
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


A__       :   int   						=					logging.get_logger(__name__)





class 						lowercase       (  __UpperCamelCase					):
     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 ,	):
        """simple docstring"""







        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:
           lowerCAmelCase__ :    Dict				      =			self.load_generation_config(self.args.generation_config    )
           lowerCAmelCase__ :    List[str]				      =			gen_config
     @staticmethod
     def lowercase_			(		SCREAMING_SNAKE_CASE__    ):
        """simple docstring"""







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

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

        lowerCAmelCase__ :    Optional[int]				      =			GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
        return gen_config
     def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ = None ,	SCREAMING_SNAKE_CASE__ = None ,	SCREAMING_SNAKE_CASE__ = "eval" ,	**SCREAMING_SNAKE_CASE__ ,	):
        """simple docstring"""







        lowerCAmelCase__ :    Optional[Any]				      =			gen_kwargs.copy()
        if gen_kwargs.get('''max_length'''    ) is None and gen_kwargs.get('''max_new_tokens'''    ) is None:
           lowerCAmelCase__ :    Dict				      =			self.args.generation_max_length
        lowerCAmelCase__ :    int				      =			(
            gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''    ) is not None else self.args.generation_num_beams
        )
        lowerCAmelCase__ :    int				      =			gen_kwargs

        return super().evaluate(SCREAMING_SNAKE_CASE__ ,	ignore_keys=SCREAMING_SNAKE_CASE__ ,	metric_key_prefix=SCREAMING_SNAKE_CASE__    )
     def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ = None ,	SCREAMING_SNAKE_CASE__ = "test" ,	**SCREAMING_SNAKE_CASE__ ,	):
        """simple docstring"""







        lowerCAmelCase__ :    List[Any]				      =			gen_kwargs.copy()
        if gen_kwargs.get('''max_length'''    ) is None and gen_kwargs.get('''max_new_tokens'''    ) is None:
           lowerCAmelCase__ :    str				      =			self.args.generation_max_length
        lowerCAmelCase__ :    Union[str, Any]				      =			(
            gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''    ) is not None else self.args.generation_num_beams
        )
        lowerCAmelCase__ :    List[Any]				      =			gen_kwargs

        return super().predict(SCREAMING_SNAKE_CASE__ ,	ignore_keys=SCREAMING_SNAKE_CASE__ ,	metric_key_prefix=SCREAMING_SNAKE_CASE__    )
     def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ = None ,	**SCREAMING_SNAKE_CASE__ ,	):
        """simple docstring"""







        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__    )

        lowerCAmelCase__ :    Tuple				      =			'''labels''' in inputs
        lowerCAmelCase__ :    Dict				      =			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'''    ):
           lowerCAmelCase__ :    List[str]				      =			self._gen_kwargs.copy()

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

        # Retrieves GenerationConfig from model.generation_config
        lowerCAmelCase__ :    Dict				      =			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:
           lowerCAmelCase__ :    Union[str, Any]				      =			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:
           lowerCAmelCase__ :    Tuple				      =			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():
                 lowerCAmelCase__ :    Optional[Any]				      =			model(**SCREAMING_SNAKE_CASE__    )
              if self.label_smoother is not None:
                 lowerCAmelCase__ :    int				      =			self.label_smoother(SCREAMING_SNAKE_CASE__ ,	inputs['''labels''']    ).mean().detach()
              else:
                 lowerCAmelCase__ :    int				      =			(outputs['''loss'''] if isinstance(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ) else outputs[0]).mean().detach()
           else:
              lowerCAmelCase__ :    List[Any]				      =			None

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

        if has_labels:
           lowerCAmelCase__ :    Dict				      =			inputs['''labels''']
           if labels.shape[-1] < gen_config.max_length:
              lowerCAmelCase__ :    Tuple				      =			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:
              lowerCAmelCase__ :    List[str]				      =			self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE__ ,	gen_config.max_new_tokens + 1    )
        else:
           lowerCAmelCase__ :    Optional[int]				      =			None

        return loss, generated_tokens, labels
     def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
        """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
           lowerCAmelCase__ :    Union[str, 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:
              lowerCAmelCase__ :    Any				      =			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'''    )

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A__       :   Union[str, Any]   						=					{"""configuration_cvt""": ["""CVT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CvtConfig"""]}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   Union[str, Any]   						=					[
							    """CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CvtForImageClassification""",
							    """CvtModel""",
							    """CvtPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__       :   str   						=					[
							    """TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFCvtForImageClassification""",
							    """TFCvtModel""",
							    """TFCvtPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_cvt import (
														    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CvtForImageClassification,
														    CvtModel,
														    CvtPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_cvt import (
														    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCvtForImageClassification,
														    TFCvtModel,
														    TFCvtPreTrainedModel,
														)


else:
							import sys

							A__       :   Optional[Any]   						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



