





"""simple docstring"""





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


lowercase__				:Optional[int]				   =       logging.get_logger(__name__)

lowercase__				:Optional[int]				   =       {
    "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 							snake_case							(			_UpperCAmelCase ):

      '''simple docstring'''



      def __init__(							self       :      str						,				__lowercase       :      Optional[Any]=None						,				__lowercase       :      Union[str, Any]=None						,				*__lowercase       :      List[Any]						,				**__lowercase       :      Union[str, Any] ):


        '''simple docstring'''




        super().__init__(*A_						,				**A_ )

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

        __UpperCAmelCase    :						Any       =						data_args
        __UpperCAmelCase    :						str       =						self.config.tgt_vocab_size if isinstance(self.config						,				A_ ) 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:
          __UpperCAmelCase    :						int       =						torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
        else:
          # dynamically import label_smoothed_nll_loss
          from utils import label_smoothed_nll_loss

          __UpperCAmelCase    :						Dict       =						label_smoothed_nll_loss

      def       A_					(							self       :      List[Any]						,				__lowercase       :      int ):


        '''simple docstring'''




        if self.optimizer is None:
          __UpperCAmelCase    :						Any       =						['''bias''', '''LayerNorm.weight''']
          __UpperCAmelCase    :						Union[str, Any]       =						[
              {
                  '''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,
              },
          ]
          __UpperCAmelCase    :						str       =						Adafactor if self.args.adafactor else AdamW
          if self.args.adafactor:
            __UpperCAmelCase    :						Any       =						Adafactor
            __UpperCAmelCase    :						Tuple       =						{'''scale_parameter''': False, '''relative_step''': False}
          else:
            __UpperCAmelCase    :						Optional[Any]       =						AdamW
            __UpperCAmelCase    :						List[str]       =						{
                '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
                '''eps''': self.args.adam_epsilon,
            }
          __UpperCAmelCase    :						Tuple       =						self.args.learning_rate
          if self.sharded_ddp:
            __UpperCAmelCase    :						Tuple       =						OSS(
                params=A_						,				optim=A_						,				**A_						,				)
          else:
            __UpperCAmelCase    :						int       =						optimizer_cls(A_						,				**A_ )

        if self.lr_scheduler is None:
          __UpperCAmelCase    :						Any       =						self._get_lr_scheduler(A_ )
        else:  # ignoring --lr_scheduler
          logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )

      def       A_					(							self       :      Any						,				__lowercase       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						arg_to_scheduler[self.args.lr_scheduler]
        if self.args.lr_scheduler == "constant":
          __UpperCAmelCase    :						Tuple       =						schedule_func(self.optimizer )
        elif self.args.lr_scheduler == "constant_w_warmup":
          __UpperCAmelCase    :						List[str]       =						schedule_func(self.optimizer						,				num_warmup_steps=self.args.warmup_steps )
        else:
          __UpperCAmelCase    :						Any       =						schedule_func(
              self.optimizer						,				num_warmup_steps=self.args.warmup_steps						,				num_training_steps=A_ )
        return scheduler

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        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       A_					(							self       :      List[Any]						,				__lowercase       :      str						,				__lowercase       :      str						,				__lowercase       :      List[str] ):


        '''simple docstring'''




        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
            __UpperCAmelCase    :						Tuple       =						model(**A_						,				use_cache=A_ )[0]
            __UpperCAmelCase    :						List[Any]       =						self.loss_fn(logits.view(-1						,				logits.shape[-1] )						,				labels.view(-1 ) )
          else:
            # compute usual loss via models
            __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[int]       =						model(**A_						,				labels=A_						,				use_cache=A_ )[:2]
        else:
          # compute label smoothed loss
          __UpperCAmelCase    :						Union[str, Any]       =						model(**A_						,				use_cache=A_ )[0]
          __UpperCAmelCase    :						Union[str, Any]       =						torch.nn.functional.log_softmax(A_						,				dim=-1 )
          __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[int]       =						self.loss_fn(A_						,				A_						,				self.args.label_smoothing						,				ignore_index=self.config.pad_token_id )
        return loss, logits

      def       A_					(							self       :      int						,				__lowercase       :      Tuple						,				__lowercase       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						inputs.pop('''labels''' )
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Dict       =						self._compute_loss(A_						,				A_						,				A_ )
        return loss

      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      nn.Module						,				__lowercase       :      Dict[str, Union[torch.Tensor, Any]]						,				__lowercase       :      bool						,				__lowercase       :      Optional[List[str]] = None						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self._prepare_inputs(A_ )

        __UpperCAmelCase    :						Dict       =						{
            '''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:
          __UpperCAmelCase    :						Dict       =						self.model.generate(
              inputs['''input_ids''']						,				attention_mask=inputs['''attention_mask''']						,				**A_						,				)
          # in case the batch is shorter than max length, the output should be padded
          if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
            __UpperCAmelCase    :						Optional[Any]       =						self._pad_tensors_to_max_len(A_						,				gen_kwargs['''max_length'''] )

        __UpperCAmelCase    :						List[Any]       =						inputs.pop('''labels''' )
        with torch.no_grad():
          # compute loss on predict data
          __UpperCAmelCase       ,     __UpperCAmelCase    :						Tuple       =						self._compute_loss(A_						,				A_						,				A_ )

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

        __UpperCAmelCase    :						List[str]       =						generated_tokens if self.args.predict_with_generate else logits

        if labels.shape[-1] < gen_kwargs["max_length"]:
          __UpperCAmelCase    :						Optional[Any]       =						self._pad_tensors_to_max_len(A_						,				gen_kwargs['''max_length'''] )

        return (loss, logits, labels)







      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      Any						,				__lowercase       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						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}''' )

        __UpperCAmelCase    :						Union[str, Any]       =						pad_token_id * torch.ones(
            (tensor.shape[0], max_length)						,				dtype=tensor.dtype						,				device=tensor.device )
        __UpperCAmelCase    :						str       =						tensor
        return padded_tensor






"""simple docstring"""





import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard




@slow
@require_flax
class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      str ):


        '''simple docstring'''




        super().tearDown()
        gc.collect()

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						List[Any]       =						FlaxStableDiffusionPipeline.from_pretrained(
            '''stabilityai/stable-diffusion-2'''						,				revision='''bf16'''						,				dtype=jnp.bfloataa						,				)

        __UpperCAmelCase    :						Dict       =						'''A painting of a squirrel eating a burger'''
        __UpperCAmelCase    :						Any       =						jax.device_count()
        __UpperCAmelCase    :						Optional[int]       =						num_samples * [prompt]
        __UpperCAmelCase    :						Tuple       =						sd_pipe.prepare_inputs(__lowercase )

        __UpperCAmelCase    :						List[Any]       =						replicate(__lowercase )
        __UpperCAmelCase    :						Optional[Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						str       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						Dict       =						jax.random.split(__lowercase						,				jax.device_count() )

        __UpperCAmelCase    :						Tuple       =						sd_pipe(__lowercase						,				__lowercase						,				__lowercase						,				num_inference_steps=25						,				jit=__lowercase )[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        __UpperCAmelCase    :						Any       =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
        __UpperCAmelCase    :						Union[str, Any]       =						images[0, 253:256, 253:256, -1]

        __UpperCAmelCase    :						str       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						List[Any]       =						jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2







      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						'''stabilityai/stable-diffusion-2'''
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						FlaxDPMSolverMultistepScheduler.from_pretrained(__lowercase						,				subfolder='''scheduler''' )
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[int]       =						FlaxStableDiffusionPipeline.from_pretrained(
            __lowercase						,				scheduler=__lowercase						,				revision='''bf16'''						,				dtype=jnp.bfloataa						,				)
        __UpperCAmelCase    :						Dict       =						scheduler_params

        __UpperCAmelCase    :						int       =						'''A painting of a squirrel eating a burger'''
        __UpperCAmelCase    :						List[Any]       =						jax.device_count()
        __UpperCAmelCase    :						Optional[int]       =						num_samples * [prompt]
        __UpperCAmelCase    :						Tuple       =						sd_pipe.prepare_inputs(__lowercase )

        __UpperCAmelCase    :						Optional[int]       =						replicate(__lowercase )
        __UpperCAmelCase    :						List[Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						List[Any]       =						jax.random.split(__lowercase						,				jax.device_count() )

        __UpperCAmelCase    :						List[Any]       =						sd_pipe(__lowercase						,				__lowercase						,				__lowercase						,				num_inference_steps=25						,				jit=__lowercase )[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        __UpperCAmelCase    :						Tuple       =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
        __UpperCAmelCase    :						Any       =						images[0, 253:256, 253:256, -1]

        __UpperCAmelCase    :						List[str]       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						Any       =						jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2