




'''simple docstring'''



import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    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.utils.versions import require_version


lowerCamelCase_	 =		logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowerCamelCase_	 =		{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowerCamelCase_	 =		{
    '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,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowerCamelCase_	 =		sorted(arg_to_scheduler.keys())
lowerCamelCase_	 =		'{' + ', '.join(arg_to_scheduler_choices) + '}'







class 	lowercase_					(				pl.LightningModule     ):


   """simple docstring"""

   def __init__(					self      :			Tuple						,   __lowerCamelCase      :			argparse.Namespace						,   __lowerCamelCase      :			Optional[int]=None						,   __lowerCamelCase      :			Any="base"						,   __lowerCamelCase      :			Dict=None						,   __lowerCamelCase      :			int=None						,   __lowerCamelCase      :			Any=None						,   **__lowerCamelCase      :			Any						,   ):

        """simple docstring"""
        super().__init__()
        # TODO: move to self.save_hyperparameters()
        # self.save_hyperparameters()
        # can also expand arguments into trainer signature for easier reading

        self.save_hyperparameters(__lowerCamelCase					)
        _SCREAMING_SNAKE_CASE =   0
        _SCREAMING_SNAKE_CASE =   Path(self.hparams.output_dir					)
        _SCREAMING_SNAKE_CASE =   self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
             _SCREAMING_SNAKE_CASE =   AutoConfig.from_pretrained(
                 self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path						,   **({"num_labels": num_labels} if num_labels is not None else {})						,   cache_dir=__lowerCamelCase						,   **__lowerCamelCase						,   )
        else:
             _SCREAMING_SNAKE_CASE =   config

        _SCREAMING_SNAKE_CASE =   ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
        for p in extra_model_params:
             if getattr(self.hparams						,   __lowerCamelCase						,   __lowerCamelCase					):
                  assert hasattr(self.config						,   __lowerCamelCase					), F"""model config doesn't have a `{p}` attribute"""
                  setattr(self.config						,   __lowerCamelCase						,   getattr(self.hparams						,   __lowerCamelCase					)					)

        if tokenizer is None:
             _SCREAMING_SNAKE_CASE =   AutoTokenizer.from_pretrained(
                 self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path						,   cache_dir=__lowerCamelCase						,   )
        else:
             _SCREAMING_SNAKE_CASE =   tokenizer
        _SCREAMING_SNAKE_CASE =   MODEL_MODES[mode]
        if model is None:
             _SCREAMING_SNAKE_CASE =   self.model_type.from_pretrained(
                 self.hparams.model_name_or_path						,   from_tf=bool(".ckpt" in self.hparams.model_name_or_path					)						,   config=self.config						,   cache_dir=__lowerCamelCase						,   )
        else:
             _SCREAMING_SNAKE_CASE =   model



   def 							lowerCAmelCase_    (					self      :			Tuple						,   *__lowerCamelCase      :			Dict						,   **__lowerCamelCase      :			Optional[int]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.model_type.from_pretrained(*__lowerCamelCase						,   **__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   arg_to_scheduler[self.hparams.lr_scheduler]
        _SCREAMING_SNAKE_CASE =   get_schedule_func(
            self.opt						,   num_warmup_steps=self.hparams.warmup_steps						,   num_training_steps=self.total_steps()					)
        _SCREAMING_SNAKE_CASE =   {"scheduler": scheduler, "interval": "step", "frequency": 1}
        return scheduler



   def 							lowerCAmelCase_    (					self      :			Tuple					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.model
        _SCREAMING_SNAKE_CASE =   ["bias", "LayerNorm.weight"]
        _SCREAMING_SNAKE_CASE =   [
            {
                "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay					)],
                "weight_decay": self.hparams.weight_decay,
            },
            {
                "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay					)],
                "weight_decay": 0.0,
            },
        ]
        if self.hparams.adafactor:
             _SCREAMING_SNAKE_CASE =   Adafactor(
                 __lowerCamelCase						,   lr=self.hparams.learning_rate						,   scale_parameter=__lowerCamelCase						,   relative_step=__lowerCamelCase					)

        else:
             _SCREAMING_SNAKE_CASE =   AdamW(
                 __lowerCamelCase						,   lr=self.hparams.learning_rate						,   eps=self.hparams.adam_epsilon					)
        _SCREAMING_SNAKE_CASE =   optimizer

        _SCREAMING_SNAKE_CASE =   self.get_lr_scheduler()

        return [optimizer], [scheduler]



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			str					):

        """simple docstring"""
        return self.validation_step(__lowerCamelCase						,   __lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   __lowerCamelCase      :			Optional[int]					):

        """simple docstring"""
        return self.validation_end(__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			List[str]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   max(1						,   self.hparams.gpus					)  # TODO: consider num_tpu_cores
        _SCREAMING_SNAKE_CASE =   self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



   def 							lowerCAmelCase_    (					self      :			Tuple						,   __lowerCamelCase      :			Optional[int]					):

        """simple docstring"""
        if stage == "test":
             _SCREAMING_SNAKE_CASE =   len(self.test_dataloader().dataset					)
        else:
             _SCREAMING_SNAKE_CASE =   self.get_dataloader("train"						,   self.hparams.train_batch_size						,   shuffle=__lowerCamelCase					)
             _SCREAMING_SNAKE_CASE =   len(self.train_dataloader().dataset					)



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			bool = False					):

        """simple docstring"""
        raise NotImplementedError("You must implement this for your task"					)



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

        """simple docstring"""
        return self.train_loader



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

        """simple docstring"""
        return self.get_dataloader("dev"						,   self.hparams.eval_batch_size						,   shuffle=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int					):

        """simple docstring"""
        return self.get_dataloader("test"						,   self.hparams.eval_batch_size						,   shuffle=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			str					):

        """simple docstring"""
        return os.path.join(
            self.hparams.data_dir						,   "cached_{}_{}_{}".format(
                __lowerCamelCase						,   list(filter(__lowerCamelCase						,   self.hparams.model_name_or_path.split("/"					)					)					).pop()						,   str(self.hparams.max_seq_length					)						,   )						,   )



   @pl.utilities.rank_zero_only
   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			Dict[str, Any]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.output_dir.joinpath("best_tfmr"					)
        _SCREAMING_SNAKE_CASE =   self.step_count
        self.model.save_pretrained(__lowerCamelCase					)
        self.tokenizer.save_pretrained(__lowerCamelCase					)



   @staticmethod
   def 							lowerCAmelCase_    (					__lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Tuple					):

        """simple docstring"""
        parser.add_argument(
            "--model_name_or_path"						,   default=__lowerCamelCase						,   type=__lowerCamelCase						,   required=__lowerCamelCase						,   help="Path to pretrained model or model identifier from huggingface.co/models"						,   )
        parser.add_argument(
            "--config_name"						,   default=""						,   type=__lowerCamelCase						,   help="Pretrained config name or path if not the same as model_name"					)
        parser.add_argument(
            "--tokenizer_name"						,   default=__lowerCamelCase						,   type=__lowerCamelCase						,   help="Pretrained tokenizer name or path if not the same as model_name"						,   )
        parser.add_argument(
            "--cache_dir"						,   default=""						,   type=__lowerCamelCase						,   help="Where do you want to store the pre-trained models downloaded from huggingface.co"						,   )
        parser.add_argument(
            "--encoder_layerdrop"						,   type=__lowerCamelCase						,   help="Encoder layer dropout probability (Optional). Goes into model.config"						,   )
        parser.add_argument(
            "--decoder_layerdrop"						,   type=__lowerCamelCase						,   help="Decoder layer dropout probability (Optional). Goes into model.config"						,   )
        parser.add_argument(
            "--dropout"						,   type=__lowerCamelCase						,   help="Dropout probability (Optional). Goes into model.config"						,   )
        parser.add_argument(
            "--attention_dropout"						,   type=__lowerCamelCase						,   help="Attention dropout probability (Optional). Goes into model.config"						,   )
        parser.add_argument("--learning_rate"						,   default=5e-5						,   type=__lowerCamelCase						,   help="The initial learning rate for Adam."					)
        parser.add_argument(
            "--lr_scheduler"						,   default="linear"						,   choices=__lowerCamelCase						,   metavar=__lowerCamelCase						,   type=__lowerCamelCase						,   help="Learning rate scheduler"						,   )
        parser.add_argument("--weight_decay"						,   default=0.0						,   type=__lowerCamelCase						,   help="Weight decay if we apply some."					)
        parser.add_argument("--adam_epsilon"						,   default=1e-8						,   type=__lowerCamelCase						,   help="Epsilon for Adam optimizer."					)
        parser.add_argument("--warmup_steps"						,   default=0						,   type=__lowerCamelCase						,   help="Linear warmup over warmup_steps."					)
        parser.add_argument("--num_workers"						,   default=4						,   type=__lowerCamelCase						,   help="kwarg passed to DataLoader"					)
        parser.add_argument("--num_train_epochs"						,   dest="max_epochs"						,   default=3						,   type=__lowerCamelCase					)
        parser.add_argument("--train_batch_size"						,   default=3_2						,   type=__lowerCamelCase					)
        parser.add_argument("--eval_batch_size"						,   default=3_2						,   type=__lowerCamelCase					)
        parser.add_argument("--adafactor"						,   action="store_true"					)







class 	lowercase_					(				pl.Callback     ):


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			Optional[Any]						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			str					):

        """simple docstring"""
        if (
            trainer.is_global_zero and trainer.global_rank == 0
        ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
             pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class 	lowercase_					(				pl.Callback     ):


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			str						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			int					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   trainer.lr_schedulers[0]["scheduler"]
        _SCREAMING_SNAKE_CASE =   {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
        pl_module.logger.log_metrics(__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[Any]						,   __lowerCamelCase      :			pl.Trainer						,   __lowerCamelCase      :			pl.LightningModule					):

        """simple docstring"""
        rank_zero_info("***** Validation results *****"					)
        _SCREAMING_SNAKE_CASE =   trainer.callback_metrics
        # Log results
        for key in sorted(__lowerCamelCase					):
             if key not in ["log", "progress_bar"]:
                  rank_zero_info("{} = {}\n".format(__lowerCamelCase						,   str(metrics[key]					)					)					)



   def 							lowerCAmelCase_    (					self      :			str						,   __lowerCamelCase      :			pl.Trainer						,   __lowerCamelCase      :			pl.LightningModule					):

        """simple docstring"""
        rank_zero_info("***** Test results *****"					)
        _SCREAMING_SNAKE_CASE =   trainer.callback_metrics
        # Log and save results to file
        _SCREAMING_SNAKE_CASE =   os.path.join(pl_module.hparams.output_dir						,   "test_results.txt"					)
        with open(__lowerCamelCase						,   "w"					) as writer:
             for key in sorted(__lowerCamelCase					):
                  if key not in ["log", "progress_bar"]:
                       rank_zero_info("{} = {}\n".format(__lowerCamelCase						,   str(metrics[key]					)					)					)
                       writer.write("{} = {}\n".format(__lowerCamelCase						,   str(metrics[key]					)					)					)


def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[Any] ,  __A       :				Any						)				->				None:
     #  To allow all pl args uncomment the following line
     #  parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         "--output_dir" ,  default=__A ,  type=__A ,  required=__A ,  help="The output directory where the model predictions and checkpoints will be written." ,  )
     parser.add_argument(
         "--fp16" ,  action="store_true" ,  help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" ,  )

     parser.add_argument(
         "--fp16_opt_level" ,  type=__A ,  default="O2" ,  help=(
             "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
             "See details at https://nvidia.github.io/apex/amp.html"
         ) ,  )
     parser.add_argument("--n_tpu_cores" ,  dest="tpu_cores" ,  type=__A						)
     parser.add_argument("--max_grad_norm" ,  dest="gradient_clip_val" ,  default=1.0 ,  type=__A ,  help="Max gradient norm"						)
     parser.add_argument("--do_train" ,  action="store_true" ,  help="Whether to run training."						)
     parser.add_argument("--do_predict" ,  action="store_true" ,  help="Whether to run predictions on the test set."						)
     parser.add_argument(
         "--gradient_accumulation_steps" ,  dest="accumulate_grad_batches" ,  type=__A ,  default=1 ,  help="Number of updates steps to accumulate before performing a backward/update pass." ,  )
     parser.add_argument("--seed" ,  type=__A ,  default=42 ,  help="random seed for initialization"						)
     parser.add_argument(
         "--data_dir" ,  default=__A ,  type=__A ,  required=__A ,  help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." ,  )






def 				SCREAMING_SNAKE_CASE_				(							__A       :				BaseTransformer ,  __A       :				argparse.Namespace ,  __A       :				List[str]=None ,  __A       :				int=True ,  __A       :				List[Any]=None ,  __A       :				List[Any]=[] ,  __A       :				str=None ,  __A       :				Optional[int]=None ,  **__A       :				Optional[int] ,  )				->				Optional[Any]:
     pl.seed_everything(args.seed						)

     # init model
     _SCREAMING_SNAKE_CASE =   Path(model.hparams.output_dir						)
     odir.mkdir(exist_ok=__A						)

     # add custom checkpoints
     if checkpoint_callback is None:
          _SCREAMING_SNAKE_CASE =   pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir ,  prefix="checkpoint" ,  monitor="val_loss" ,  mode="min" ,  save_top_k=1						)
     if early_stopping_callback:
          extra_callbacks.append(__A						)
     if logging_callback is None:
          _SCREAMING_SNAKE_CASE =   LoggingCallback()

     _SCREAMING_SNAKE_CASE =   {}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          _SCREAMING_SNAKE_CASE =   16
          # train_params["amp_level"] = args.fp16_opt_level

     if args.gpus > 1:
          _SCREAMING_SNAKE_CASE =   "auto"  # "ddp"
          _SCREAMING_SNAKE_CASE =   "ddp"

     _SCREAMING_SNAKE_CASE =   args.accumulate_grad_batches
     _SCREAMING_SNAKE_CASE =   None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
     _SCREAMING_SNAKE_CASE =   "auto"

     _SCREAMING_SNAKE_CASE =   pl.Trainer.from_argparse_args(
         __A ,  weights_summary=__A ,  callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()] ,  logger=__A ,  **__A ,  )

     if args.do_train:
          trainer.fit(__A						)

     return trainer







'''simple docstring'''



import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:







 class 	lowercase_					:


    """simple docstring"""

    @staticmethod
    def 							lowerCAmelCase_    (					*__lowerCamelCase      :			Union[str, Any]						,   **__lowerCamelCase      :			str					):

       """simple docstring"""
       pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   lowerCamelCase_       						=		TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   ImageClassificationPipeline(model=__lowerCamelCase						,   image_processor=__lowerCamelCase						,   top_k=2					)
      _SCREAMING_SNAKE_CASE =   [
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
      ]
      return image_classifier, examples



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"					)

      self.assertEqual(
          __lowerCamelCase						,   [
              {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
          ]						,   )

      import datasets

      _SCREAMING_SNAKE_CASE =   datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,   "image"						,   split="test"					)

      # Accepts URL + PIL.Image + lists
      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              # RGBA
              dataset[0]["file"],
              # LA
              dataset[1]["file"],
              # L
              dataset[2]["file"],
          ]					)
      self.assertEqual(
          __lowerCamelCase						,   [
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
          ]						,   )



   @require_torch
   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "hf-internal-testing/tiny-random-vit"
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]						,   )

      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]						,   top_k=2						,   )
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]						,   )



   @require_tf
   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "hf-internal-testing/tiny-random-vit"
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model=__lowerCamelCase						,   framework="tf"					)

      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]						,   )

      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]						,   top_k=2						,   )
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]						,   )



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   PreTrainedTokenizer()

      # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
      _SCREAMING_SNAKE_CASE =   pipeline(
          "image-classification"						,   model="hf-internal-testing/tiny-random-vit"						,   tokenizer=__lowerCamelCase					)

      self.assertIs(image_classifier.tokenizer						,   __lowerCamelCase					)



   @slow
   @require_torch
   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      # Perceiver is not tested by `run_pipeline_test` properly.
      # That is because the type of feature_extractor and model preprocessor need to be kept
      # in sync, which is not the case in the current design
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-conv"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
              {"score": 0.3_2_1, "label": "tiger cat"},
              {"score": 0.0_5_0_2, "label": "Egyptian cat"},
              {"score": 0.0_1_3_7, "label": "crib, cot"},
              {"score": 0.0_0_7, "label": "radiator"},
          ]						,   )

      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-fourier"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
              {"score": 0.1_3_0_9, "label": "tiger cat"},
              {"score": 0.0_7_2_2, "label": "Egyptian cat"},
              {"score": 0.0_7_0_7, "label": "remote control, remote"},
              {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
          ]						,   )

      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-learned"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
              {"score": 0.2_3_6_2, "label": "Egyptian cat"},
              {"score": 0.1_8_5_6, "label": "tiger cat"},
              {"score": 0.0_3_2_4, "label": "remote control, remote"},
              {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
          ]						,   )

