

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_									=   logging.get_logger(__name__)

lowercase_									=   {
    '''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  __lowerCAmelCase     (					snake_case__							):




  def __init__(				self							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   *lowerCAmelCase							,   **lowerCAmelCase					)  -> int:
        '''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__}'''
              )
              _lowercase	       =self.model.config
        else:
              _lowercase	       =config

        _lowercase	       =data_args
        _lowercase	       =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:
              _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   A__     (				self							,   lowerCAmelCase					)  -> Any:
        '''simple docstring'''






        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=_A							,   optim=_A							,   **_A							,   )
              else:
                    _lowercase	       =optimizer_cls(_A							,   **_A					)

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




  def   A__     (				self							,   lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






        _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=_A					)
        return scheduler




  def   A__     (				self					)  -> Tuple:
        '''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							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Dict:
        '''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
                    _lowercase	       =model(**_A							,   use_cache=_A					)[0]
                    _lowercase	       =self.loss_fn(logits.view(-1							,   logits.shape[-1]					)							,   labels.view(-1					)					)
              else:
                    # compute usual loss via models
                    _lowercase	       =model(**_A							,   labels=_A							,   use_cache=_A					)[:2]
        else:
              # compute label smoothed loss
              _lowercase	       =model(**_A							,   use_cache=_A					)[0]
              _lowercase	       =torch.nn.functional.log_softmax(_A							,   dim=-1					)
              _lowercase	       =self.loss_fn(_A							,   _A							,   self.args.label_smoothing							,   ignore_index=self.config.pad_token_id					)
        return loss, logits




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =inputs.pop('labels'					)
        _lowercase	       =self._compute_loss(_A							,   _A							,   _A					)
        return loss




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = None							,   )  -> Dict:
        '''simple docstring'''






        _lowercase	       =self._prepare_inputs(_A					)

        _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']							,   **_A							,   )
              # 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(_A							,   gen_kwargs['max_length']					)

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

        _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(_A							,   gen_kwargs['max_length']					)

        return (loss, logits, labels)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> List[str]:
        '''simple docstring'''






        _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









import colorsys

from PIL import Image  # type: ignore







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      float:
					__A	:						List[str]       =				x
					__A	:						str       =				y
					for step in range(a  ):  # noqa: B007
										__A	:						Union[str, Any]       =				a * a - b * b + x
										__A	:						Optional[int]       =				2 * a * b + y
										__A	:						List[str]       =				a_new

										# divergence happens for all complex number with an absolute value
										# greater than 4
										if a * a + b * b > 4:
															break
					return step / (max_step - 1)







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      tuple:
					if distance == 1:
										return (0, 0, 0)
					else:
										return (2_55, 2_55, 2_55)







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      tuple:
					if distance == 1:
										return (0, 0, 0)
					else:
										return tuple(round(i * 2_55  ) for i in colorsys.hsv_to_rgb(a					,						1					,						1  )  )







def  _SCREAMING_SNAKE_CASE				(   a = 8_00					,						a = 6_00					,						a = -0.6					,						a = 0					,						a = 3.2					,						a = 50					,						a = True					,						)      ->      Image.Image:
					__A	:						str       =				Image.new('RGB'					,						(image_width, image_height)  )
					__A	:						Dict       =				img.load()

					# loop through the image-coordinates
					for image_x in range(a  ):
										for image_y in range(a  ):
															# determine the figure-coordinates based on the image-coordinates
															__A	:						Dict       =				figure_width / image_width * image_height
															__A	:						Union[str, Any]       =				figure_center_x + (image_x / image_width - 0.5) * figure_width
															__A	:						Optional[Any]       =				figure_center_y + (image_y / image_height - 0.5) * figure_height

															__A	:						Union[str, Any]       =				get_distance(a					,						a					,						a  )

															# color the corresponding pixel based on the selected coloring-function
															if use_distance_color_coding:
																				__A	:						Optional[Any]       =				get_color_coded_rgb(a  )
															else:
																				__A	:						Dict       =				get_black_and_white_rgb(a  )

					return img


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# colored version, full figure
				UpperCAmelCase						:   str			  =     get_image()

				# uncomment for colored version, different section, zoomed in
				# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
				# figure_width = 0.8)

				# uncomment for black and white version, full figure
				# img = get_image(use_distance_color_coding = False)

				# uncomment to save the image
				# img.save("mandelbrot.png")

				img.show()




