'''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


__a:			Union[str, Any]	       =       logging.get_logger(__name__)

__a:			str	       =       {
    """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       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  List[Any]:
        super().__init__(*__lowerCAmelCase     ,       **__lowerCAmelCase			)

        if config is None:
         assert isinstance(self.model     ,       __lowerCAmelCase			), (
             "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
             F""" {self.model.__class__}"""
         )
         lowercase__    :   int						= self.model.config
        else:
         lowercase__    :   Tuple						= config

        lowercase__    :   Tuple						= data_args
        lowercase__    :   str						= self.config.tgt_vocab_size if isinstance(self.config     ,       __lowerCAmelCase			) 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__    :   Optional[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

         lowercase__    :   int						= label_smoothed_nll_loss

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Any:
        if self.optimizer is None:
         lowercase__    :   Any						= ['''bias''', '''LayerNorm.weight''']
         lowercase__    :   Optional[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,
             },
         ]
         lowercase__    :   List[str]						= Adafactor if self.args.adafactor else AdamW
         if self.args.adafactor:
          lowercase__    :   Any						= Adafactor
          lowercase__    :   Optional[Any]						= {'''scale_parameter''': False, '''relative_step''': False}
         else:
          lowercase__    :   Optional[int]						= AdamW
          lowercase__    :   Dict						= {
              '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
              '''eps''': self.args.adam_epsilon,
          }
         lowercase__    :   Optional[int]						= self.args.learning_rate
         if self.sharded_ddp:
          lowercase__    :   str						= OSS(
              params=__lowerCAmelCase     ,       optim=__lowerCAmelCase     ,       **__lowerCAmelCase     ,       )
         else:
          lowercase__    :   List[str]						= optimizer_cls(__lowerCAmelCase     ,       **__lowerCAmelCase			)

        if self.lr_scheduler is None:
         lowercase__    :   Optional[int]						= self._get_lr_scheduler(__lowerCAmelCase			)
        else:  # ignoring --lr_scheduler
         logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[int]:
        lowercase__    :   List[Any]						= arg_to_scheduler[self.args.lr_scheduler]
        if self.args.lr_scheduler == "constant":
         lowercase__    :   str						= schedule_func(self.optimizer			)
        elif self.args.lr_scheduler == "constant_w_warmup":
         lowercase__    :   Tuple						= schedule_func(self.optimizer     ,       num_warmup_steps=self.args.warmup_steps			)
        else:
         lowercase__    :   List[Any]						= schedule_func(
             self.optimizer     ,       num_warmup_steps=self.args.warmup_steps     ,       num_training_steps=__lowerCAmelCase			)
        return scheduler

       def _lowerCAmelCase(						self			)  ->  Optional[torch.utils.data.Sampler]:
        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 _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Any:
        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__    :   int						= model(**__lowerCAmelCase     ,       use_cache=__lowerCAmelCase			)[0]
          lowercase__    :   Optional[Any]						= self.loss_fn(logits.view(-1     ,       logits.shape[-1]			)     ,       labels.view(-1			)			)
         else:
          # compute usual loss via models
          lowercase__			,							lowercase__    :   Optional[int]						= model(**__lowerCAmelCase     ,       labels=__lowerCAmelCase     ,       use_cache=__lowerCAmelCase			)[:2]
        else:
         # compute label smoothed loss
         lowercase__    :   List[str]						= model(**__lowerCAmelCase     ,       use_cache=__lowerCAmelCase			)[0]
         lowercase__    :   Union[str, Any]						= torch.nn.functional.log_softmax(__lowerCAmelCase     ,       dim=-1			)
         lowercase__			,							lowercase__    :   Any						= self.loss_fn(__lowerCAmelCase     ,       __lowerCAmelCase     ,       self.args.label_smoothing     ,       ignore_index=self.config.pad_token_id			)
        return loss, logits

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   Optional[int]						= inputs.pop('''labels'''			)
        lowercase__			,							lowercase__    :   Optional[int]						= self._compute_loss(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        return loss

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       )  ->  Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
        lowercase__    :   Optional[int]						= self._prepare_inputs(__lowerCAmelCase			)

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

        lowercase__    :   Tuple						= inputs.pop('''labels'''			)
        with torch.no_grad():
         # compute loss on predict data
         lowercase__			,							lowercase__    :   Any						= self._compute_loss(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)

        lowercase__    :   str						= loss.mean().detach()
        if self.args.prediction_loss_only:
         return (loss, None, None)

        lowercase__    :   Optional[Any]						= generated_tokens if self.args.predict_with_generate else logits

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

        return (loss, logits, labels)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        # If PAD token is not defined at least EOS token has to be defined
        lowercase__    :   Optional[Any]						= 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__    :   int						= pad_token_id * torch.ones(
            (tensor.shape[0], max_length)     ,       dtype=tensor.dtype     ,       device=tensor.device			)
        lowercase__    :   Optional[int]						= tensor
        return padded_tensor


'''simple docstring'''

from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
  from ..tf_utils import stable_softmax

__a:			Optional[Any]	       =       logging.get_logger(__name__)



@add_end_docstrings(a__ )
class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       **__lowerCAmelCase			)  ->  int:
        super().__init__(**__lowerCAmelCase			)

        requires_backends(self     ,       '''vision'''			)
        self.check_model_type(
            TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == '''tf'''
            else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING			)

       def __call__(						self     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  List[str]:
        return super().__call__(__lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       **__lowerCAmelCase			)  ->  Optional[Any]:
        lowercase__    :   str						= {}
        if "candidate_labels" in kwargs:
         lowercase__    :   str						= kwargs['''candidate_labels''']
        if "hypothesis_template" in kwargs:
         lowercase__    :   str						= kwargs['''hypothesis_template''']

        return preprocess_params, {}, {}

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase="This is a photo of {}."			)  ->  Any:
        lowercase__    :   Union[str, Any]						= load_image(__lowerCAmelCase			)
        lowercase__    :   List[str]						= self.image_processor(images=[image]     ,       return_tensors=self.framework			)
        lowercase__    :   Union[str, Any]						= candidate_labels
        lowercase__    :   int						= [hypothesis_template.format(__lowerCAmelCase			) for x in candidate_labels]
        lowercase__    :   Any						= self.tokenizer(__lowerCAmelCase     ,       return_tensors=self.framework     ,       padding=__lowerCAmelCase			)
        lowercase__    :   Any						= [text_inputs]
        return inputs

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        lowercase__    :   Any						= model_inputs.pop('''candidate_labels'''			)
        lowercase__    :   int						= model_inputs.pop('''text_inputs'''			)
        if isinstance(text_inputs[0]     ,       __lowerCAmelCase			):
         lowercase__    :   Union[str, Any]						= text_inputs[0]
        else:
         # Batching case.
         lowercase__    :   Optional[Any]						= text_inputs[0][0]

        lowercase__    :   Any						= self.model(**__lowerCAmelCase     ,       **__lowerCAmelCase			)

        lowercase__    :   Any						= {
            '''candidate_labels''': candidate_labels,
            '''logits''': outputs.logits_per_image,
        }
        return model_outputs


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[str]:
        lowercase__    :   Union[str, Any]						= model_outputs.pop('''candidate_labels'''			)
        lowercase__    :   Optional[int]						= model_outputs['''logits'''][0]
        if self.framework == "pt":
         lowercase__    :   Optional[int]						= logits.softmax(dim=-1			).squeeze(-1			)
         lowercase__    :   Any						= probs.tolist()
         if not isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			):
          lowercase__    :   Dict						= [scores]
        elif self.framework == "tf":
         lowercase__    :   List[Any]						= stable_softmax(__lowerCAmelCase     ,       axis=-1			)
         lowercase__    :   str						= probs.numpy().tolist()
        else:
         raise ValueError(F"""Unsupported framework: {self.framework}"""			)

        lowercase__    :   Optional[int]						= [
            {'''score''': score, '''label''': candidate_label}
            for score, candidate_label in sorted(zip(__lowerCAmelCase     ,       __lowerCAmelCase			)     ,       key=lambda __lowerCAmelCase			: -x[0]			)
        ]
        return result

