"""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()
SCREAMING_SNAKE_CASE						=    logging.get_logger(__name__)

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


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	for attribute in key.split('.'      ):
		_lowercase			:     Optional[Any]      					=				getattr(__UpperCAmelCase  ,__UpperCAmelCase      )

	if weight_type is not None:
		_lowercase			:     List[str]      					=				getattr(__UpperCAmelCase  ,__UpperCAmelCase      ).shape
	else:
		_lowercase			:     List[str]      					=				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			:     Dict      					=				value
	elif weight_type == "weight_v":
		_lowercase			:     Union[str, Any]      					=				value
	elif weight_type == "bias":
		_lowercase			:     str      					=				value
	else:
		_lowercase			:     str      					=				value

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


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     str      					=				[]
	_lowercase			:     Optional[Any]      					=				fairseq_model.state_dict()

	if not is_headless:
		_lowercase			:     List[Any]      					=				hf_model.dataavec_audio.feature_extractor
		_lowercase			:     List[str]      					=				hf_model.dataavec_audio.encoder.pos_conv_embed

	else:
		_lowercase			:     Any      					=				hf_model.feature_extractor
		_lowercase			:     Union[str, Any]      					=				hf_model.encoder.pos_conv_embed

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

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


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     List[str]      					=				path.split('.'      )
	return reduce(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     Union[str, Any]      					=				access_by_string(__UpperCAmelCase  ,__UpperCAmelCase      )
	_lowercase			:     Dict      					=				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			:     int      					=				fsq_value
	logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''      )


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     Tuple      					=				full_name.split('conv_layers.'      )[-1]
	_lowercase			:     Dict      					=				name.split('.'      )
	_lowercase			:     List[Any]      					=				int(items[0]      )
	_lowercase			:     List[str]      					=				int(items[1]      )

	_lowercase			:     Optional[int]      					=				name.split('.'      )[-1]
	if type_id == 0:
		_lowercase			:     Any      					=				'conv'
	elif type_id == 2:
		_lowercase			:     str      					=				'layer_norm'
	else:
		unused_weights.append(__UpperCAmelCase      )
		return

	set_weights(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,F'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''      )


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     int      					=				full_name.split('pos_conv.'      )[-1]
	_lowercase			:     Optional[Any]      					=				name.split('.'      )
	_lowercase			:     str      					=				int(items[0]      )
	_lowercase			:     Union[str, Any]      					=				int(items[1]      )

	_lowercase			:     Tuple      					=				name.split('.'      )[-1]
	if type_id != 0:
		unused_weights.append(__UpperCAmelCase      )
		return
	else:
		_lowercase			:     List[Any]      					=				'conv'

	set_weights(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,F'''layers.{layer_id}.{layer_type}.{weight_type}'''      )


@torch.no_grad()
def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=True      ):






	"""simple docstring"""





	if config_path is not None:
		_lowercase			:     str      					=				DataaVecAudioConfig.from_pretrained(__UpperCAmelCase      )
	else:
		_lowercase			:     List[str]      					=				DataaVecAudioConfig()

	if not is_finetuned:
		# Modify final_proj layer name
		_lowercase			:     List[Any]      					=				DataaVecAudioModel(__UpperCAmelCase      )
		_lowercase			:     List[str]      					=				os.path.dirname(__UpperCAmelCase      )

		_lowercase			:     List[Any]      					=				torch.load(__UpperCAmelCase      )
		_lowercase			:     Dict      					=				state_dict['model'].pop('final_proj.0.weight'      )
		_lowercase			:     Any      					=				state_dict['model'].pop('final_proj.0.bias'      )
		_lowercase			:     int      					=				os.path.join(__UpperCAmelCase  ,'converted.pt'      )
		torch.save(__UpperCAmelCase  ,__UpperCAmelCase      )
	else:
		_lowercase			:     Union[str, Any]      					=				DataaVecAudioForCTC(__UpperCAmelCase      )
		_lowercase			:     List[Any]      					=				checkpoint_path

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

	_lowercase			:     Optional[int]      					=				load_dataavec(__UpperCAmelCase      )

	recursively_load_weights(__UpperCAmelCase  ,__UpperCAmelCase  ,not is_finetuned      )

	_lowercase			:     List[Any]      					=				WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'      )

	_lowercase			:     Optional[Any]      					=				load_dataset('patrickvonplaten/librispeech_asr_dummy'  ,'clean'  ,split='validation'      )
	_lowercase			:     List[Any]      					=				[x['array'] for x in ds[:4]['audio']]

	_lowercase			:     Union[str, Any]      					=				processor(__UpperCAmelCase  ,return_tensors='pt'  ,padding=__UpperCAmelCase      )

	_lowercase			:     List[Any]      					=				inputs.input_values
	_lowercase			:     List[Any]      					=				inputs.attention_mask
	#    input_values = inputs.input_values[:, :-1]
	#    attention_mask = inputs.attention_mask[:, :-1]

	hf_wavavec.eval()
	model.eval()
	if is_finetuned:
		_lowercase			:     Optional[Any]      					=				model(source=__UpperCAmelCase  ,padding_mask=(1 - attention_mask)  ,mask=__UpperCAmelCase  ,features_only=__UpperCAmelCase      )[
		    'encoder_out'
		].transpose(0  ,1      )
		_lowercase			:     Union[str, Any]      					=				hf_wavavec(__UpperCAmelCase  ,attention_mask=__UpperCAmelCase      )['logits']

		_lowercase			:     Optional[int]      					=				torch.argmax(__UpperCAmelCase  ,dim=-1      )
		_lowercase			:     List[Any]      					=				processor.batch_decode(__UpperCAmelCase      )

		print(F'''Expected Output: {ds[:4]['text']}, Pred: {output_string}'''      )
	else:
		_lowercase			:     Tuple      					=				model(source=__UpperCAmelCase  ,padding_mask=(1 - attention_mask)  ,mask=__UpperCAmelCase  ,features_only=__UpperCAmelCase      )[
		    'layer_results'
		][-1][0].transpose(0  ,1      )
		_lowercase			:     Any      					=				hf_wavavec(__UpperCAmelCase  ,attention_mask=__UpperCAmelCase      )['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[int]      					=				torch.allclose(__UpperCAmelCase  ,__UpperCAmelCase  ,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(__UpperCAmelCase      )

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


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE						=    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'
						)
						SCREAMING_SNAKE_CASE						=    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"""







from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin



@dataclass
class 			_lowerCamelCase      (__lowerCamelCase  ):
				_snake_case  =	42



class 			_lowerCamelCase      (__lowerCamelCase    ,				__lowerCamelCase  ):







				@register_to_config
				def __init__(			self     :							Dict      ,       lowerCamelCase_     :							int = 1_6      ,       lowerCamelCase_     :							int = 8_8      ,       lowerCamelCase_     :							Optional[int] = None      ,       lowerCamelCase_     :							Optional[int] = None      ,       lowerCamelCase_     :							int = 1      ,       lowerCamelCase_     :							float = 0.0      ,       lowerCamelCase_     :							int = 3_2      ,       lowerCamelCase_     :							Optional[int] = None      ,       lowerCamelCase_     :							bool = False      ,       lowerCamelCase_     :							Optional[int] = None      ,       lowerCamelCase_     :							Optional[int] = None      ,       lowerCamelCase_     :							Optional[int] = None      ,       lowerCamelCase_     :							str = "geglu"      ,       lowerCamelCase_     :							Optional[int] = None      ,       lowerCamelCase_     :							bool = False      ,       lowerCamelCase_     :							bool = False      ,       lowerCamelCase_     :							bool = False      ,       lowerCamelCase_     :							str = "layer_norm"      ,       lowerCamelCase_     :							bool = True      ,       ):

					"""simple docstring"""






					super().__init__()
					_lowercase			:     Tuple      					=				use_linear_projection
					_lowercase			:     List[Any]      					=				num_attention_heads
					_lowercase			:     Optional[int]      					=				attention_head_dim
					_lowercase			:     Union[str, Any]      					=				num_attention_heads * attention_head_dim

					# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
					# Define whether input is continuous or discrete depending on configuration
					_lowercase			:     Dict      					=				(in_channels is not None) and (patch_size is None)
					_lowercase			:     Any      					=				num_vector_embeds is not None
					_lowercase			:     List[Any]      					=				in_channels is not None and patch_size is not None

					if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
						_lowercase			:     Tuple      					=				(
						    F'''The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'''
						    ' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'
						    ' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'
						    ' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'
						    ' would be very nice if you could open a Pull request for the `transformer/config.json` file'
						)
						deprecate('norm_type!=num_embeds_ada_norm'      ,       '1.0.0'      ,       lowerCamelCase_      ,       standard_warn=lowerCamelCase_    )
						_lowercase			:     Dict      					=				'ada_norm'

					if self.is_input_continuous and self.is_input_vectorized:
						raise ValueError(
						    F'''Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'''
						    ' sure that either `in_channels` or `num_vector_embeds` is None.'    )
					elif self.is_input_vectorized and self.is_input_patches:
						raise ValueError(
						    F'''Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'''
						    ' sure that either `num_vector_embeds` or `num_patches` is None.'    )
					elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
						raise ValueError(
						    F'''Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'''
						    F''' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.'''    )

					# 2. Define input layers
					if self.is_input_continuous:
						_lowercase			:     int      					=				in_channels

						_lowercase			:     List[Any]      					=				torch.nn.GroupNorm(num_groups=lowerCamelCase_      ,       num_channels=lowerCamelCase_      ,       eps=1E-6      ,       affine=lowerCamelCase_    )
						if use_linear_projection:
							_lowercase			:     Any      					=				nn.Linear(lowerCamelCase_      ,       lowerCamelCase_    )
						else:
							_lowercase			:     List[str]      					=				nn.Convad(lowerCamelCase_      ,       lowerCamelCase_      ,       kernel_size=1      ,       stride=1      ,       padding=0    )
					elif self.is_input_vectorized:
						assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
						assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

						_lowercase			:     int      					=				sample_size
						_lowercase			:     str      					=				sample_size
						_lowercase			:     int      					=				num_vector_embeds
						_lowercase			:     List[str]      					=				self.height * self.width

						_lowercase			:     Optional[int]      					=				ImagePositionalEmbeddings(
						    num_embed=lowerCamelCase_      ,       embed_dim=lowerCamelCase_      ,       height=self.height      ,       width=self.width    )
					elif self.is_input_patches:
						assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

						_lowercase			:     Optional[Any]      					=				sample_size
						_lowercase			:     Optional[int]      					=				sample_size

						_lowercase			:     str      					=				patch_size
						_lowercase			:     Union[str, Any]      					=				PatchEmbed(
						    height=lowerCamelCase_      ,       width=lowerCamelCase_      ,       patch_size=lowerCamelCase_      ,       in_channels=lowerCamelCase_      ,       embed_dim=lowerCamelCase_      ,       )

					# 3. Define transformers blocks
					_lowercase			:     Any      					=				nn.ModuleList(
					    [
					        BasicTransformerBlock(
					            lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_      ,       dropout=lowerCamelCase_      ,       cross_attention_dim=lowerCamelCase_      ,       activation_fn=lowerCamelCase_      ,       num_embeds_ada_norm=lowerCamelCase_      ,       attention_bias=lowerCamelCase_      ,       only_cross_attention=lowerCamelCase_      ,       upcast_attention=lowerCamelCase_      ,       norm_type=lowerCamelCase_      ,       norm_elementwise_affine=lowerCamelCase_      ,       )
					        for d in range(lowerCamelCase_    )
					    ]    )

					# 4. Define output layers
					_lowercase			:     Dict      					=				in_channels if out_channels is None else out_channels
					if self.is_input_continuous:
						# TODO: should use out_channels for continuous projections
						if use_linear_projection:
							_lowercase			:     str      					=				nn.Linear(lowerCamelCase_      ,       lowerCamelCase_    )
						else:
							_lowercase			:     List[Any]      					=				nn.Convad(lowerCamelCase_      ,       lowerCamelCase_      ,       kernel_size=1      ,       stride=1      ,       padding=0    )
					elif self.is_input_vectorized:
						_lowercase			:     Dict      					=				nn.LayerNorm(lowerCamelCase_    )
						_lowercase			:     Union[str, Any]      					=				nn.Linear(lowerCamelCase_      ,       self.num_vector_embeds - 1    )
					elif self.is_input_patches:
						_lowercase			:     Dict      					=				nn.LayerNorm(lowerCamelCase_      ,       elementwise_affine=lowerCamelCase_      ,       eps=1E-6    )
						_lowercase			:     Union[str, Any]      					=				nn.Linear(lowerCamelCase_      ,       2 * inner_dim    )
						_lowercase			:     List[Any]      					=				nn.Linear(lowerCamelCase_      ,       patch_size * patch_size * self.out_channels    )







				def 				__UpperCAmelCase       (			self     :							Optional[Any]      ,       lowerCamelCase_     :							torch.Tensor      ,       lowerCamelCase_     :							Optional[torch.Tensor] = None      ,       lowerCamelCase_     :							Optional[torch.LongTensor] = None      ,       lowerCamelCase_     :							Optional[torch.LongTensor] = None      ,       lowerCamelCase_     :							Dict[str, Any] = None      ,       lowerCamelCase_     :							Optional[torch.Tensor] = None      ,       lowerCamelCase_     :							Optional[torch.Tensor] = None      ,       lowerCamelCase_     :							bool = True      ,       ):

					"""simple docstring"""






					if attention_mask is not None and attention_mask.ndim == 2:
						# assume that mask is expressed as:
						#   (1 = keep,      0 = discard)
						# convert mask into a bias that can be added to attention scores:
						#       (keep = +0,     discard = -10000.0)
						_lowercase			:     Optional[Any]      					=				(1 - attention_mask.to(hidden_states.dtype    )) * -1_0000.0
						_lowercase			:     Optional[Any]      					=				attention_mask.unsqueeze(1    )

					# convert encoder_attention_mask to a bias the same way we do for attention_mask
					if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
						_lowercase			:     List[Any]      					=				(1 - encoder_attention_mask.to(hidden_states.dtype    )) * -1_0000.0
						_lowercase			:     Any      					=				encoder_attention_mask.unsqueeze(1    )

					# 1. Input
					if self.is_input_continuous:
						_lowercase					,   _lowercase					,   _lowercase					,   _lowercase			:     List[Any]      					=				hidden_states.shape
						_lowercase			:     int      					=				hidden_states

						_lowercase			:     Any      					=				self.norm(lowerCamelCase_    )
						if not self.use_linear_projection:
							_lowercase			:     Any      					=				self.proj_in(lowerCamelCase_    )
							_lowercase			:     Any      					=				hidden_states.shape[1]
							_lowercase			:     List[Any]      					=				hidden_states.permute(0      ,       2      ,       3      ,       1    ).reshape(lowerCamelCase_      ,       height * width      ,       lowerCamelCase_    )
						else:
							_lowercase			:     Any      					=				hidden_states.shape[1]
							_lowercase			:     int      					=				hidden_states.permute(0      ,       2      ,       3      ,       1    ).reshape(lowerCamelCase_      ,       height * width      ,       lowerCamelCase_    )
							_lowercase			:     Any      					=				self.proj_in(lowerCamelCase_    )
					elif self.is_input_vectorized:
						_lowercase			:     Optional[Any]      					=				self.latent_image_embedding(lowerCamelCase_    )
					elif self.is_input_patches:
						_lowercase			:     Union[str, Any]      					=				self.pos_embed(lowerCamelCase_    )

					# 2. Blocks
					for block in self.transformer_blocks:
						_lowercase			:     Tuple      					=				block(
						    lowerCamelCase_      ,       attention_mask=lowerCamelCase_      ,       encoder_hidden_states=lowerCamelCase_      ,       encoder_attention_mask=lowerCamelCase_      ,       timestep=lowerCamelCase_      ,       cross_attention_kwargs=lowerCamelCase_      ,       class_labels=lowerCamelCase_      ,       )

					# 3. Output
					if self.is_input_continuous:
						if not self.use_linear_projection:
							_lowercase			:     Union[str, Any]      					=				hidden_states.reshape(lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_    ).permute(0      ,       3      ,       1      ,       2    ).contiguous()
							_lowercase			:     Tuple      					=				self.proj_out(lowerCamelCase_    )
						else:
							_lowercase			:     int      					=				self.proj_out(lowerCamelCase_    )
							_lowercase			:     List[Any]      					=				hidden_states.reshape(lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_    ).permute(0      ,       3      ,       1      ,       2    ).contiguous()

						_lowercase			:     List[str]      					=				hidden_states + residual
					elif self.is_input_vectorized:
						_lowercase			:     Dict      					=				self.norm_out(lowerCamelCase_    )
						_lowercase			:     Dict      					=				self.out(lowerCamelCase_    )
						# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
						_lowercase			:     Dict      					=				logits.permute(0      ,       2      ,       1    )

						# log(p(x_0))
						_lowercase			:     int      					=				F.log_softmax(logits.double()      ,       dim=1    ).float()
					elif self.is_input_patches:
						# TODO: cleanup!
						_lowercase			:     Tuple      					=				self.transformer_blocks[0].norma.emb(
						    lowerCamelCase_      ,       lowerCamelCase_      ,       hidden_dtype=hidden_states.dtype    )
						_lowercase					,   _lowercase			:     List[Any]      					=				self.proj_out_a(F.silu(lowerCamelCase_    )    ).chunk(2      ,       dim=1    )
						_lowercase			:     Optional[int]      					=				self.norm_out(lowerCamelCase_    ) * (1 + scale[:, None]) + shift[:, None]
						_lowercase			:     Optional[Any]      					=				self.proj_out_a(lowerCamelCase_    )

						# unpatchify
						_lowercase			:     Union[str, Any]      					=				int(hidden_states.shape[1] ** 0.5    )
						_lowercase			:     int      					=				hidden_states.reshape(
						    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)    )
						_lowercase			:     str      					=				torch.einsum('nhwpqc->nchpwq'      ,       lowerCamelCase_    )
						_lowercase			:     List[str]      					=				hidden_states.reshape(
						    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)    )

					if not return_dict:
						return (output,)

					return TransformeraDModelOutput(sample=lowerCamelCase_    )



