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 UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					42

class 	_a  (							lowerCamelCase_							,		lowerCamelCase_					):



	"""simple docstring"""



	@register_to_config
	def __init__(   self       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = 3       ,  lowerCAmelCase_ = 3       ,  lowerCAmelCase_ = False       ,  lowerCAmelCase_ = "positional"       ,  lowerCAmelCase_ = 0       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")       ,  lowerCAmelCase_ = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")       ,  lowerCAmelCase_ = (224, 448, 672, 896)       ,  lowerCAmelCase_ = 2       ,  lowerCAmelCase_ = 1       ,  lowerCAmelCase_ = 1       ,  lowerCAmelCase_ = "conv"       ,  lowerCAmelCase_ = "conv"       ,  lowerCAmelCase_ = "silu"       ,  lowerCAmelCase_ = 8       ,  lowerCAmelCase_ = 32       ,  lowerCAmelCase_ = 1e-5       ,  lowerCAmelCase_ = "default"       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  ):
		super().__init__()

		_lowercase   =sample_size
		_lowercase   =block_out_channels[0] * 4

		# Check inputs
		if len(lowerCAmelCase_       ) != len(lowerCAmelCase_       ):
			raise ValueError(
			    F'''Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'''       )

		if len(lowerCAmelCase_       ) != len(lowerCAmelCase_       ):
			raise ValueError(
			    F'''Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'''       )

		# input
		_lowercase   =nn.Convad(lowerCAmelCase_       ,  block_out_channels[0]       ,  kernel_size=3       ,  padding=(1, 1)       )

		# time
		if time_embedding_type == "fourier":
			_lowercase   =GaussianFourierProjection(embedding_size=block_out_channels[0]       ,  scale=16       )
			_lowercase   =2 * block_out_channels[0]
		elif time_embedding_type == "positional":
			_lowercase   =Timesteps(block_out_channels[0]       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
			_lowercase   =block_out_channels[0]

		_lowercase   =TimestepEmbedding(lowerCAmelCase_       ,  lowerCAmelCase_       )

		# class embedding
		if class_embed_type is None and num_class_embeds is not None:
			_lowercase   =nn.Embedding(lowerCAmelCase_       ,  lowerCAmelCase_       )
		elif class_embed_type == "timestep":
			_lowercase   =TimestepEmbedding(lowerCAmelCase_       ,  lowerCAmelCase_       )
		elif class_embed_type == "identity":
			_lowercase   =nn.Identity(lowerCAmelCase_       ,  lowerCAmelCase_       )
		else:
			_lowercase   =None

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

		# down
		_lowercase   =block_out_channels[0]
		for i, down_block_type in enumerate(lowerCAmelCase_       ):
			_lowercase   =output_channel
			_lowercase   =block_out_channels[i]
			_lowercase   =i == len(lowerCAmelCase_       ) - 1

			_lowercase   =get_down_block(
			    lowerCAmelCase_       ,  num_layers=lowerCAmelCase_       ,  in_channels=lowerCAmelCase_       ,  out_channels=lowerCAmelCase_       ,  temb_channels=lowerCAmelCase_       ,  add_downsample=not is_final_block       ,  resnet_eps=lowerCAmelCase_       ,  resnet_act_fn=lowerCAmelCase_       ,  resnet_groups=lowerCAmelCase_       ,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel       ,  downsample_padding=lowerCAmelCase_       ,  resnet_time_scale_shift=lowerCAmelCase_       ,  downsample_type=lowerCAmelCase_       ,  )
			self.down_blocks.append(lowerCAmelCase_       )

		# mid
		_lowercase   =UNetMidBlockaD(
		    in_channels=block_out_channels[-1]       ,  temb_channels=lowerCAmelCase_       ,  resnet_eps=lowerCAmelCase_       ,  resnet_act_fn=lowerCAmelCase_       ,  output_scale_factor=lowerCAmelCase_       ,  resnet_time_scale_shift=lowerCAmelCase_       ,  attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]       ,  resnet_groups=lowerCAmelCase_       ,  add_attention=lowerCAmelCase_       ,  )

		# up
		_lowercase   =list(reversed(lowerCAmelCase_       )       )
		_lowercase   =reversed_block_out_channels[0]
		for i, up_block_type in enumerate(lowerCAmelCase_       ):
			_lowercase   =output_channel
			_lowercase   =reversed_block_out_channels[i]
			_lowercase   =reversed_block_out_channels[min(i + 1       ,  len(lowerCAmelCase_       ) - 1       )]

			_lowercase   =i == len(lowerCAmelCase_       ) - 1

			_lowercase   =get_up_block(
			    lowerCAmelCase_       ,  num_layers=layers_per_block + 1       ,  in_channels=lowerCAmelCase_       ,  out_channels=lowerCAmelCase_       ,  prev_output_channel=lowerCAmelCase_       ,  temb_channels=lowerCAmelCase_       ,  add_upsample=not is_final_block       ,  resnet_eps=lowerCAmelCase_       ,  resnet_act_fn=lowerCAmelCase_       ,  resnet_groups=lowerCAmelCase_       ,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel       ,  resnet_time_scale_shift=lowerCAmelCase_       ,  upsample_type=lowerCAmelCase_       ,  )
			self.up_blocks.append(lowerCAmelCase_       )
			_lowercase   =output_channel

		# out
		_lowercase   =norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       ,  32       )
		_lowercase   =nn.GroupNorm(num_channels=block_out_channels[0]       ,  num_groups=lowerCAmelCase_       ,  eps=lowerCAmelCase_       )
		_lowercase   =nn.SiLU()
		_lowercase   =nn.Convad(block_out_channels[0]       ,  lowerCAmelCase_       ,  kernel_size=3       ,  padding=1       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = True       ,  ):
		# 0. center input if necessary
		if self.config.center_input_sample:
			_lowercase   =2 * sample - 1.0

		# 1. time
		_lowercase   =timestep
		if not torch.is_tensor(lowerCAmelCase_       ):
			_lowercase   =torch.tensor([timesteps]       ,  dtype=torch.long       ,  device=sample.device       )
		elif torch.is_tensor(lowerCAmelCase_       ) and len(timesteps.shape       ) == 0:
			_lowercase   =timesteps[None].to(sample.device       )

		# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
		_lowercase   =timesteps * torch.ones(sample.shape[0]       ,  dtype=timesteps.dtype       ,  device=timesteps.device       )

		_lowercase   =self.time_proj(lowerCAmelCase_       )

		# timesteps does not contain any weights and will always return f32 tensors
		# but time_embedding might actually be running in fp16. so we need to cast here.
		# there might be better ways to encapsulate this.
		_lowercase   =t_emb.to(dtype=self.dtype       )
		_lowercase   =self.time_embedding(lowerCAmelCase_       )

		if self.class_embedding is not None:
			if class_labels is None:
				raise ValueError("class_labels should be provided when doing class conditioning"       )

			if self.config.class_embed_type == "timestep":
				_lowercase   =self.time_proj(lowerCAmelCase_       )

			_lowercase   =self.class_embedding(lowerCAmelCase_       ).to(dtype=self.dtype       )
			_lowercase   =emb + class_emb

		# 2. pre-process
		_lowercase   =sample
		_lowercase   =self.conv_in(lowerCAmelCase_       )

		# 3. down
		_lowercase   =(sample,)
		for downsample_block in self.down_blocks:
			if hasattr(lowerCAmelCase_       ,  "skip_conv"       ):
				_lowercase   ,  _lowercase   ,  _lowercase   =downsample_block(
				    hidden_states=lowerCAmelCase_       ,  temb=lowerCAmelCase_       ,  skip_sample=lowerCAmelCase_       )
			else:
				_lowercase   ,  _lowercase   =downsample_block(hidden_states=lowerCAmelCase_       ,  temb=lowerCAmelCase_       )

			down_block_res_samples += res_samples

		# 4. mid
		_lowercase   =self.mid_block(lowerCAmelCase_       ,  lowerCAmelCase_       )

		# 5. up
		_lowercase   =None
		for upsample_block in self.up_blocks:
			_lowercase   =down_block_res_samples[-len(upsample_block.resnets       ) :]
			_lowercase   =down_block_res_samples[: -len(upsample_block.resnets       )]

			if hasattr(lowerCAmelCase_       ,  "skip_conv"       ):
				_lowercase   ,  _lowercase   =upsample_block(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
			else:
				_lowercase   =upsample_block(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )

        # 6. post-process
		_lowercase   =self.conv_norm_out(lowerCAmelCase_       )
		_lowercase   =self.conv_act(lowerCAmelCase_       )
		_lowercase   =self.conv_out(lowerCAmelCase_       )

		if skip_sample is not None:
			sample += skip_sample

		if self.config.time_embedding_type == "fourier":
			_lowercase   =timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]       )))       )
			_lowercase   =sample / timesteps

		if not return_dict:
			return (sample,)

		return UNetaDOutput(sample=lowerCAmelCase_       )











"""simple docstring"""





from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
						from .data.data_collator import DataCollator
						from .modeling_utils import PreTrainedModel
						from .tokenization_utils_base import PreTrainedTokenizerBase
						from .trainer_callback import TrainerCallback
						from .trainer_utils import EvalPrediction, PredictionOutput
						from .training_args import TrainingArguments


lowerCAmelCase__        =      logging.get_logger(__name__)





class 							_lowerCamelCase   (							_lowercase			):


						def __init__(self ,				__a = None ,				__a = None ,				__a = None ,				__a = None ,				__a = None ,				__a = None ,				__a = None ,				__a = None ,				__a = None ,				__a = (None, None) ,				__a = None ,				)			-> List[Any]:
								super().__init__(
								    model=__a ,				args=__a ,				data_collator=__a ,				train_dataset=__a ,				eval_dataset=__a ,				tokenizer=__a ,				model_init=__a ,				compute_metrics=__a ,				callbacks=__a ,				optimizers=__a ,				preprocess_logits_for_metrics=__a ,				)

								# Override self.model.generation_config if a GenerationConfig is specified in args.
								# Priority: args.generation_config > model.generation_config > default GenerationConfig.
								if self.args.generation_config is not None:
										UpperCamelCase =						self.load_generation_config(self.args.generation_config		)
										UpperCamelCase =						gen_config


						@staticmethod
						def      snake_case_ (__a		)			-> GenerationConfig:

								# GenerationConfig provided, nothing to do
								if isinstance(__a ,				__a		):
										return deepcopy(__a		)

								# str or Path
								UpperCamelCase =						Path(__a		) if isinstance(__a ,				__a		) else gen_config_arg
								UpperCamelCase =						None

								# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
								# This step is required in order to determine config_file_name
								if pretrained_model_name.is_file():
										UpperCamelCase =						pretrained_model_name.name
										UpperCamelCase =						pretrained_model_name.parent
								# dir path
								elif pretrained_model_name.is_dir():
										pass
								# model id or URL
								else:
										UpperCamelCase =						gen_config_arg

								UpperCamelCase =						GenerationConfig.from_pretrained(__a ,				__a		)
								return gen_config


						def      snake_case_ (self ,				__a = None ,				__a = None ,				__a = "eval" ,				**__a ,				)			-> Dict[str, float]:
								UpperCamelCase =						gen_kwargs.copy()
								if gen_kwargs.get("max_length"		) is None and gen_kwargs.get("max_new_tokens"		) is None:
										UpperCamelCase =						self.args.generation_max_length
								UpperCamelCase =						(
								    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"		) is not None else self.args.generation_num_beams
								)
								UpperCamelCase =						gen_kwargs

								return super().evaluate(__a ,				ignore_keys=__a ,				metric_key_prefix=__a		)


						def      snake_case_ (self ,				__a ,				__a = None ,				__a = "test" ,				**__a ,				)			-> "PredictionOutput":
								UpperCamelCase =						gen_kwargs.copy()
								if gen_kwargs.get("max_length"		) is None and gen_kwargs.get("max_new_tokens"		) is None:
										UpperCamelCase =						self.args.generation_max_length
								UpperCamelCase =						(
								    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"		) is not None else self.args.generation_num_beams
								)
								UpperCamelCase =						gen_kwargs

								return super().predict(__a ,				ignore_keys=__a ,				metric_key_prefix=__a		)


						def      snake_case_ (self ,				__a ,				__a ,				__a ,				__a = None ,				**__a ,				)			-> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

								if not self.args.predict_with_generate or prediction_loss_only:
										return super().prediction_step(
										    __a ,				__a ,				prediction_loss_only=__a ,				ignore_keys=__a		)

								UpperCamelCase =						"labels" in inputs
								UpperCamelCase =						self._prepare_inputs(__a		)

								# XXX: adapt synced_gpus for fairscale as well
								# Priority (handled in generate):
								# gen_kwargs > model.generation_config > default GenerationConfig()

								if len(__a		) == 0 and hasattr(self ,				"_gen_kwargs"		):
										UpperCamelCase =						self._gen_kwargs.copy()

								if gen_kwargs.get("max_length"		) is None and gen_kwargs.get("max_new_tokens"		) is None:
										UpperCamelCase =						self.model.config.max_length
								UpperCamelCase =						(
								    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"		) is not None else self.model.config.num_beams
								)
								UpperCamelCase =						True if is_deepspeed_zeroa_enabled() else False
								UpperCamelCase =						(
								    gen_kwargs["synced_gpus"] if gen_kwargs.get("synced_gpus"		) is not None else default_synced_gpus
								)

								# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
								# (otherwise, it would continue generating from the padded `decoder_input_ids`)
								if (
								    "labels" in inputs
								    and "decoder_input_ids" in inputs
								    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
								):
										UpperCamelCase =						{k: v for k, v in inputs.items() if k != "decoder_input_ids"}
								UpperCamelCase =						self.model.generate(**__a ,				**__a		)

								# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
								# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
								# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
								if self.model.generation_config._from_model_config:
										UpperCamelCase =						False

								# Retrieves GenerationConfig from model.generation_config
								UpperCamelCase =						self.model.generation_config
								# in case the batch is shorter than max length, the output should be padded
								if generated_tokens.shape[-1] < gen_config.max_length:
										UpperCamelCase =						self._pad_tensors_to_max_len(__a ,				gen_config.max_length		)
								elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
										UpperCamelCase =						self._pad_tensors_to_max_len(__a ,				gen_config.max_new_tokens + 1		)

								with torch.no_grad():
										if has_labels:
												with self.compute_loss_context_manager():
														UpperCamelCase =						model(**__a		)
												if self.label_smoother is not None:
														UpperCamelCase =						self.label_smoother(__a ,				inputs["labels"]		).mean().detach()
												else:
														UpperCamelCase =						(outputs["loss"] if isinstance(__a ,				__a		) else outputs[0]).mean().detach()
										else:
												UpperCamelCase =						None

								if self.args.prediction_loss_only:
										return loss, None, None

								if has_labels:
										UpperCamelCase =						inputs["labels"]
										if labels.shape[-1] < gen_config.max_length:
												UpperCamelCase =						self._pad_tensors_to_max_len(__a ,				gen_config.max_length		)
										elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
												UpperCamelCase =						self._pad_tensors_to_max_len(__a ,				gen_config.max_new_tokens + 1		)
								else:
										UpperCamelCase =						None

								return loss, generated_tokens, labels




						def      snake_case_ (self ,				__a ,				__a		)			-> str:
								if self.tokenizer is not None and hasattr(self.tokenizer ,				"pad_token_id"		):
										# If PAD token is not defined at least EOS token has to be defined
										UpperCamelCase =						(
										    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
										)
								else:
										if self.model.config.pad_token_id is not None:
												UpperCamelCase =						self.model.config.pad_token_id
										else:
												raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors"		)

								UpperCamelCase =						pad_token_id * torch.ones(
								    (tensor.shape[0], max_length) ,				dtype=tensor.dtype ,				device=tensor.device		)
								UpperCamelCase =						tensor
								return padded_tensor






