





"""simple docstring"""





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()
lowerCAmelCase__        =      logging.get_logger(__name__)

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


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""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    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""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    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						path.split("."     )
		return reduce(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     )


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""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    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""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    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""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    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE=None	,						_SCREAMING_SNAKE_CASE=None	,						_SCREAMING_SNAKE_CASE=True     ):




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













"""simple docstring"""





def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						sorted(_SCREAMING_SNAKE_CASE	,						key=lambda _SCREAMING_SNAKE_CASE     : value[2]	,						reverse=_SCREAMING_SNAKE_CASE     )

		# Create a list of size equal to the maximum deadline
		# and initialize it with -1
		UpperCamelCase =						max(_SCREAMING_SNAKE_CASE	,						key=lambda _SCREAMING_SNAKE_CASE     : value[1]     )[1]
		UpperCamelCase =						[-1] * max_deadline

		# Finding the maximum profit and the count of jobs
		UpperCamelCase =						0
		UpperCamelCase =						0
		for job in jobs:
				# Find a free time slot for this job
				# (Note that we start from the last possible slot)
				for i in range(job[1] - 1	,						-1	,						-1     ):
						if time_slots[i] == -1:
								UpperCamelCase =						job[0]
								count += 1
								max_profit += job[2]
								break
		return [count, max_profit]


if __name__ == "__main__":
						import doctest

						doctest.testmod()






