
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


__a							  =						logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		42
	_A  =		None


def    UpperCamelCase_ (	a_  ,				a_=0.999  ,				a_="cosine"  ,				)     ->List[Any]:
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(a_		):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(a_		):
						return math.exp(t * -12.0		)

		else:
				raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''		)

		A				=[]
		for i in range(a_		):
				A				=i / num_diffusion_timesteps
				A				=(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(a_		) / alpha_bar_fn(a_		)  ,				a_		)		)
		return torch.tensor(a_  ,				dtype=torch.floataa		)






class        UpperCamelCase__(    lowerCAmelCase__						,						lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		[e.name for e in KarrasDiffusionSchedulers]
	_A  =		1







	@register_to_config
	def __init__(      self				:			Any							,			snake_case__				:			int = 10_00							,			snake_case__				:			float = 0.0_001							,			snake_case__				:			float = 0.02							,			snake_case__				:			str = "linear"							,			snake_case__				:			Optional[Union[np.ndarray, List[float]]] = None							,			snake_case__				:			str = "epsilon"							,			snake_case__				:			str = "linear"							,			snake_case__				:			Optional[bool] = False							,			snake_case__				:			str = "linspace"							,			snake_case__				:			int = 0							,			):
			"""simple docstring"""





			if trained_betas is not None:
					A				=torch.tensor(snake_case__							,			dtype=torch.floataa )
			elif beta_schedule == "linear":
					A				=torch.linspace(snake_case__							,			snake_case__							,			snake_case__							,			dtype=torch.floataa )
			elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					A				=(
					    torch.linspace(beta_start**0.5							,			beta_end**0.5							,			snake_case__							,			dtype=torch.floataa ) ** 2
					)
			elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					A				=betas_for_alpha_bar(snake_case__ )
			else:
					raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

			A				=1.0 - self.betas
			A				=torch.cumprod(self.alphas							,			dim=0 )

			A				=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
			A				=np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
			A				=torch.from_numpy(snake_case__ )

			# setable values
			A				=None
			A				=np.linspace(0							,			num_train_timesteps - 1							,			snake_case__							,			dtype=snake_case__ )[::-1].copy()
			A				=torch.from_numpy(snake_case__ )
			A				=False
			A				=use_karras_sigmas







	@property
	def        _a					(      self				:			Dict ):
			"""simple docstring"""





			if self.config.timestep_spacing in ["linspace", "trailing"]:
					return self.sigmas.max()

			return (self.sigmas.max() ** 2 + 1) ** 0.5







	def        _a					(      self				:			str							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			Union[float, torch.FloatTensor] ):
			"""simple docstring"""





			if isinstance(snake_case__							,			torch.Tensor ):
					A				=timestep.to(self.timesteps.device )
			A				=(self.timesteps == timestep).nonzero().item()
			A				=self.sigmas[step_index]

			A				=sample / ((sigma**2 + 1) ** 0.5)

			A				=True
			return sample







	def        _a					(      self				:			str							,			snake_case__				:			int							,			snake_case__				:			Union[str, torch.device] = None ):
			"""simple docstring"""





			A				=num_inference_steps

			# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
			if self.config.timestep_spacing == "linspace":
					A				=np.linspace(0							,			self.config.num_train_timesteps - 1							,			snake_case__							,			dtype=snake_case__ )[
					    ::-1
					].copy()
			elif self.config.timestep_spacing == "leading":
					A				=self.config.num_train_timesteps // self.num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# casting to int to avoid issues when num_inference_step is power of 3
					A				=(np.arange(0							,			snake_case__ ) * step_ratio).round()[::-1].copy().astype(snake_case__ )
					timesteps += self.config.steps_offset
			elif self.config.timestep_spacing == "trailing":
					A				=self.config.num_train_timesteps / self.num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# casting to int to avoid issues when num_inference_step is power of 3
					A				=(np.arange(self.config.num_train_timesteps							,			0							,			-step_ratio )).round().copy().astype(snake_case__ )
					timesteps -= 1
			else:
					raise ValueError(
					    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )

			A				=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
			A				=np.log(snake_case__ )

			if self.config.interpolation_type == "linear":
					A				=np.interp(snake_case__							,			np.arange(0							,			len(snake_case__ ) )							,			snake_case__ )
			elif self.config.interpolation_type == "log_linear":
					A				=torch.linspace(np.log(sigmas[-1] )							,			np.log(sigmas[0] )							,			num_inference_steps + 1 ).exp()
			else:
					raise ValueError(
					    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
					    " 'linear' or 'log_linear'" )

			if self.use_karras_sigmas:
					A				=self._convert_to_karras(in_sigmas=snake_case__							,			num_inference_steps=self.num_inference_steps )
					A				=np.array([self._sigma_to_t(snake_case__							,			snake_case__ ) for sigma in sigmas] )

			A				=np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
			A				=torch.from_numpy(snake_case__ ).to(device=snake_case__ )
			if str(snake_case__ ).startswith("mps" ):
					# mps does not support float64
					A				=torch.from_numpy(snake_case__ ).to(snake_case__							,			dtype=torch.floataa )
			else:
					A				=torch.from_numpy(snake_case__ ).to(device=snake_case__ )







	def        _a					(      self				:			Dict							,			snake_case__				:			Dict							,			snake_case__				:			Any ):
			"""simple docstring"""





			A				=np.log(snake_case__ )

			# get distribution
			A				=log_sigma - log_sigmas[:, np.newaxis]

			# get sigmas range
			A				=np.cumsum((dists >= 0)							,			axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
			A				=low_idx + 1

			A				=log_sigmas[low_idx]
			A				=log_sigmas[high_idx]

			# interpolate sigmas
			A				=(low - log_sigma) / (low - high)
			A				=np.clip(snake_case__							,			0							,			1 )

			# transform interpolation to time range
			A				=(1 - w) * low_idx + w * high_idx
			A				=t.reshape(sigma.shape )
			return t







	def        _a					(      self				:			List[str]							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			Dict ):
			"""simple docstring"""





			A				=in_sigmas[-1].item()
			A				=in_sigmas[0].item()

			A				=7.0  # 7.0 is the value used in the paper
			A				=np.linspace(0							,			1							,			snake_case__ )
			A				=sigma_min ** (1 / rho)
			A				=sigma_max ** (1 / rho)
			A				=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
			return sigmas







	def        _a					(      self				:			Any							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			Union[float, torch.FloatTensor]							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			float = 0.0							,			snake_case__				:			float = 0.0							,			snake_case__				:			float = float("inf" )							,			snake_case__				:			float = 1.0							,			snake_case__				:			Optional[torch.Generator] = None							,			snake_case__				:			bool = True							,			):
			"""simple docstring"""





			if (
			    isinstance(snake_case__							,			snake_case__ )
			    or isinstance(snake_case__							,			torch.IntTensor )
			    or isinstance(snake_case__							,			torch.LongTensor )
			):
					raise ValueError(
					    (
					        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
					        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
					        " one of the `scheduler.timesteps` as a timestep."
					    )							,			)

			if not self.is_scale_input_called:
					logger.warning(
					    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
					    "See `StableDiffusionPipeline` for a usage example." )

			if isinstance(snake_case__							,			torch.Tensor ):
					A				=timestep.to(self.timesteps.device )

			A				=(self.timesteps == timestep).nonzero().item()
			A				=self.sigmas[step_index]

			A				=min(s_churn / (len(self.sigmas ) - 1)							,			2**0.5 - 1 ) if s_tmin <= sigma <= s_tmax else 0.0

			A				=randn_tensor(
			    model_output.shape							,			dtype=model_output.dtype							,			device=model_output.device							,			generator=snake_case__ )

			A				=noise * s_noise
			A				=sigma * (gamma + 1)

			if gamma > 0:
					A				=sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

			# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
			# NOTE: "original_sample" should not be an expected prediction_type but is left in for
			# backwards compatibility
			if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
					A				=model_output
			elif self.config.prediction_type == "epsilon":
					A				=sample - sigma_hat * model_output
			elif self.config.prediction_type == "v_prediction":
					# * c_out + input * c_skip
					A				=model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
			else:
					raise ValueError(
					    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )

			# 2. Convert to an ODE derivative
			A				=(sample - pred_original_sample) / sigma_hat

			A				=self.sigmas[step_index + 1] - sigma_hat

			A				=sample + derivative * dt

			if not return_dict:
					return (prev_sample,)

			return EulerDiscreteSchedulerOutput(prev_sample=snake_case__							,			pred_original_sample=snake_case__ )







	def        _a					(      self				:			Optional[int]							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			torch.FloatTensor							,			):
			"""simple docstring"""





			A				=self.sigmas.to(device=original_samples.device							,			dtype=original_samples.dtype )
			if original_samples.device.type == "mps" and torch.is_floating_point(snake_case__ ):
					# mps does not support float64
					A				=self.timesteps.to(original_samples.device							,			dtype=torch.floataa )
					A				=timesteps.to(original_samples.device							,			dtype=torch.floataa )
			else:
					A				=self.timesteps.to(original_samples.device )
					A				=timesteps.to(original_samples.device )

			A				=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

			A				=sigmas[step_indices].flatten()
			while len(sigma.shape ) < len(original_samples.shape ):
					A				=sigma.unsqueeze(-1 )

			A				=original_samples + noise * sigma
			return noisy_samples







	def __len__(      self				:			Tuple ):
			"""simple docstring"""





			return self.config.num_train_timesteps






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    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_  ,				a_		)     ->Dict:
		for attribute in key.split("."		):
				A				=getattr(a_  ,				a_		)

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

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

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->Union[str, Any]:
		A				=[]
		A				=fairseq_model.state_dict()

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

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

		for name, value in fairseq_dict.items():
				A				=False
				if "conv_layers" in name:
						load_conv_layer(
						    a_  ,				a_  ,				a_  ,				a_  ,				)
						A				=True
				elif "pos_conv" in name:
						load_pos_conv_layer(
						    a_  ,				a_  ,				a_  ,				a_  ,				)
						A				=True
				else:
						for key, mapped_key in MAPPING.items():
								if not is_headless:
										A				="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]:
										A				=True
										if "*" in mapped_key:
												A				=name.split(a_		)[0].split("."		)[-2]
												A				=mapped_key.replace("*"  ,				a_		)
										if "weight_g" in name:
												A				="weight_g"
										elif "weight_v" in name:
												A				="weight_v"
										elif "bias" in name:
												A				="bias"
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												A				="weight"
										else:
												A				=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    UpperCamelCase_ (	a_  ,				a_		)     ->List[str]:
		A				=path.split("."		)
		return reduce(a_  ,				a_  ,				a_		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->str:
		A				=access_by_string(a_  ,				a_		)
		A				=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.'''		)
		A				=fsq_value
		logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->List[Any]:
		A				=full_name.split("conv_layers."		)[-1]
		A				=name.split("."		)
		A				=int(items[0]		)
		A				=int(items[1]		)

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

		set_weights(a_  ,				a_  ,				a_  ,				f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''		)

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->str:
		A				=full_name.split("pos_conv."		)[-1]
		A				=name.split("."		)
		A				=int(items[0]		)
		A				=int(items[1]		)

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

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

@torch.no_grad()
def    UpperCamelCase_ (	a_  ,				a_  ,				a_=None  ,				a_=None  ,				a_=True		)     ->Optional[int]:
		if config_path is not None:
				A				=DataaVecAudioConfig.from_pretrained(a_		)
		else:
				A				=DataaVecAudioConfig()

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

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

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

		A				=load_dataavec(a_		)

		recursively_load_weights(a_  ,				a_  ,				not is_finetuned		)

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

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

		A				=processor(a_  ,				return_tensors="pt"  ,				padding=a_		)

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

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

				A				=torch.argmax(a_  ,				dim=-1		)
				A				=processor.batch_decode(a_		)

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

		print(our_output.shape  ,				their_output.shape		)
		A				=torch.max(torch.abs(our_output - their_output		)		).item()
		print(f'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
		A				=torch.allclose(a_  ,				a_  ,				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(a_		)

		if is_finetuned:
				processor.save_pretrained(a_		)
		else:
				processor.feature_extractor.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_wavaveca_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)




