






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


__snake_case							 =  logging.get_logger(__name__)







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      def __init__(    self   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = (None, None)   ,    SCREAMING_SNAKE_CASE_ = None   ,    )    ->				Optional[int]:
          super().__init__(
              model=SCREAMING_SNAKE_CASE_   ,    args=SCREAMING_SNAKE_CASE_   ,    data_collator=SCREAMING_SNAKE_CASE_   ,    train_dataset=SCREAMING_SNAKE_CASE_   ,    eval_dataset=SCREAMING_SNAKE_CASE_   ,    tokenizer=SCREAMING_SNAKE_CASE_   ,    model_init=SCREAMING_SNAKE_CASE_   ,    compute_metrics=SCREAMING_SNAKE_CASE_   ,    callbacks=SCREAMING_SNAKE_CASE_   ,    optimizers=SCREAMING_SNAKE_CASE_   ,    preprocess_logits_for_metrics=SCREAMING_SNAKE_CASE_   ,    )

          # 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	:Optional[Any]     							=       self.load_generation_config(self.args.generation_config			)
              UpperCamelCase	:int     							=       gen_config
      @staticmethod
      def    UpperCAmelCase						(    SCREAMING_SNAKE_CASE_			)    ->				GenerationConfig:

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

          # str or Path
          UpperCamelCase	:int     							=       Path(SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			) else gen_config_arg
          UpperCamelCase	:List[str]     							=       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	:Dict     							=       pretrained_model_name.name
              UpperCamelCase	:Union[str, Any]     							=       pretrained_model_name.parent
          # dir path
          elif pretrained_model_name.is_dir():
              pass
          # model id or URL
          else:
              UpperCamelCase	:Tuple     							=       gen_config_arg

          UpperCamelCase	:List[str]     							=       GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
          return gen_config
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = "eval"   ,    **SCREAMING_SNAKE_CASE_   ,    )    ->				Dict[str, float]:
          UpperCamelCase	:List[str]     							=       gen_kwargs.copy()
          if gen_kwargs.get('''max_length'''			) is None and gen_kwargs.get('''max_new_tokens'''			) is None:
              UpperCamelCase	:Optional[Any]     							=       self.args.generation_max_length
          UpperCamelCase	:Dict     							=       (
              gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''			) is not None else self.args.generation_num_beams
          )
          UpperCamelCase	:Union[str, Any]     							=       gen_kwargs

          return super().evaluate(SCREAMING_SNAKE_CASE_   ,    ignore_keys=SCREAMING_SNAKE_CASE_   ,    metric_key_prefix=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = "test"   ,    **SCREAMING_SNAKE_CASE_   ,    )    ->				"PredictionOutput":
          UpperCamelCase	:Tuple     							=       gen_kwargs.copy()
          if gen_kwargs.get('''max_length'''			) is None and gen_kwargs.get('''max_new_tokens'''			) is None:
              UpperCamelCase	:Dict     							=       self.args.generation_max_length
          UpperCamelCase	:Union[str, Any]     							=       (
              gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''			) is not None else self.args.generation_num_beams
          )
          UpperCamelCase	:Union[str, Any]     							=       gen_kwargs

          return super().predict(SCREAMING_SNAKE_CASE_   ,    ignore_keys=SCREAMING_SNAKE_CASE_   ,    metric_key_prefix=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_ = None   ,    **SCREAMING_SNAKE_CASE_   ,    )    ->				Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

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

          UpperCamelCase	:int     							=       '''labels''' in inputs
          UpperCamelCase	:Optional[Any]     							=       self._prepare_inputs(SCREAMING_SNAKE_CASE_			)

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

          if len(SCREAMING_SNAKE_CASE_			) == 0 and hasattr(self   ,    '''_gen_kwargs'''			):
              UpperCamelCase	:Optional[Any]     							=       self._gen_kwargs.copy()

          if gen_kwargs.get('''max_length'''			) is None and gen_kwargs.get('''max_new_tokens'''			) is None:
              UpperCamelCase	:Optional[Any]     							=       self.model.config.max_length
          UpperCamelCase	:Union[str, Any]     							=       (
              gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''			) is not None else self.model.config.num_beams
          )
          UpperCamelCase	:List[str]     							=       True if is_deepspeed_zeroa_enabled() else False
          UpperCamelCase	:Any     							=       (
              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	:Dict     							=       {k: v for k, v in inputs.items() if k != '''decoder_input_ids'''}
          UpperCamelCase	:List[str]     							=       self.model.generate(**SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_			)

          # 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	:Optional[int]     							=       False

          # Retrieves GenerationConfig from model.generation_config
          UpperCamelCase	:List[Any]     							=       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	:Optional[int]     							=       self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_   ,    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	:List[str]     							=       self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_   ,    gen_config.max_new_tokens + 1			)

          with torch.no_grad():
              if has_labels:
                  with self.compute_loss_context_manager():
                      UpperCamelCase	:List[str]     							=       model(**SCREAMING_SNAKE_CASE_			)
                  if self.label_smoother is not None:
                      UpperCamelCase	:int     							=       self.label_smoother(SCREAMING_SNAKE_CASE_   ,    inputs['''labels''']			).mean().detach()
                  else:
                      UpperCamelCase	:int     							=       (outputs['''loss'''] if isinstance(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			) else outputs[0]).mean().detach()
              else:
                  UpperCamelCase	:Tuple     							=       None

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

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

          return loss, generated_tokens, labels




      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				Tuple:
          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	:Any     							=       (
                  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	:Optional[int]     							=       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	:Tuple     							=       pad_token_id * torch.ones(
              (tensor.shape[0], max_length)   ,    dtype=tensor.dtype   ,    device=tensor.device			)
          UpperCamelCase	:List[Any]     							=       tensor
          return padded_tensor













import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

__snake_case							 =  0.2_5 + 0.0j
__snake_case							 =  -0.4 + 0.6j
__snake_case							 =  -0.1 + 0.651j
__snake_case							 =  -2.0
__snake_case							 =  56
__snake_case							 =  2.0
__snake_case							 =  6_66


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return numpy.exp(SCREAMING_SNAKE_CASE__	) + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return z_values * z_values + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:List[Any]     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Any     							=       x.reshape((nb_pixels, 1)	)
    UpperCamelCase	:Tuple     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:int     							=       y.reshape((1, nb_pixels)	)
    return x + 1.0J * y


def   _A       (    SCREAMING_SNAKE_CASE__  :     Callable[[Any, numpy.ndarray], numpy.ndarray]					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  SCREAMING_SNAKE_CASE__  :     float | None = None					,  ):
    UpperCamelCase	:Dict     							=       z_a.astype('''complex64'''	)
    for _ in range(SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:int     							=       eval_function(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
        if infinity is not None:
            numpy.nan_to_num(SCREAMING_SNAKE_CASE__					,  copy=SCREAMING_SNAKE_CASE__					,  nan=SCREAMING_SNAKE_CASE__	)
            UpperCamelCase	:Tuple     							=       infinity
    return z_n


def   _A       (    SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  ):
    UpperCamelCase	:Any     							=       (abs(SCREAMING_SNAKE_CASE__	)).transpose()
    UpperCamelCase	:Any     							=       abs_z_final[::-1, :]
    pyplot.matshow(abs_z_final < escape_radius	)
    pyplot.title(F'''Julia set of ${function_label}$, $c={function_params}$'''	)
    pyplot.show()


def   _A       (    ):
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in multiply'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''invalid value encountered in multiply'''					,  )
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in absolute'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in exp'''	)


if __name__ == "__main__":
 __snake_case							 =  prepare_grid(window_size, nb_pixels)

 ignore_overflow_warnings()  # See file header for explanations

 __snake_case							 =  24
 __snake_case							 =  2 * abs(c_cauliflower) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_cauliflower,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_cauliflower, escape_radius, z_final)

 __snake_case							 =  64
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  1_61
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  12
 __snake_case							 =  1_0_0_0_0.0
 __snake_case							 =  iterate_function(
     eval_exponential,
     c_exponential,
     nb_iterations,
     z_a + 2,
     infinity=1.0E1_0,
 )
 show_results("""e^z+c""", c_exponential, escape_radius, z_final)





