

'''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


UpperCAmelCase							=					logging.get_logger(__name__)


class 					lowerCAmelCase       ( lowerCAmelCase__				):







       def __init__(							self     :	Dict  , __lowercase     :	Optional[Any] = None  , __lowercase     :	int = None  , __lowercase     :	Optional[Any] = None  , __lowercase     :	Tuple = None  , __lowercase     :	Optional[Any] = None  , __lowercase     :	Tuple = None  , __lowercase     :	Tuple = None  , __lowercase     :	List[str] = None  , __lowercase     :	Tuple = None  , __lowercase     :	Any = (None, None)  , __lowercase     :	Optional[Any] = None  , ):







            """simple docstring"""







            super().__init__(
                model=UpperCamelCase__  , args=UpperCamelCase__  , data_collator=UpperCamelCase__  , train_dataset=UpperCamelCase__  , eval_dataset=UpperCamelCase__  , tokenizer=UpperCamelCase__  , model_init=UpperCamelCase__  , compute_metrics=UpperCamelCase__  , callbacks=UpperCamelCase__  , optimizers=UpperCamelCase__  , preprocess_logits_for_metrics=UpperCamelCase__  , )

            # 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:
                 __lowercase     						=self.load_generation_config(self.args.generation_config )
                 __lowercase     						=gen_config







       @staticmethod
       def      snake_case				(							__lowercase     :	str ):







            """simple docstring"""







            if isinstance(UpperCamelCase__  , UpperCamelCase__ ):
                 return deepcopy(UpperCamelCase__ )

            # str or Path
            __lowercase     						=Path(UpperCamelCase__ ) if isinstance(UpperCamelCase__  , UpperCamelCase__ ) else gen_config_arg
            __lowercase     						=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():
                 __lowercase     						=pretrained_model_name.name
                 __lowercase     						=pretrained_model_name.parent
            # dir path
            elif pretrained_model_name.is_dir():
                 pass
            # model id or URL
            else:
                 __lowercase     						=gen_config_arg

            __lowercase     						=GenerationConfig.from_pretrained(UpperCamelCase__  , UpperCamelCase__ )
            return gen_config







       def      snake_case				(							self     :	Dict  , __lowercase     :	List[Any] = None  , __lowercase     :	List[str] = None  , __lowercase     :	str = "eval"  , **__lowercase     :	str  , ):







            """simple docstring"""







            __lowercase     						=gen_kwargs.copy()
            if gen_kwargs.get('max_length' ) is None and gen_kwargs.get('max_new_tokens' ) is None:
                 __lowercase     						=self.args.generation_max_length
            __lowercase     						=(
                gen_kwargs["num_beams"] if gen_kwargs.get('num_beams' ) is not None else self.args.generation_num_beams
            )
            __lowercase     						=gen_kwargs

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







       def      snake_case				(							self     :	Optional[Any]  , __lowercase     :	Any  , __lowercase     :	List[Any] = None  , __lowercase     :	Optional[Any] = "test"  , **__lowercase     :	Tuple  , ):







            """simple docstring"""







            __lowercase     						=gen_kwargs.copy()
            if gen_kwargs.get('max_length' ) is None and gen_kwargs.get('max_new_tokens' ) is None:
                 __lowercase     						=self.args.generation_max_length
            __lowercase     						=(
                gen_kwargs["num_beams"] if gen_kwargs.get('num_beams' ) is not None else self.args.generation_num_beams
            )
            __lowercase     						=gen_kwargs

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







       def      snake_case				(							self     :	Optional[Any]  , __lowercase     :	Optional[Any]  , __lowercase     :	int  , __lowercase     :	Tuple  , __lowercase     :	Dict = None  , **__lowercase     :	Any  , ):







            """simple docstring"""







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

            __lowercase     						="labels" in inputs
            __lowercase     						=self._prepare_inputs(UpperCamelCase__ )

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

            if len(UpperCamelCase__ ) == 0 and hasattr(self  , '_gen_kwargs' ):
                 __lowercase     						=self._gen_kwargs.copy()

            if gen_kwargs.get('max_length' ) is None and gen_kwargs.get('max_new_tokens' ) is None:
                 __lowercase     						=self.model.config.max_length
            __lowercase     						=(
                gen_kwargs["num_beams"] if gen_kwargs.get('num_beams' ) is not None else self.model.config.num_beams
            )
            __lowercase     						=True if is_deepspeed_zeroa_enabled() else False
            __lowercase     						=(
                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
            ):
                 __lowercase     						={k: v for k, v in inputs.items() if k != "decoder_input_ids"}
            __lowercase     						=self.model.generate(**UpperCamelCase__  , **UpperCamelCase__ )

            # 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:
                 __lowercase     						=False

            # Retrieves GenerationConfig from model.generation_config
            __lowercase     						=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:
                 __lowercase     						=self._pad_tensors_to_max_len(UpperCamelCase__  , gen_config.max_length )
            elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
                 __lowercase     						=self._pad_tensors_to_max_len(UpperCamelCase__  , gen_config.max_new_tokens + 1 )

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

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

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

            return loss, generated_tokens, labels







       def      snake_case				(							self     :	str  , __lowercase     :	Dict  , __lowercase     :	Any ):







            """simple docstring"""







            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
                 __lowercase     						=(
                     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:
                      __lowercase     						=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' )

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





'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	(DDIMScheduler,)
       lowerCAmelCase_				=	(("eta", 0.0), ("num_inference_steps", 5_0))







       def      snake_case				(							self     :	List[Any]  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						={
                'num_train_timesteps': 1000,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**__lowercase )
            return config







       def      snake_case				(							self     :	Optional[Any]  , **__lowercase     :	str ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(**__lowercase )
            __lowercase     						=scheduler_class(**__lowercase )

            __lowercase						,       __lowercase     						=10, 0.0

            __lowercase     						=self.dummy_model()
            __lowercase     						=self.dummy_sample_deter

            scheduler.set_timesteps(__lowercase )

            for t in scheduler.timesteps:
                 __lowercase     						=model(__lowercase  , __lowercase )
                 __lowercase     						=scheduler.step(__lowercase  , __lowercase  , __lowercase  , __lowercase ).prev_sample

            return sample







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for timesteps in [100, 500, 1000]:
                 self.check_over_configs(num_train_timesteps=__lowercase )







       def      snake_case				(							self     :	List[Any] ):







            """simple docstring"""







            for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__lowercase )

            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config(steps_offset=1 )
            __lowercase     						=scheduler_class(**__lowercase )
            scheduler.set_timesteps(5 )
            assert torch.equal(scheduler.timesteps  , torch.LongTensor([801, 601, 401, 201, 1] ) )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  , [0.0_0_2, 0.0_2, 0.2, 2] ):
                 self.check_over_configs(beta_start=__lowercase  , beta_end=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__lowercase )







       def      snake_case				(							self     :	str ):







            """simple docstring"""







            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=__lowercase )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=__lowercase )







       def      snake_case				(							self     :	int ):







            """simple docstring"""







            self.check_over_configs(thresholding=__lowercase )
            for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__lowercase  , prediction_type=__lowercase  , sample_max_value=__lowercase  , )







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            for t in [1, 10, 49]:
                 self.check_over_forward(time_step=__lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for t, num_inference_steps in zip([1, 10, 50]  , [10, 50, 500] ):
                 self.check_over_forward(time_step=__lowercase  , num_inference_steps=__lowercase )







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            for t, eta in zip([1, 10, 49]  , [0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=__lowercase  , eta=__lowercase )







       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            __lowercase     						=self.scheduler_classes[0]
            __lowercase     						=self.get_scheduler_config()
            __lowercase     						=scheduler_class(**__lowercase )

            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(420  , 400 ) - 0.1_4_7_7_1 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(980  , 960 ) - 0.3_2_4_6_0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0  , 0 ) - 0.0 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487  , 486 ) - 0.0_0_9_7_9 ) ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999  , 998 ) - 0.0_2 ) ) < 1E-5







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.full_loop()

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_7_2.0_0_6_7 ) < 1E-2
            assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1E-3







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(prediction_type='v_prediction' )

            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1E-2
            assert abs(result_mean.item() - 0.0_6_8_4 ) < 1E-3







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.8_2_9_5 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_5_1 ) < 1E-3







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            __lowercase     						=self.full_loop(set_alpha_to_one=__lowercase  , beta_start=0.0_1 )
            __lowercase     						=torch.sum(torch.abs(__lowercase ) )
            __lowercase     						=torch.mean(torch.abs(__lowercase ) )

            assert abs(result_sum.item() - 1_4_9.0_7_8_4 ) < 1E-2
            assert abs(result_mean.item() - 0.1_9_4_1 ) < 1E-3


