





"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class    _UpperCAmelCase  (      lowerCAmelCase__					):
					UpperCamelCase						=				4_2







class    _UpperCAmelCase  (      lowerCAmelCase__	,				lowerCAmelCase__					):


					@register_to_config
					def __init__(   self     :Optional[int]	,			__UpperCamelCase     :Optional[Union[int, Tuple[int, int]]] = None	,			__UpperCamelCase     :int = 3	,			__UpperCamelCase     :int = 3	,			__UpperCamelCase     :bool = False	,			__UpperCamelCase     :str = "positional"	,			__UpperCamelCase     :int = 0	,			__UpperCamelCase     :bool = True	,			__UpperCamelCase     :Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")	,			__UpperCamelCase     :Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")	,			__UpperCamelCase     :Tuple[int] = (2_24, 4_48, 6_72, 8_96)	,			__UpperCamelCase     :int = 2	,			__UpperCamelCase     :float = 1	,			__UpperCamelCase     :int = 1	,			__UpperCamelCase     :str = "conv"	,			__UpperCamelCase     :str = "conv"	,			__UpperCamelCase     :str = "silu"	,			__UpperCamelCase     :Optional[int] = 8	,			__UpperCamelCase     :int = 32	,			__UpperCamelCase     :float = 1e-5	,			__UpperCamelCase     :str = "default"	,			__UpperCamelCase     :bool = True	,			__UpperCamelCase     :Optional[str] = None	,			__UpperCamelCase     :Optional[int] = None	,			):
												super().__init__()

												A						=    sample_size
												A						=    block_out_channels[0] * 4

												# Check inputs
												if len(snake_case__						) != len(snake_case__						):
																			raise ValueError(
																			    f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."						)

												if len(snake_case__						) != len(snake_case__						):
																			raise ValueError(
																			    f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."						)

												# input
												A						=    nn.Convad(snake_case__	,			block_out_channels[0]	,			kernel_size=3	,			padding=(1, 1)						)

												# time
												if time_embedding_type == "fourier":
																			A						=    GaussianFourierProjection(embedding_size=block_out_channels[0]	,			scale=16						)
																			A						=    2 * block_out_channels[0]
												elif time_embedding_type == "positional":
																			A						=    Timesteps(block_out_channels[0]	,			snake_case__	,			snake_case__						)
																			A						=    block_out_channels[0]

												A						=    TimestepEmbedding(snake_case__	,			snake_case__						)

												# class embedding
												if class_embed_type is None and num_class_embeds is not None:
																			A						=    nn.Embedding(snake_case__	,			snake_case__						)
												elif class_embed_type == "timestep":
																			A						=    TimestepEmbedding(snake_case__	,			snake_case__						)
												elif class_embed_type == "identity":
																			A						=    nn.Identity(snake_case__	,			snake_case__						)
												else:
																			A						=    None

												A						=    nn.ModuleList([]						)
												A						=    None
												A						=    nn.ModuleList([]						)

												# down
												A						=    block_out_channels[0]
												for i, down_block_type in enumerate(snake_case__						):
																			A						=    output_channel
																			A						=    block_out_channels[i]
																			A						=    i == len(snake_case__						) - 1

																			A						=    get_down_block(
																			    snake_case__	,			num_layers=snake_case__	,			in_channels=snake_case__	,			out_channels=snake_case__	,			temb_channels=snake_case__	,			add_downsample=not is_final_block	,			resnet_eps=snake_case__	,			resnet_act_fn=snake_case__	,			resnet_groups=snake_case__	,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel	,			downsample_padding=snake_case__	,			resnet_time_scale_shift=snake_case__	,			downsample_type=snake_case__	,			)
																			self.down_blocks.append(snake_case__						)

												# mid
												A						=    UNetMidBlockaD(
												    in_channels=block_out_channels[-1]	,			temb_channels=snake_case__	,			resnet_eps=snake_case__	,			resnet_act_fn=snake_case__	,			output_scale_factor=snake_case__	,			resnet_time_scale_shift=snake_case__	,			attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]	,			resnet_groups=snake_case__	,			add_attention=snake_case__	,			)

												# up
												A						=    list(reversed(snake_case__						)						)
												A						=    reversed_block_out_channels[0]
												for i, up_block_type in enumerate(snake_case__						):
																			A						=    output_channel
																			A						=    reversed_block_out_channels[i]
																			A						=    reversed_block_out_channels[min(i + 1	,			len(snake_case__						) - 1						)]

																			A						=    i == len(snake_case__						) - 1

																			A						=    get_up_block(
																			    snake_case__	,			num_layers=layers_per_block + 1	,			in_channels=snake_case__	,			out_channels=snake_case__	,			prev_output_channel=snake_case__	,			temb_channels=snake_case__	,			add_upsample=not is_final_block	,			resnet_eps=snake_case__	,			resnet_act_fn=snake_case__	,			resnet_groups=snake_case__	,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel	,			resnet_time_scale_shift=snake_case__	,			upsample_type=snake_case__	,			)
																			self.up_blocks.append(snake_case__						)
																			A						=    output_channel

												# out
												A						=    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4	,			32						)
												A						=    nn.GroupNorm(num_channels=block_out_channels[0]	,			num_groups=snake_case__	,			eps=snake_case__						)
												A						=    nn.SiLU()
												A						=    nn.Convad(block_out_channels[0]	,			snake_case__	,			kernel_size=3	,			padding=1						)


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :torch.FloatTensor	,			__UpperCamelCase     :Union[torch.Tensor, float, int]	,			__UpperCamelCase     :Optional[torch.Tensor] = None	,			__UpperCamelCase     :bool = True	,			):





												if self.config.center_input_sample:
																			A						=    2 * sample - 1.0

												# 1. time
												A						=    timestep
												if not torch.is_tensor(snake_case__						):
																			A						=    torch.tensor([timesteps]	,			dtype=torch.long	,			device=sample.device						)
												elif torch.is_tensor(snake_case__						) and len(timesteps.shape						) == 0:
																			A						=    timesteps[None].to(sample.device						)

												# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
												A						=    timesteps * torch.ones(sample.shape[0]	,			dtype=timesteps.dtype	,			device=timesteps.device						)

												A						=    self.time_proj(snake_case__						)

												# timesteps does not contain any weights and will always return f32 tensors
												# but time_embedding might actually be running in fp16. so we need to cast here.
												# there might be better ways to encapsulate this.
												A						=    t_emb.to(dtype=self.dtype						)
												A						=    self.time_embedding(snake_case__						)

												if self.class_embedding is not None:
																			if class_labels is None:
																										raise ValueError("class_labels should be provided when doing class conditioning"						)

																			if self.config.class_embed_type == "timestep":
																										A						=    self.time_proj(snake_case__						)

																			A						=    self.class_embedding(snake_case__						).to(dtype=self.dtype						)
																			A						=    emb + class_emb

												# 2. pre-process
												A						=    sample
												A						=    self.conv_in(snake_case__						)

												# 3. down
												A						=    (sample,)
												for downsample_block in self.down_blocks:
																			if hasattr(snake_case__	,			"skip_conv"						):
																										A,      A,      A						=    downsample_block(
																										    hidden_states=snake_case__	,			temb=snake_case__	,			skip_sample=snake_case__						)
																			else:
																										A,      A						=    downsample_block(hidden_states=snake_case__	,			temb=snake_case__						)

																			down_block_res_samples += res_samples

												# 4. mid
												A						=    self.mid_block(snake_case__	,			snake_case__						)

												# 5. up
												A						=    None
												for upsample_block in self.up_blocks:
																			A						=    down_block_res_samples[-len(upsample_block.resnets						) :]
																			A						=    down_block_res_samples[: -len(upsample_block.resnets						)]

																			if hasattr(snake_case__	,			"skip_conv"						):
																										A,      A						=    upsample_block(snake_case__	,			snake_case__	,			snake_case__	,			snake_case__						)
																			else:
																										A						=    upsample_block(snake_case__	,			snake_case__	,			snake_case__						)

        # 6. post-process
												A						=    self.conv_norm_out(snake_case__						)
												A						=    self.conv_act(snake_case__						)
												A						=    self.conv_out(snake_case__						)

												if skip_sample is not None:
																			sample += skip_sample

												if self.config.time_embedding_type == "fourier":
																			A						=    timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]						)))						)
																			A						=    sample / timesteps

												if not return_dict:
																			return (sample,)

												return UNetaDOutput(sample=snake_case__						)




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
	)




