

import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class 					__UpperCAmelCase     (       unittest.TestCase						):



   @property
   def        __magic_name__    (							self							:     Tuple						):
     torch.manual_seed(0						)
     UpperCAmelCase       :							Any  = UNetaDModel(
         block_out_channels=(3_2, 6_4),		layers_per_block=2,		sample_size=3_2,		in_channels=3,		out_channels=3,		down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''),		up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''),		)
     return model



   @property
   def        __magic_name__    (							self							:     int						):
     torch.manual_seed(0						)
     UpperCAmelCase       :							Optional[int]  = VQModel(
         block_out_channels=[3_2, 6_4],		in_channels=3,		out_channels=3,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],		latent_channels=3,		)
     return model



   @property
   def        __magic_name__    (							self							:     str						):
     torch.manual_seed(0						)
     UpperCAmelCase       :							Any  = CLIPTextConfig(
         bos_token_id=0,		eos_token_id=2,		hidden_size=3_2,		intermediate_size=3_7,		layer_norm_eps=1E-05,		num_attention_heads=4,		num_hidden_layers=5,		pad_token_id=1,		vocab_size=1_0_0_0,		)
     return CLIPTextModel(SCREAMING_SNAKE_CASE_						)





   def        __magic_name__    (							self							:     str						):
     UpperCAmelCase       :							Optional[int]  = self.dummy_uncond_unet
     UpperCAmelCase       :							List[str]  = DDIMScheduler()
     UpperCAmelCase       :							Any  = self.dummy_vq_model

     UpperCAmelCase       :							List[str]  = LDMPipeline(unet=SCREAMING_SNAKE_CASE_,		vqvae=SCREAMING_SNAKE_CASE_,		scheduler=SCREAMING_SNAKE_CASE_						)
     ldm.to(SCREAMING_SNAKE_CASE_						)
     ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_						)

     UpperCAmelCase       :							List[str]  = torch.manual_seed(0						)
     UpperCAmelCase       :							Tuple  = ldm(generator=SCREAMING_SNAKE_CASE_,		num_inference_steps=2,		output_type='''numpy'''						).images

     UpperCAmelCase       :							Dict  = torch.manual_seed(0						)
     UpperCAmelCase       :							Dict  = ldm(generator=SCREAMING_SNAKE_CASE_,		num_inference_steps=2,		output_type='''numpy''',		return_dict=SCREAMING_SNAKE_CASE_						)[0]

     UpperCAmelCase       :							Any  = image[0, -3:, -3:, -1]
     UpperCAmelCase       :							Optional[Any]  = image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 6_4, 6_4, 3)
     UpperCAmelCase       :							Optional[int]  = np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]						)
     UpperCAmelCase       :							Optional[Any]  = 1E-2 if torch_device != '''mps''' else 3E-2

     assert np.abs(image_slice.flatten() - expected_slice						).max() < tolerance
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < tolerance






@slow
@require_torch
class 					__UpperCAmelCase     (       unittest.TestCase						):



   def        __magic_name__    (							self							:     List[Any]						):
     UpperCAmelCase       :							List[Any]  = LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''						)
     ldm.to(SCREAMING_SNAKE_CASE_						)
     ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_						)

     UpperCAmelCase       :							Optional[Any]  = torch.manual_seed(0						)
     UpperCAmelCase       :							str  = ldm(generator=SCREAMING_SNAKE_CASE_,		num_inference_steps=5,		output_type='''numpy'''						).images

     UpperCAmelCase       :							Dict  = image[0, -3:, -3:, -1]

     assert image.shape == (1, 2_5_6, 2_5_6, 3)
     UpperCAmelCase       :							Union[str, Any]  = np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]						)
     UpperCAmelCase       :							Dict  = 1E-2 if torch_device != '''mps''' else 3E-2

     assert np.abs(image_slice.flatten() - expected_slice						).max() < tolerance







from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
						from fairscale.optim import OSS


__a  			=					logging.get_logger(__name__)

__a  			=					{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    'constant': get_constant_schedule,
    'constant_w_warmup': get_constant_schedule_with_warmup,
}
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[str]=None       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=None       ,							*SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     List[str]:
					super().__init__(*SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					if config is None:
									assert isinstance(self.model       ,							SCREAMING_SNAKE_CASE_       ), (
									    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
									    f''' {self.model.__class__}'''
									)
									lowercase_       =					self.model.config
					else:
									lowercase_       =					config

					lowercase_       =					data_args
					lowercase_       =					self.config.tgt_vocab_size if isinstance(self.config       ,							SCREAMING_SNAKE_CASE_       ) else self.config.vocab_size

					if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
									assert self.config.pad_token_id is not None, (
									    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
									    " calculation or doing label smoothing."
									)

					if self.config.pad_token_id is None and self.config.eos_token_id is not None:
									logger.warning(
									    f'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
									    ''' padding..'''       )

					if self.args.label_smoothing == 0:
									lowercase_       =					torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id       )
					else:
									# dynamically import label_smoothed_nll_loss
									from utils import label_smoothed_nll_loss

									lowercase_       =					label_smoothed_nll_loss
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     Optional[Any]:
					if self.optimizer is None:
									lowercase_       =					['''bias''', '''LayerNorm.weight''']
									lowercase_       =					[
									    {
									        '''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay       )],
									        '''weight_decay''': self.args.weight_decay,
									    },
									    {
									        '''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay       )],
									        '''weight_decay''': 0.0,
									    },
									]
									lowercase_       =					Adafactor if self.args.adafactor else AdamW
									if self.args.adafactor:
													lowercase_       =					Adafactor
													lowercase_       =					{'''scale_parameter''': False, '''relative_step''': False}
									else:
													lowercase_       =					AdamW
													lowercase_       =					{
													    '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
													    '''eps''': self.args.adam_epsilon,
													}
									lowercase_       =					self.args.learning_rate
									if self.sharded_ddp:
													lowercase_       =					OSS(
													    params=SCREAMING_SNAKE_CASE_       ,							optim=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       ,							)
									else:
													lowercase_       =					optimizer_cls(SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					if self.lr_scheduler is None:
									lowercase_       =					self._get_lr_scheduler(SCREAMING_SNAKE_CASE_       )
					else:  # ignoring --lr_scheduler
									logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''       )
	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Dict       )       ->     Dict:
					lowercase_       =					arg_to_scheduler[self.args.lr_scheduler]
					if self.args.lr_scheduler == "constant":
									lowercase_       =					schedule_func(self.optimizer       )
					elif self.args.lr_scheduler == "constant_w_warmup":
									lowercase_       =					schedule_func(self.optimizer       ,							num_warmup_steps=self.args.warmup_steps       )
					else:
									lowercase_       =					schedule_func(
									    self.optimizer       ,							num_warmup_steps=self.args.warmup_steps       ,							num_training_steps=SCREAMING_SNAKE_CASE_       )
					return scheduler
	def 						_lowercase  (       self  :			Tuple       )       ->     Optional[torch.utils.data.Sampler]:
					if isinstance(self.train_dataset       ,							torch.utils.data.IterableDataset       ):
									return None
					elif is_torch_tpu_available():
									return get_tpu_sampler(self.train_dataset       )
					else:
									if self.args.sortish_sampler:
													self.train_dataset.make_sortish_sampler(
													    self.args.per_device_train_batch_size       ,							distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)       ,							)

									return (
									    RandomSampler(self.train_dataset       )
									    if self.args.local_rank == -1
									    else DistributedSampler(self.train_dataset       )
									)
	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Any:
					if self.args.label_smoothing == 0:
									if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
													# force training to ignore pad token
													lowercase_       =					model(**SCREAMING_SNAKE_CASE_       ,							use_cache=SCREAMING_SNAKE_CASE_       )[0]
													lowercase_       =					self.loss_fn(logits.view(-1       ,							logits.shape[-1]       )       ,							labels.view(-1       )       )
									else:
													# compute usual loss via models
													lowercase_  ,	lowercase_       =					model(**SCREAMING_SNAKE_CASE_       ,							labels=SCREAMING_SNAKE_CASE_       ,							use_cache=SCREAMING_SNAKE_CASE_       )[:2]
					else:
									# compute label smoothed loss
									lowercase_       =					model(**SCREAMING_SNAKE_CASE_       ,							use_cache=SCREAMING_SNAKE_CASE_       )[0]
									lowercase_       =					torch.nn.functional.log_softmax(SCREAMING_SNAKE_CASE_       ,							dim=-1       )
									lowercase_  ,	lowercase_       =					self.loss_fn(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							self.args.label_smoothing       ,							ignore_index=self.config.pad_token_id       )
					return loss, logits
	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     List[Any]:
					lowercase_       =					inputs.pop('''labels'''       )
					lowercase_  ,	lowercase_       =					self._compute_loss(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					return loss
	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			nn.Module       ,							SCREAMING_SNAKE_CASE_  :			Dict[str, Union[torch.Tensor, Any]]       ,							SCREAMING_SNAKE_CASE_  :			bool       ,							SCREAMING_SNAKE_CASE_  :			Optional[List[str]] = None       ,							)       ->     Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
					lowercase_       =					self._prepare_inputs(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					{
					    '''max_length''': self.data_args.val_max_target_length
					    if self.data_args is not None
					    else self.config.max_length,
					    '''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
					}

					if self.args.predict_with_generate and not self.args.prediction_loss_only:
									lowercase_       =					self.model.generate(
									    inputs['''input_ids''']       ,							attention_mask=inputs['''attention_mask''']       ,							**SCREAMING_SNAKE_CASE_       ,							)
									# in case the batch is shorter than max length, the output should be padded
									if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
													lowercase_       =					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_       ,							gen_kwargs['''max_length''']       )

					lowercase_       =					inputs.pop('''labels'''       )
					with torch.no_grad():
									# compute loss on predict data
									lowercase_  ,	lowercase_       =					self._compute_loss(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					loss.mean().detach()
					if self.args.prediction_loss_only:
									return (loss, None, None)

					lowercase_       =					generated_tokens if self.args.predict_with_generate else logits

					if labels.shape[-1] < gen_kwargs["max_length"]:
									lowercase_       =					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_       ,							gen_kwargs['''max_length''']       )

					return (loss, logits, labels)




	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     Tuple:
					# If PAD token is not defined at least EOS token has to be defined
					lowercase_       =					self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

					if pad_token_id is None:
									raise ValueError(
									    '''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
									    f''' padded to `max_length`={max_length}'''       )

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




