'''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 get_down_block, get_mid_block, get_out_block, get_up_block






@dataclass
class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	torch.FloatTensor







class 							a__(   lowerCAmelCase__					,		lowerCAmelCase__      ):


			'''simple docstring'''






			@register_to_config
			def __init__(  self			,       __lowerCAmelCase = 65536			,       __lowerCAmelCase = None			,       __lowerCAmelCase = 2			,       __lowerCAmelCase = 2			,       __lowerCAmelCase = 0			,       __lowerCAmelCase = "fourier"			,       __lowerCAmelCase = True			,       __lowerCAmelCase = False			,       __lowerCAmelCase = 0.0			,       __lowerCAmelCase = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,       __lowerCAmelCase = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,       __lowerCAmelCase = "UNetMidBlock1D"			,       __lowerCAmelCase = None			,       __lowerCAmelCase = (32, 32, 64)			,       __lowerCAmelCase = None			,       __lowerCAmelCase = 8			,       __lowerCAmelCase = 1			,       __lowerCAmelCase = False			,       ):




					"""simple docstring"""


					super().__init__()
					lowerCAmelCase     =    sample_size

					# time
					if time_embedding_type == "fourier":
							lowerCAmelCase     =    GaussianFourierProjection(
							    embedding_size=8			,       set_W_to_weight=__lowerCAmelCase			,       log=__lowerCAmelCase			,       flip_sin_to_cos=__lowerCAmelCase)
							lowerCAmelCase     =    2 * block_out_channels[0]
					elif time_embedding_type == "positional":
							lowerCAmelCase     =    Timesteps(
							    block_out_channels[0]			,       flip_sin_to_cos=__lowerCAmelCase			,       downscale_freq_shift=__lowerCAmelCase)
							lowerCAmelCase     =    block_out_channels[0]

					if use_timestep_embedding:
							lowerCAmelCase     =    block_out_channels[0] * 4
							lowerCAmelCase     =    TimestepEmbedding(
							    in_channels=__lowerCAmelCase			,       time_embed_dim=__lowerCAmelCase			,       act_fn=__lowerCAmelCase			,       out_dim=block_out_channels[0]			,       )

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

					# down
					lowerCAmelCase     =    in_channels
					for i, down_block_type in enumerate(__lowerCAmelCase):
							lowerCAmelCase     =    output_channel
							lowerCAmelCase     =    block_out_channels[i]

							if i == 0:
									input_channel += extra_in_channels

							lowerCAmelCase     =    i == len(__lowerCAmelCase) - 1

							lowerCAmelCase     =    get_down_block(
							    __lowerCAmelCase			,       num_layers=__lowerCAmelCase			,       in_channels=__lowerCAmelCase			,       out_channels=__lowerCAmelCase			,       temb_channels=block_out_channels[0]			,       add_downsample=not is_final_block or downsample_each_block			,       )
							self.down_blocks.append(__lowerCAmelCase)

					# mid
					lowerCAmelCase     =    get_mid_block(
					    __lowerCAmelCase			,       in_channels=block_out_channels[-1]			,       mid_channels=block_out_channels[-1]			,       out_channels=block_out_channels[-1]			,       embed_dim=block_out_channels[0]			,       num_layers=__lowerCAmelCase			,       add_downsample=__lowerCAmelCase			,       )

					# up
					lowerCAmelCase     =    list(reversed(__lowerCAmelCase))
					lowerCAmelCase     =    reversed_block_out_channels[0]
					if out_block_type is None:
							lowerCAmelCase     =    out_channels
					else:
							lowerCAmelCase     =    block_out_channels[0]

					for i, up_block_type in enumerate(__lowerCAmelCase):
							lowerCAmelCase     =    output_channel
							lowerCAmelCase     =    (
							    reversed_block_out_channels[i + 1] if i < len(__lowerCAmelCase) - 1 else final_upsample_channels
							)

							lowerCAmelCase     =    i == len(__lowerCAmelCase) - 1

							lowerCAmelCase     =    get_up_block(
							    __lowerCAmelCase			,       num_layers=__lowerCAmelCase			,       in_channels=__lowerCAmelCase			,       out_channels=__lowerCAmelCase			,       temb_channels=block_out_channels[0]			,       add_upsample=not is_final_block			,       )
							self.up_blocks.append(__lowerCAmelCase)
							lowerCAmelCase     =    output_channel

					# out
					lowerCAmelCase     =    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,       32)
					lowerCAmelCase     =    get_out_block(
					    out_block_type=__lowerCAmelCase			,       num_groups_out=__lowerCAmelCase			,       embed_dim=block_out_channels[0]			,       out_channels=__lowerCAmelCase			,       act_fn=__lowerCAmelCase			,       fc_dim=block_out_channels[-1] // 4			,       )







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = True			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    timestep
					if not torch.is_tensor(__lowerCAmelCase):
							lowerCAmelCase     =    torch.tensor([timesteps]			,       dtype=torch.long			,       device=sample.device)
					elif torch.is_tensor(__lowerCAmelCase) and len(timesteps.shape) == 0:
							lowerCAmelCase     =    timesteps[None].to(sample.device)

					lowerCAmelCase     =    self.time_proj(__lowerCAmelCase)
					if self.config.use_timestep_embedding:
							lowerCAmelCase     =    self.time_mlp(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    timestep_embed[..., None]
							lowerCAmelCase     =    timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
							lowerCAmelCase     =    timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

					# 2. down
					lowerCAmelCase     =    ()
					for downsample_block in self.down_blocks:
							lowerCAmelCase   ,	lowerCAmelCase     =    downsample_block(hidden_states=__lowerCAmelCase			,       temb=__lowerCAmelCase)
							down_block_res_samples += res_samples

					# 3. mid
					if self.mid_block:
							lowerCAmelCase     =    self.mid_block(__lowerCAmelCase			,       __lowerCAmelCase)

					# 4. up
					for i, upsample_block in enumerate(self.up_blocks):
							lowerCAmelCase     =    down_block_res_samples[-1:]
							lowerCAmelCase     =    down_block_res_samples[:-1]
							lowerCAmelCase     =    upsample_block(__lowerCAmelCase			,       res_hidden_states_tuple=__lowerCAmelCase			,       temb=__lowerCAmelCase)

					# 5. post-process
					if self.out_block:
							lowerCAmelCase     =    self.out_block(__lowerCAmelCase			,       __lowerCAmelCase)

					if not return_dict:
							return (sample,)

					return UNetaDOutput(sample=__lowerCAmelCase)





'''simple docstring'''




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			['''image_processor''', '''tokenizer''']
			UpperCAmelCase_     :	str						=			'''ViltImageProcessor'''
			UpperCAmelCase_     :	Any						=			('''BertTokenizer''', '''BertTokenizerFast''')



			def __init__(  self			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
							    """ instead."""			,       __lowerCAmelCase			,       )
							lowerCAmelCase     =    kwargs.pop("""feature_extractor""")

					lowerCAmelCase     =    image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
							raise ValueError("""You need to specify an `image_processor`.""")
					if tokenizer is None:
							raise ValueError("""You need to specify a `tokenizer`.""")

					super().__init__(__lowerCAmelCase			,       __lowerCAmelCase)
					lowerCAmelCase     =    self.image_processor



			def __call__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = False			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = 0			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer(
					    text=__lowerCAmelCase			,       add_special_tokens=__lowerCAmelCase			,       padding=__lowerCAmelCase			,       truncation=__lowerCAmelCase			,       max_length=__lowerCAmelCase			,       stride=__lowerCAmelCase			,       pad_to_multiple_of=__lowerCAmelCase			,       return_token_type_ids=__lowerCAmelCase			,       return_attention_mask=__lowerCAmelCase			,       return_overflowing_tokens=__lowerCAmelCase			,       return_special_tokens_mask=__lowerCAmelCase			,       return_offsets_mapping=__lowerCAmelCase			,       return_length=__lowerCAmelCase			,       verbose=__lowerCAmelCase			,       return_tensors=__lowerCAmelCase			,       **__lowerCAmelCase			,       )
					# add pixel_values + pixel_mask
					lowerCAmelCase     =    self.image_processor(__lowerCAmelCase			,       return_tensors=__lowerCAmelCase)
					encoding.update(__lowerCAmelCase)

					return encoding



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.batch_decode(*__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.decode(*__lowerCAmelCase			,       **__lowerCAmelCase)



			@property
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer.model_input_names
					lowerCAmelCase     =    self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



			@property
			def 				a_			(  self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,       __lowerCAmelCase			,       )
					return self.image_processor_class







			@property
			def 				a_			(  self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,       __lowerCAmelCase			,       )
					return self.image_processor




