
"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch


snake_case			       =		random.Random()

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_=1.0,					SCREAMING_SNAKE_CASE_=None,					SCREAMING_SNAKE_CASE_=None      ):
					if rng is None:
										SCREAMING_SNAKE_CASE           =     global_rng

					SCREAMING_SNAKE_CASE           =     []
					for batch_idx in range(shape[0]      ):
										values.append([]      )
										for _ in range(shape[1]      ):
															values[-1].append(rng.random() * scale      )

					return values




@require_torch
class 					UpperCamelCase     (	unittest.TestCase				):


				def __init__(    self		,     lowercase__		,     lowercase__=7		,     lowercase__=400		,     lowercase__=2000		,     lowercase__=1		,     lowercase__=0.0		,     lowercase__=24000		,     lowercase__=True		,     )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     parent
									SCREAMING_SNAKE_CASE           =     batch_size
									SCREAMING_SNAKE_CASE           =     min_seq_length
									SCREAMING_SNAKE_CASE           =     max_seq_length
									SCREAMING_SNAKE_CASE           =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									SCREAMING_SNAKE_CASE           =     feature_size
									SCREAMING_SNAKE_CASE           =     padding_value
									SCREAMING_SNAKE_CASE           =     sampling_rate
									SCREAMING_SNAKE_CASE           =     return_attention_mask


				def 							A (    self      )   ->   Union[str, Any]:
									"""simple docstring"""







									return {
									    "feature_size": self.feature_size,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "return_attention_mask": self.return_attention_mask,
									}




				def 							A (    self		,     lowercase__=False		,     lowercase__=False      )   ->   str:
									"""simple docstring"""







									def _flatten(lowercase__      ):
														return list(itertools.chain(*_snake_case      )      )

									if equal_length:
														SCREAMING_SNAKE_CASE           =     floats_list((self.batch_size, self.max_seq_length)      )
									else:
														# make sure that inputs increase in size
														SCREAMING_SNAKE_CASE           =     [
														    _flatten(floats_list((x, self.feature_size)      )      )
														    for x in range(self.min_seq_length		,     self.max_seq_length		,     self.seq_length_diff      )
														]

									if numpify:
														SCREAMING_SNAKE_CASE           =     [np.asarray(_snake_case      ) for x in audio_inputs]

									return audio_inputs






@require_torch
class 					UpperCamelCase     (	lowerCAmelCase_  ,				unittest.TestCase				):
				UpperCAmelCase_    :				str						  =    EncodecFeatureExtractor


				def 							A (    self      )   ->   Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     EnCodecFeatureExtractionTester(self      )


				def 							A (    self      )   ->   str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
									# create three inputs of length 800, 1000, and 1200
									SCREAMING_SNAKE_CASE           =     [floats_list((1, x)      )[0] for x in range(800		,     1400		,     200      )]
									SCREAMING_SNAKE_CASE           =     [np.asarray(_snake_case      ) for audio_input in audio_inputs]

									# Test not batched input
									SCREAMING_SNAKE_CASE           =     feat_extract(audio_inputs[0]		,     return_tensors='np'      ).input_values
									SCREAMING_SNAKE_CASE           =     feat_extract(np_audio_inputs[0]		,     return_tensors='np'      ).input_values
									self.assertTrue(np.allclose(_snake_case		,     _snake_case		,     atol=1E-3      )      )

									# Test batched
									SCREAMING_SNAKE_CASE           =     feat_extract(_snake_case		,     padding=_snake_case		,     return_tensors='np'      ).input_values
									SCREAMING_SNAKE_CASE           =     feat_extract(_snake_case		,     padding=_snake_case		,     return_tensors='np'      ).input_values
									for enc_seq_a, enc_seq_a in zip(_snake_case		,     _snake_case      ):
														self.assertTrue(np.allclose(_snake_case		,     _snake_case		,     atol=1E-3      )      )


				def 							A (    self      )   ->   Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
									SCREAMING_SNAKE_CASE           =     np.random.rand(100      ).astype(np.floataa      )
									SCREAMING_SNAKE_CASE           =     np_audio_inputs.tolist()

									for inputs in [py_audio_inputs, np_audio_inputs]:
														SCREAMING_SNAKE_CASE           =     feature_extractor.pad([{'input_values': inputs}]		,     return_tensors='np'      )
														self.assertTrue(np_processed.input_values.dtype == np.floataa      )
														SCREAMING_SNAKE_CASE           =     feature_extractor.pad([{'input_values': inputs}]		,     return_tensors='pt'      )
														self.assertTrue(pt_processed.input_values.dtype == torch.floataa      )


				def 							A (    self		,     lowercase__      )   ->   Optional[int]:
									"""simple docstring"""







									from datasets import load_dataset

									SCREAMING_SNAKE_CASE           =     load_dataset('hf-internal-testing/librispeech_asr_dummy'		,     'clean'		,     split='validation'      )
									# automatic decoding with librispeech
									SCREAMING_SNAKE_CASE           =     ds.sort('id'      ).select(range(_snake_case      )      )[:num_samples]['audio']

									return [x["array"] for x in audio_samples]


				def 							A (    self      )   ->   List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     torch.tensor(
									    [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
									     3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
									     2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
									     4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
									     7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
									     4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]      )
									# fmt: on
									SCREAMING_SNAKE_CASE           =     self._load_datasamples(1      )
									SCREAMING_SNAKE_CASE           =     EncodecFeatureExtractor()
									SCREAMING_SNAKE_CASE           =     feature_extractor(_snake_case		,     return_tensors='pt'      ).input_values
									self.assertEquals(input_values.shape		,     (1, 1, 93680)      )
									self.assertTrue(torch.allclose(input_values[0, 0, :30]		,     _snake_case		,     atol=1E-6      )      )


				def 							A (    self      )   ->   Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     torch.tensor(
									    [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
									     3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
									     2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
									     4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
									     7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
									     4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]      )
									# fmt: on
									SCREAMING_SNAKE_CASE           =     self._load_datasamples(1      )
									SCREAMING_SNAKE_CASE           =     [np.tile(input_audio[0][None]		,     reps=(2, 1)      )]
									input_audio[0][1] *= 0.5
									SCREAMING_SNAKE_CASE           =     EncodecFeatureExtractor(feature_size=2      )
									SCREAMING_SNAKE_CASE           =     feature_extractor(_snake_case		,     return_tensors='pt'      ).input_values
									self.assertEquals(input_values.shape		,     (1, 2, 93680)      )
									self.assertTrue(torch.allclose(input_values[0, 0, :30]		,     _snake_case		,     atol=1E-6      )      )
									self.assertTrue(torch.allclose(input_values[0, 1, :30]		,     EXPECTED_INPUT_VALUES * 0.5		,     atol=1E-6      )      )




				def 							A (    self      )   ->   Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._load_datasamples(2      )
									# would be easier if the stride was like
									SCREAMING_SNAKE_CASE           =     EncodecFeatureExtractor(feature_size=1		,     chunk_length_s=1		,     overlap=0.01      )

									# pad and trunc raise an error ?
									with self.assertRaisesRegex(
									    _snake_case		,     '^Both padding and truncation were set. Make sure you only set one.$'		,     ):
														SCREAMING_SNAKE_CASE           =     feature_extractor(
														    _snake_case		,     padding='max_length'		,     truncation=_snake_case		,     return_tensors='pt'      ).input_values

									# truncate to chunk
									SCREAMING_SNAKE_CASE           =     feature_extractor(_snake_case		,     truncation=_snake_case		,     return_tensors='pt'      ).input_values
									self.assertEquals(truncated_outputs.shape		,     (2, 1, 71520)      )  # 2 chunks

									# force truncate to max_length
									SCREAMING_SNAKE_CASE           =     feature_extractor(
									    _snake_case		,     truncation=_snake_case		,     max_length=48000		,     return_tensors='pt'      ).input_values
									self.assertEquals(truncated_outputs.shape		,     (2, 1, 48000)      )

									# pad to chunk
									SCREAMING_SNAKE_CASE           =     feature_extractor(_snake_case		,     padding=_snake_case		,     return_tensors='pt'      ).input_values
									self.assertEquals(padded_outputs.shape		,     (2, 1, 95280)      )

									# pad to chunk
									SCREAMING_SNAKE_CASE           =     feature_extractor(_snake_case		,     return_tensors='pt'      ).input_values
									self.assertEquals(truncated_outputs.shape		,     (2, 1, 95280)      )

									# force pad to max length
									SCREAMING_SNAKE_CASE           =     feature_extractor(
									    _snake_case		,     padding='max_length'		,     max_length=100000		,     return_tensors='pt'      ).input_values
									self.assertEquals(truncated_outputs.shape		,     (2, 1, 100000)      )

									# force no pad
									with self.assertRaisesRegex(
									    _snake_case		,     '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'		,     ):
														SCREAMING_SNAKE_CASE           =     feature_extractor(_snake_case		,     padding=_snake_case		,     return_tensors='pt'      ).input_values

									SCREAMING_SNAKE_CASE           =     feature_extractor(input_audio[0]		,     padding=_snake_case		,     return_tensors='pt'      ).input_values
									self.assertEquals(truncated_outputs.shape		,     (1, 1, 93680)      )

									# no pad if no chunk_length_s
									SCREAMING_SNAKE_CASE           =     None
									with self.assertRaisesRegex(
									    _snake_case		,     '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'		,     ):
														SCREAMING_SNAKE_CASE           =     feature_extractor(_snake_case		,     padding=_snake_case		,     return_tensors='pt'      ).input_values

									SCREAMING_SNAKE_CASE           =     feature_extractor(input_audio[0]		,     padding=_snake_case		,     return_tensors='pt'      ).input_values
									self.assertEquals(truncated_outputs.shape		,     (1, 1, 93680)      )

									# no pad if no overlap
									SCREAMING_SNAKE_CASE           =     2
									SCREAMING_SNAKE_CASE           =     None
									with self.assertRaisesRegex(
									    _snake_case		,     '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'		,     ):
														SCREAMING_SNAKE_CASE           =     feature_extractor(_snake_case		,     padding=_snake_case		,     return_tensors='pt'      ).input_values

									SCREAMING_SNAKE_CASE           =     feature_extractor(input_audio[0]		,     padding=_snake_case		,     return_tensors='pt'      ).input_values
									self.assertEquals(truncated_outputs.shape		,     (1, 1, 93680)      )


"""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 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				torch.FloatTensor






class 					UpperCamelCase     (	__magic_name__  ,				__magic_name__				):

				"""simple docstring"""



				@register_to_config
				def __init__(    self		,     lowercase__ = 16		,     lowercase__ = 88		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = 1		,     lowercase__ = 0.0		,     lowercase__ = 32		,     lowercase__ = None		,     lowercase__ = False		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = "geglu"		,     lowercase__ = None		,     lowercase__ = False		,     lowercase__ = False		,     lowercase__ = False		,     lowercase__ = "layer_norm"		,     lowercase__ = True		,     )   ->   List[Any]:
									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE           =     use_linear_projection
									SCREAMING_SNAKE_CASE           =     num_attention_heads
									SCREAMING_SNAKE_CASE           =     attention_head_dim
									SCREAMING_SNAKE_CASE           =     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
									SCREAMING_SNAKE_CASE           =     (in_channels is not None) and (patch_size is None)
									SCREAMING_SNAKE_CASE           =     num_vector_embeds is not None
									SCREAMING_SNAKE_CASE           =     in_channels is not None and patch_size is not None

									if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
														SCREAMING_SNAKE_CASE           =     (
														    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'		,     lowercase__		,     standard_warn=lowercase__      )
														SCREAMING_SNAKE_CASE           =     '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:
														SCREAMING_SNAKE_CASE           =     in_channels

														SCREAMING_SNAKE_CASE           =     torch.nn.GroupNorm(num_groups=lowercase__		,     num_channels=lowercase__		,     eps=1E-6		,     affine=lowercase__      )
														if use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     nn.Convad(lowercase__		,     lowercase__		,     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"

														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     num_vector_embeds
														SCREAMING_SNAKE_CASE           =     self.height * self.width

														SCREAMING_SNAKE_CASE           =     ImagePositionalEmbeddings(
														    num_embed=lowercase__		,     embed_dim=lowercase__		,     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"

														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     sample_size

														SCREAMING_SNAKE_CASE           =     patch_size
														SCREAMING_SNAKE_CASE           =     PatchEmbed(
														    height=lowercase__		,     width=lowercase__		,     patch_size=lowercase__		,     in_channels=lowercase__		,     embed_dim=lowercase__		,     )

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

									# 4. Define output layers
									SCREAMING_SNAKE_CASE           =     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:
																			SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     nn.Convad(lowercase__		,     lowercase__		,     kernel_size=1		,     stride=1		,     padding=0      )
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     nn.LayerNorm(lowercase__      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     self.num_vector_embeds - 1      )
									elif self.is_input_patches:
														SCREAMING_SNAKE_CASE           =     nn.LayerNorm(lowercase__		,     elementwise_affine=lowercase__		,     eps=1E-6      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     2 * inner_dim      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     patch_size * patch_size * self.out_channels      )




				def 							A (    self		,     lowercase__		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = True		,     )   ->   List[Any]:
									"""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)
														SCREAMING_SNAKE_CASE           =     (1 - attention_mask.to(hidden_states.dtype      )) * -10_000.0
														SCREAMING_SNAKE_CASE           =     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:
														SCREAMING_SNAKE_CASE           =     (1 - encoder_attention_mask.to(hidden_states.dtype      )) * -10_000.0
														SCREAMING_SNAKE_CASE           =     encoder_attention_mask.unsqueeze(1      )

									# 1. Input
									if self.is_input_continuous:
														SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     hidden_states.shape
														SCREAMING_SNAKE_CASE           =     hidden_states

														SCREAMING_SNAKE_CASE           =     self.norm(lowercase__      )
														if not self.use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     self.proj_in(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     hidden_states.shape[1]
																			SCREAMING_SNAKE_CASE           =     hidden_states.permute(0		,     2		,     3		,     1      ).reshape(lowercase__		,     height * width		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     hidden_states.shape[1]
																			SCREAMING_SNAKE_CASE           =     hidden_states.permute(0		,     2		,     3		,     1      ).reshape(lowercase__		,     height * width		,     lowercase__      )
																			SCREAMING_SNAKE_CASE           =     self.proj_in(lowercase__      )
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     self.latent_image_embedding(lowercase__      )
									elif self.is_input_patches:
														SCREAMING_SNAKE_CASE           =     self.pos_embed(lowercase__      )

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

									# 3. Output
									if self.is_input_continuous:
														if not self.use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     hidden_states.reshape(lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      ).permute(0		,     3		,     1		,     2      ).contiguous()
																			SCREAMING_SNAKE_CASE           =     self.proj_out(lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     self.proj_out(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     hidden_states.reshape(lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      ).permute(0		,     3		,     1		,     2      ).contiguous()

														SCREAMING_SNAKE_CASE           =     hidden_states + residual
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     self.norm_out(lowercase__      )
														SCREAMING_SNAKE_CASE           =     self.out(lowercase__      )
														# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
														SCREAMING_SNAKE_CASE           =     logits.permute(0		,     2		,     1      )

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

														# unpatchify
														SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     int(hidden_states.shape[1] ** 0.5      )
														SCREAMING_SNAKE_CASE           =     hidden_states.reshape(
														    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)      )
														SCREAMING_SNAKE_CASE           =     torch.einsum('nhwpqc->nchpwq'		,     lowercase__      )
														SCREAMING_SNAKE_CASE           =     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=lowercase__      )
