"""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()
UpperCAmelCase       :    Tuple									=			logging.get_logger(__name__)

UpperCAmelCase       :    Dict									=			{
    "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",
}
UpperCAmelCase       :    Dict									=			[
    "lm_head",
]



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





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

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

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



def 				__a       (			_lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





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

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

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

					for name, value in fairseq_dict.items():
										lowerCamelCase__		:   Dict  				=					False
										if "conv_layers" in name:
															load_conv_layer(
															    __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, )
															lowerCamelCase__		:   Dict  				=					True
										elif "pos_conv" in name:
															load_pos_conv_layer(
															    __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, )
															lowerCamelCase__		:   List[str]  				=					True
										else:
															for key, mapped_key in MAPPING.items():
																				if not is_headless:
																									lowerCamelCase__		:   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]:
																									lowerCamelCase__		:   Any  				=					True
																									if "*" in mapped_key:
																														lowerCamelCase__		:   int  				=					name.split(__UpperCAmelCase						)[0].split('''.'''						)[-2]
																														lowerCamelCase__		:   List[Any]  				=					mapped_key.replace('''*'''							, __UpperCAmelCase						)
																									if "weight_g" in name:
																														lowerCamelCase__		:   List[Any]  				=					'weight_g'
																									elif "weight_v" in name:
																														lowerCamelCase__		:   Optional[Any]  				=					'weight_v'
																									elif "bias" in name:
																														lowerCamelCase__		:   int  				=					'bias'
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														lowerCamelCase__		:   Optional[int]  				=					'weight'
																									else:
																														lowerCamelCase__		:   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 				__a       (			_lowercase							, _lowercase						):







					"""simple docstring"""





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



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   Union[str, Any]  				=					access_by_string(__UpperCAmelCase							, __UpperCAmelCase						)
					lowerCamelCase__		:   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."""						)
					lowerCamelCase__		:   int  				=					fsq_value
					logger.info(f"""{full_name} was correctly initialized from {hf_weight_path}."""						)



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





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

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

					set_weights(__UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, f"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""						)



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





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

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

					set_weights(__UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, f"""layers.{layer_id}.{layer_type}.{weight_type}"""						)



@torch.no_grad()
def 				__a       (			_lowercase							, _lowercase							, _lowercase=None							, _lowercase=None							, _lowercase=True						):







					"""simple docstring"""





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

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

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

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

					lowerCamelCase__		:   Optional[int]  				=					load_dataavec(__UpperCAmelCase						)

					recursively_load_weights(__UpperCAmelCase							, __UpperCAmelCase							, not is_finetuned						)

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

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

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

					lowerCamelCase__		:   List[Any]  				=					inputs.input_values
					lowerCamelCase__		:   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:
										lowerCamelCase__		:   Optional[Any]  				=					model(source=__UpperCAmelCase							, padding_mask=(1 - attention_mask)							, mask=__UpperCAmelCase							, features_only=__UpperCAmelCase						)[
										    'encoder_out'
										].transpose(0							, 1						)
										lowerCamelCase__		:   Union[str, Any]  				=					hf_wavavec(__UpperCAmelCase							, attention_mask=__UpperCAmelCase						)['logits']

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

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

					print(our_output.shape							, their_output.shape						)
					lowerCamelCase__		:   List[str]  				=					torch.max(torch.abs(our_output - their_output						)						).item()
					print(f"""max_absolute_diff = {max_absolute_diff}"""						)  # ~ 1e-7
					lowerCamelCase__		:   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__":
					UpperCAmelCase       :    Dict									=			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"
					)
					UpperCAmelCase       :    Optional[Any]									=			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_    )



