




import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       dataset
							__lowerCamelCase									=       process
							__lowerCamelCase									=       params


			def __len__( self				)       -> List[str]:



							'''simple docstring'''
							return len(self.dataset				)






			def __getitem__( self     ,		lowerCamelCase__				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       self.dataset[i]
							__lowerCamelCase									=       self.process(lowerCamelCase__     ,		**self.params				)
							return processed







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__=None				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       loader
							__lowerCamelCase									=       infer
							__lowerCamelCase									=       params
							if loader_batch_size == 1:
											# Let's spare some time by deactivating altogether
											__lowerCamelCase									=       None
							__lowerCamelCase									=       loader_batch_size

							# Internal bookkeeping
							__lowerCamelCase									=       None
							__lowerCamelCase									=       None


			def __len__( self				)       -> Union[str, Any]:



							'''simple docstring'''
							return len(self.loader				)


			def __iter__( self				)       -> List[str]:



							'''simple docstring'''
							__lowerCamelCase									=       iter(self.loader				)
							return self


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							if isinstance(self._loader_batch_data     ,		torch.Tensor				):
											# Batch data is simple tensor, just fetch the slice
											__lowerCamelCase									=       self._loader_batch_data[self._loader_batch_index]
							else:
											# Batch data is assumed to be BaseModelOutput (or dict)
											__lowerCamelCase									=       {}
											for k, element in self._loader_batch_data.items():
															if isinstance(lowerCamelCase__     ,		lowerCamelCase__				):
																			# Convert ModelOutput to tuple first
																			__lowerCamelCase									=       element.to_tuple()
																			if isinstance(element[0]     ,		torch.Tensor				):
																							__lowerCamelCase									=       tuple(el[self._loader_batch_index].unsqueeze(0				) for el in element				)
																			elif isinstance(element[0]     ,		np.ndarray				):
																							__lowerCamelCase									=       tuple(np.expand_dims(el[self._loader_batch_index]     ,		0				) for el in element				)
																			continue
															if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(lowerCamelCase__     ,		lowerCamelCase__				):
																			# Those are stored as lists of tensors so need specific unbatching.
																			if isinstance(element[0]     ,		torch.Tensor				):
																							__lowerCamelCase									=       tuple(el[self._loader_batch_index].unsqueeze(0				) for el in element				)
																			elif isinstance(element[0]     ,		np.ndarray				):
																							__lowerCamelCase									=       tuple(np.expand_dims(el[self._loader_batch_index]     ,		0				) for el in element				)
																			continue
															if element is None:
																			# This can happen for optional data that get passed around
																			__lowerCamelCase									=       None
															elif isinstance(element[self._loader_batch_index]     ,		torch.Tensor				):
																			# Take correct batch data, but make it looked like batch_size=1
																			# For compatibility with other methods within transformers

																			__lowerCamelCase									=       element[self._loader_batch_index].unsqueeze(0				)
															elif isinstance(element[self._loader_batch_index]     ,		np.ndarray				):
																			# Take correct batch data, but make it looked like batch_size=1
																			# For compatibility with other methods within transformers
																			__lowerCamelCase									=       np.expand_dims(element[self._loader_batch_index]     ,		0				)
															else:
																			# This is typically a list, so no need to `unsqueeze`.
																			__lowerCamelCase									=       element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
											__lowerCamelCase									=       self._loader_batch_data.__class__(lowerCamelCase__				)
							self._loader_batch_index += 1
							return result






			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
											# We are currently unrolling a batch so we just need to return
											# the current item within a batch
											return self.loader_batch_item()

							# We're out of items within a batch
							__lowerCamelCase									=       next(self.iterator				)
							__lowerCamelCase									=       self.infer(lowerCamelCase__     ,		**self.params				)
							# We now have a batch of "inferred things".
							if self.loader_batch_size is not None:
											# Try to infer the size of the batch
											if isinstance(lowerCamelCase__     ,		torch.Tensor				):
															__lowerCamelCase									=       processed
											else:
															__lowerCamelCase									=       list(processed.keys()				)[0]
															__lowerCamelCase									=       processed[key]
											if isinstance(lowerCamelCase__     ,		lowerCamelCase__				):
															__lowerCamelCase									=       len(lowerCamelCase__				)
											else:
															__lowerCamelCase									=       first_tensor.shape[0]
											if 0 < observed_batch_size < self.loader_batch_size:
															# could be last batch so we can't unroll as many
															# elements.
															__lowerCamelCase									=       observed_batch_size
											# Setting internal index to unwrap the batch
											__lowerCamelCase									=       processed
											__lowerCamelCase									=       0
											return self.loader_batch_item()
							else:
											# We're not unrolling batches
											return processed







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__=None				)       -> Optional[int]:



							'''simple docstring'''
							super().__init__(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)


			def __iter__( self				)       -> Dict:



							'''simple docstring'''
							__lowerCamelCase									=       iter(self.loader				)
							__lowerCamelCase									=       None
							return self






			def  lowercase_       ( self				)       -> Optional[int]:



							'''simple docstring'''
							if self.subiterator is None:
											__lowerCamelCase									=       self.infer(next(self.iterator				)     ,		**self.params				)
							try:
											# Try to return next item
											__lowerCamelCase									=       next(self.subiterator				)
							except StopIteration:
											# When a preprocess iterator ends, we can start lookig at the next item
											# ChunkIterator will keep feeding until ALL elements of iterator
											# all have created their subiterator and have been iterating against.
											#
											# Another way to look at it, is we're basically flattening lists of lists
											# into a single list, but with generators
											__lowerCamelCase									=       self.infer(next(self.iterator				)     ,		**self.params				)
											__lowerCamelCase									=       next(self.subiterator				)
							return processed







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __iter__( self				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       iter(self.loader				)
							return self






			def  lowercase_       ( self				)       -> Optional[int]:



							'''simple docstring'''
							# Extremely similar to PipelineIterator in its unpacking mechanism
							# BUT, we have an extra required item which is the presence of `is_last`
							# That is because everything is flattened by `PipelineChunkIterator` we
							# need to keep track of how to regroup here in the original `process`
							# boundaries so that `process` and `postprocess` see the same data.

							# This iterator accumulates items (possibly while unbatching) until it
							# its a `is_last` and then just passes it on to the caller.
							__lowerCamelCase									=       False
							__lowerCamelCase									=       []
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
											while self._loader_batch_index < self.loader_batch_size:
															__lowerCamelCase									=       self.loader_batch_item()
															__lowerCamelCase									=       item.pop('is_last'				)
															accumulator.append(lowerCamelCase__				)
															if is_last:
																			return accumulator

							while not is_last:
											__lowerCamelCase									=       self.infer(next(self.iterator				)     ,		**self.params				)
											if self.loader_batch_size is not None:
															if isinstance(lowerCamelCase__     ,		torch.Tensor				):
																			__lowerCamelCase									=       processed
															else:
																			__lowerCamelCase									=       list(processed.keys()				)[0]
																			__lowerCamelCase									=       processed[key]
															if isinstance(lowerCamelCase__     ,		lowerCamelCase__				):
																			__lowerCamelCase									=       len(lowerCamelCase__				)
															else:
																			__lowerCamelCase									=       first_tensor.shape[0]
															if 0 < observed_batch_size < self.loader_batch_size:
																			# could be last batch so we can't unroll as many
																			# elements.
																			__lowerCamelCase									=       observed_batch_size
															__lowerCamelCase									=       processed
															__lowerCamelCase									=       0
															while self._loader_batch_index < self.loader_batch_size:
																			__lowerCamelCase									=       self.loader_batch_item()
																			__lowerCamelCase									=       item.pop('is_last'				)
																			accumulator.append(lowerCamelCase__				)
																			if is_last:
																							return accumulator
											else:
															__lowerCamelCase									=       processed
															__lowerCamelCase									=       item.pop('is_last'				)
															accumulator.append(lowerCamelCase__				)
							return accumulator







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       dataset
							__lowerCamelCase									=       key


			def __len__( self				)       -> Union[str, Any]:



							'''simple docstring'''
							return len(self.dataset				)






			def __getitem__( self     ,		lowerCamelCase__				)       -> Union[str, Any]:



							'''simple docstring'''
							return self.dataset[i][self.key]







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       dataset
							__lowerCamelCase									=       keya
							__lowerCamelCase									=       keya


			def __len__( self				)       -> Dict:



							'''simple docstring'''
							return len(self.dataset				)






			def __getitem__( self     ,		lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}








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        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			42







class        __lowerCAmelCase					(			__magic_name__     ,							__magic_name__					):

			"""simple docstring"""







			@register_to_config
			def __init__( self     ,		lowerCamelCase__ = 65_536     ,		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     ,		)       -> Dict:



							'''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  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = True     ,		)       -> Union[UNetaDOutput, Tuple]:



							'''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__				)


