: E741
							if r < a or l > b:
										return True
							if l == r:
										lowerCAmelCase_      =     val
										return True
							lowerCAmelCase_      =     (l + r) // 2
							self.update_recursive(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							self.update_recursive(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     max(self.st[self.left(_UpperCamelCase					)]    ,    self.st[self.right(_UpperCamelCase					)]					)
							return True




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Optional[Any]:
							return self.query_recursive(1    ,    0    ,    self.N - 1    ,    a - 1    ,    b - 1					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Any:  # noqa: E741
							if r < a or l > b:
										return -math.inf
							if l >= a and r <= b:
										return self.st[idx]
							lowerCAmelCase_      =     (l + r) // 2
							lowerCAmelCase_      =     self.query_recursive(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     self.query_recursive(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							return max(_UpperCamelCase    ,    _UpperCamelCase					)




				def 					__a						( self					)    ->				int:
							lowerCAmelCase_      =     []
							for i in range(1    ,    N + 1					):
										show_list += [self.query(_UpperCamelCase    ,    _UpperCamelCase					)]
							print(_UpperCamelCase					)


if __name__ == "__main__":
			_A       =					[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
			_A       =					15
			_A       =					SegmentTree(A)
			print(segt.query(4, 6))
			print(segt.query(7, 11))
			print(segt.query(7, 12))
			segt.update(1, 3, 1_11)
			print(segt.query(1, 15))
			segt.update(7, 8, 2_35)
			segt.show_data()



