




'''simple docstring'''





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


lowercase__			=	'''
Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
class names.
'''

lowercase__			=	logging.getLogger(__name__)






@dataclass
class 						lowercase     :
      __lowercase 		=  field(
          default='''roberta-large-mnli'''				,			metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''})
      __lowercase 		=  field(
          default='''This example is {}.'''				,			metadata={
              '''help''': (
                  '''Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}'''
                  '''where class name is inserted.'''
              )
          }				,			)
      __lowercase 		=  field(
          default=32				,			metadata={'''help''': '''Batch size for generating teacher predictions.'''})
      __lowercase 		=  field(
          default=UpperCAmelCase_				,			metadata={
              '''help''': (
                  '''Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called'''
                  '''multi-class multi-label classification).'''
              )
          }				,			)
      __lowercase 		=  field(
          default=1.0				,			metadata={'''help''': '''Temperature applied to teacher softmax for distillation.'''})






@dataclass
class 						lowercase     :
      __lowercase 		=  field(
          default='''distilbert-base-uncased'''				,			metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''})






@dataclass
class 						lowercase     :
      __lowercase 		=  field(metadata={'''help''': '''Text file with one unlabeled instance per line.'''})
      __lowercase 		=  field(metadata={'''help''': '''Text file with one class name per line.'''})
      __lowercase 		=  field(
          default=UpperCAmelCase_				,			metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not.'''}				,			)






@dataclass
class 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  field(
          default=UpperCAmelCase_				,			metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}				,			)
      __lowercase 		=  field(
          default=32				,			metadata={'''help''': '''Batch size per GPU/TPU core/CPU for training.'''})
      __lowercase 		=  field(
          default=1_28				,			metadata={'''help''': '''Batch size per GPU/TPU core/CPU for evaluation.'''})
      __lowercase 		=  field(default=1.0				,			metadata={'''help''': '''Total number of training epochs to perform.'''})
      __lowercase 		=  field(default=UpperCAmelCase_				,			metadata={'''help''': '''Whether to run training of student model.'''})
      __lowercase 		=  field(
          default=UpperCAmelCase_				,			metadata={
              '''help''': (
                  '''Whether to evaluate the agreement of the final student predictions and the teacher predictions'''
                  '''after training.'''
              )
          }				,			)
      __lowercase 		=  field(
          default=0				,			metadata={
              '''help''': (
                  '''Limit the total amount of checkpoints.'''
                  '''Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints).'''
              )
          }				,			)






class 						lowercase     (   UpperCAmelCase_):






      def    A__(self ,  a__ ,  a__ ,  a__=False					)       ->   int:
         SCREAMING_SNAKE_CASE__			:				Dict       						= inputs['''labels''']
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= model(inputs['''input_ids'''] ,  attention_mask=inputs['''attention_mask''']					)
         SCREAMING_SNAKE_CASE__			:				Dict       						= outputs[0]

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= -torch.sum(target_p * logits.log_softmax(dim=-1					) ,  axis=-1					).mean()

         if return_outputs:
            return loss, outputs

         return loss


def 				UpperCamelCase	(  snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= []
   with open(snake_case	,     '''r'''   ) as f:
      for line in f:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= line.strip()
         if len(snake_case   ) > 0:
            lines.append(snake_case   )
   return lines


def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= []
   SCREAMING_SNAKE_CASE__			:				int       						= []
   for example in examples:
      for name in class_names:
         premises.append(snake_case   )
         hypotheses.append(hypothesis_template.format(snake_case   )   )
   return premises, hypotheses


def 				UpperCamelCase	(  snake_case   ):




   '''simple docstring'''

   for label, ind in config.labelaid.items():
      if label.lower().startswith('''entail'''   ):
         return ind
   logger.warning('''Could not identify entailment dimension from teacher config label2id. Setting to -1.'''   )
   return -1


def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case	,     snake_case	,     snake_case	,     snake_case	,     snake_case	,     snake_case	,     snake_case	,     snake_case	,     ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= AutoModelForSequenceClassification.from_pretrained(snake_case   )
   SCREAMING_SNAKE_CASE__			:				Optional[int]       						= model.config
   if not no_cuda and torch.cuda.is_available():
      SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= nn.DataParallel(model.cuda()   )
      batch_size *= len(model.device_ids   )
   SCREAMING_SNAKE_CASE__			:				List[Any]       						= AutoTokenizer.from_pretrained(snake_case	,     use_fast=snake_case   )

   SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Dict       						= get_premise_hypothesis_pairs(snake_case	,     snake_case	,     snake_case   )
   SCREAMING_SNAKE_CASE__			:				Dict       						= []

   for i in tqdm(range(0	,     len(snake_case   )	,     snake_case   )   ):
      SCREAMING_SNAKE_CASE__			:				int       						= premises[i : i + batch_size]
      SCREAMING_SNAKE_CASE__			:				str       						= hypotheses[i : i + batch_size]

      SCREAMING_SNAKE_CASE__			:				Tuple       						= tokenizer(
          snake_case	,     snake_case	,     padding=snake_case	,     truncation='''only_first'''	,     return_tensors='''pt'''	,     )

      with torch.cuda.amp.autocast(enabled=snake_case   ):
         with torch.no_grad():
            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= model(**snake_case   )
      logits.append(outputs.logits.detach().cpu().float()   )

   SCREAMING_SNAKE_CASE__			:				Any       						= get_entailment_id(snake_case   )
   SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= -1 if entail_id == 0 else 0
   SCREAMING_SNAKE_CASE__			:				Tuple       						= torch.cat(snake_case	,     dim=0   )  # N*K x 3
   SCREAMING_SNAKE_CASE__			:				Optional[int]       						= logits.reshape(len(snake_case   )	,     len(snake_case   )	,     -1   )[..., [contr_id, entail_id]]  # N x K x 2

   if multi_label:
      # softmax over (contr, entail) logits for each class independently
      SCREAMING_SNAKE_CASE__			:				str       						= (nli_logits / temperature).softmax(-1   )
   else:
      # softmax over entail logits across classes s.t. class probabilities sum to 1.
      SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= (nli_logits / temperature).softmax(1   )

   return nli_prob[..., 1]  # N x K


def 				UpperCamelCase	(  ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Any       						= HfArgumentParser(
       (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)	,     description=snake_case	,     )

   if len(sys.argv   ) == 2 and sys.argv[1].endswith('''.json'''   ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				Dict       						= parser.parse_json_file(
          json_file=os.path.abspath(sys.argv[1]   )   )
   else:
      SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				str       						= parser.parse_args_into_dataclasses()

   # Detecting last checkpoint.
   SCREAMING_SNAKE_CASE__			:				Dict       						= None
   if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
      SCREAMING_SNAKE_CASE__			:				int       						= get_last_checkpoint(training_args.output_dir   )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
         raise ValueError(
             f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
             '''Use --overwrite_output_dir to overcome.'''   )
      elif last_checkpoint is not None:
         logger.info(
             f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
             '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''   )

    # Setup logging
   logging.basicConfig(
       format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''	,     datefmt='''%m/%d/%Y %H:%M:%S'''	,     handlers=[logging.StreamHandler(sys.stdout   )]	,     )
   logger.setLevel(logging.INFO if is_main_process(training_args.local_rank   ) else logging.WARN   )

   # Log on each process the small summary:
   logger.warning(
       f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
       + f"""distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}"""   )
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank   ):
      utils.logging.set_verbosity_info()
      utils.logging.enable_default_handler()
      utils.logging.enable_explicit_format()

   if training_args.local_rank != -1:
      raise ValueError('''Distributed training is not currently supported.'''   )
   if training_args.tpu_num_cores is not None:
      raise ValueError('''TPU acceleration is not currently supported.'''   )

   logger.info(f"""Training/evaluation parameters {training_args}"""   )

   # Set seed before initializing model.
   set_seed(training_args.seed   )

   # 1. read in data
   SCREAMING_SNAKE_CASE__			:				List[Any]       						= read_lines(data_args.data_file   )
   SCREAMING_SNAKE_CASE__			:				Tuple       						= read_lines(data_args.class_names_file   )

   # 2. get teacher predictions and load into dataset
   logger.info('''Generating predictions from zero-shot teacher model'''   )
   SCREAMING_SNAKE_CASE__			:				int       						= get_teacher_predictions(
       teacher_args.teacher_name_or_path	,     snake_case	,     snake_case	,     teacher_args.hypothesis_template	,     teacher_args.teacher_batch_size	,     teacher_args.temperature	,     teacher_args.multi_label	,     data_args.use_fast_tokenizer	,     training_args.no_cuda	,     training_args.fpaa	,     )
   SCREAMING_SNAKE_CASE__			:				str       						= Dataset.from_dict(
       {
           '''text''': examples,
           '''labels''': teacher_soft_preds,
       }   )

   # 3. create student
   logger.info('''Initializing student model'''   )
   SCREAMING_SNAKE_CASE__			:				Optional[int]       						= AutoModelForSequenceClassification.from_pretrained(
       student_args.student_name_or_path	,     num_labels=len(snake_case   )   )
   SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= AutoTokenizer.from_pretrained(student_args.student_name_or_path	,     use_fast=data_args.use_fast_tokenizer   )
   SCREAMING_SNAKE_CASE__			:				Dict       						= dict(enumerate(snake_case   )   )
   SCREAMING_SNAKE_CASE__			:				Dict       						= {label: i for i, label in enumerate(snake_case   )}

   # 4. train student on teacher predictions
   SCREAMING_SNAKE_CASE__			:				Any       						= dataset.map(snake_case	,     input_columns='''text'''   )
   dataset.set_format('''torch'''   )

   def compute_metrics(snake_case	,     snake_case=False   ):
      SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= p.predictions.argmax(-1   )
      SCREAMING_SNAKE_CASE__			:				List[Any]       						= p.label_ids.argmax(-1   )  # "label_ids" are actually distributions
      return {"agreement": (preds == proxy_labels).mean().item()}

   SCREAMING_SNAKE_CASE__			:				Tuple       						= DistillationTrainer(
       model=snake_case	,     tokenizer=snake_case	,     args=snake_case	,     train_dataset=snake_case	,     compute_metrics=snake_case	,     )

   if training_args.do_train:
      logger.info('''Training student model on teacher predictions'''   )
      trainer.train()

   if training_args.do_eval:
      SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= trainer.evaluate(eval_dataset=snake_case   )['''eval_agreement''']
      logger.info(f"""Agreement of student and teacher predictions: {agreement * 1_00:0.2f}%"""   )

   trainer.save_model()


if __name__ == "__main__":
       main()







'''simple docstring'''





import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
       from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
       lowercase__			=	False


if is_vision_available():
       from PIL import Image
else:






       class 						lowercase     :






             @staticmethod
             def    A__(*a__ ,  **a__					)       ->   Optional[Any]:
                pass






@is_pipeline_test
@require_vision
class 						lowercase     (   unittest.TestCase):
      __lowercase 		=  MODEL_FOR_VISION_2_SEQ_MAPPING
      __lowercase 		=  TF_MODEL_FOR_VISION_2_SEQ_MAPPING






      def    A__(self ,  a__ ,  a__ ,  a__					)       ->   Optional[Any]:
         SCREAMING_SNAKE_CASE__			:				Tuple       						= pipeline('''image-to-text''' ,  model=a__ ,  tokenizer=a__ ,  image_processor=a__					)
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= [
             Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					),
             '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
         ]
         return pipe, examples






      def    A__(self ,  a__ ,  a__					)       ->   Any:
         SCREAMING_SNAKE_CASE__			:				Tuple       						= pipe(a__					)
         self.assertEqual(
             a__ ,  [
                 [{'''generated_text''': ANY(a__					)}],
                 [{'''generated_text''': ANY(a__					)}],
             ] ,  )






      @require_tf
      def    A__(self					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= pipeline('''image-to-text''' ,  model='''hf-internal-testing/tiny-random-vit-gpt2''' ,  framework='''tf'''					)
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= '''./tests/fixtures/tests_samples/COCO/000000039769.png'''

         SCREAMING_SNAKE_CASE__			:				str       						= pipe(a__					)
         self.assertEqual(
             a__ ,  [
                 {
                     '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                 },
             ] ,  )

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= pipe([image, image]					)
         self.assertEqual(
             a__ ,  [
                 [
                     {
                         '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                     }
                 ],
                 [
                     {
                         '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                     }
                 ],
             ] ,  )

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipe(a__ ,  max_new_tokens=1					)
         self.assertEqual(
             a__ ,  [{'''generated_text''': '''growth'''}] ,  )






      @require_torch
      def    A__(self					)       ->   Optional[Any]:
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= pipeline('''image-to-text''' ,  model='''hf-internal-testing/tiny-random-vit-gpt2'''					)
         SCREAMING_SNAKE_CASE__			:				Any       						= '''./tests/fixtures/tests_samples/COCO/000000039769.png'''

         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= pipe(a__					)
         self.assertEqual(
             a__ ,  [
                 {
                     '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                 },
             ] ,  )

         SCREAMING_SNAKE_CASE__			:				Any       						= pipe([image, image]					)
         self.assertEqual(
             a__ ,  [
                 [
                     {
                         '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                     }
                 ],
                 [
                     {
                         '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
                     }
                 ],
             ] ,  )






      @require_torch
      def    A__(self					)       ->   Any:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipeline('''image-to-text''' ,  model='''hf-internal-testing/tiny-random-BlipForConditionalGeneration'''					)
         SCREAMING_SNAKE_CASE__			:				int       						= '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
         SCREAMING_SNAKE_CASE__			:				Tuple       						= '''a photo of'''

         SCREAMING_SNAKE_CASE__			:				List[Any]       						= pipe(a__ ,  prompt=a__					)
         self.assertTrue(outputs[0]['''generated_text'''].startswith(a__					)					)






      @slow
      @require_torch
      def    A__(self					)       ->   List[Any]:
         SCREAMING_SNAKE_CASE__			:				Tuple       						= pipeline('''image-to-text''' ,  model='''ydshieh/vit-gpt2-coco-en'''					)
         SCREAMING_SNAKE_CASE__			:				List[str]       						= '''./tests/fixtures/tests_samples/COCO/000000039769.png'''

         SCREAMING_SNAKE_CASE__			:				List[Any]       						= pipe(a__					)
         self.assertEqual(a__ ,  [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}]					)

         SCREAMING_SNAKE_CASE__			:				Tuple       						= pipe([image, image]					)
         self.assertEqual(
             a__ ,  [
                 [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}],
                 [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}],
             ] ,  )






      @slow
      @require_torch
      def    A__(self					)       ->   Tuple:
         SCREAMING_SNAKE_CASE__			:				Dict       						= pipeline('''image-to-text''' ,  model='''Salesforce/blip-image-captioning-base'''					)
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= '''https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'''
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= Image.open(requests.get(a__ ,  stream=a__					).raw					)

         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= pipe(a__					)
         self.assertEqual(a__ ,  [{'''generated_text''': '''a pink pokemon pokemon with a blue shirt and a blue shirt'''}]					)






      @slow
      @require_torch
      def    A__(self					)       ->   int:
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= pipeline('''image-to-text''' ,  model='''microsoft/git-base-coco'''					)
         SCREAMING_SNAKE_CASE__			:				str       						= '''https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'''
         SCREAMING_SNAKE_CASE__			:				List[str]       						= Image.open(requests.get(a__ ,  stream=a__					).raw					)

         SCREAMING_SNAKE_CASE__			:				str       						= pipe(a__					)
         self.assertEqual(a__ ,  [{'''generated_text''': '''a cartoon of a purple character.'''}]					)






      @slow
      @require_torch
      def    A__(self					)       ->   Dict:
         SCREAMING_SNAKE_CASE__			:				str       						= pipeline('''image-to-text''' ,  model='''Salesforce/blip-image-captioning-base'''					)
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= Image.open(requests.get(a__ ,  stream=a__					).raw					)

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= '''a photography of'''

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipe(a__ ,  prompt=a__					)
         self.assertEqual(a__ ,  [{'''generated_text''': '''a photography of a volcano'''}]					)

         with self.assertRaises(a__					):
            SCREAMING_SNAKE_CASE__			:				Any       						= pipe([image, image] ,  prompt=[prompt, prompt]					)






      @slow
      @require_torch
      def    A__(self					)       ->   Dict:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipeline('''image-to-text''' ,  model='''microsoft/git-base-coco'''					)
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
         SCREAMING_SNAKE_CASE__			:				Dict       						= Image.open(requests.get(a__ ,  stream=a__					).raw					)

         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= '''a photo of a'''

         SCREAMING_SNAKE_CASE__			:				str       						= pipe(a__ ,  prompt=a__					)
         self.assertEqual(a__ ,  [{'''generated_text''': '''a photo of a tent with a tent and a tent in the background.'''}]					)

         with self.assertRaises(a__					):
            SCREAMING_SNAKE_CASE__			:				Dict       						= pipe([image, image] ,  prompt=[prompt, prompt]					)






      @unittest.skipIf(
          not is_torch_greater_or_equal_than_1_11 ,  reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''					)
      @slow
      @require_torch
      def    A__(self					)       ->   Union[str, Any]:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= pipeline('''image-to-text''' ,  model='''google/pix2struct-ai2d-base'''					)
         SCREAMING_SNAKE_CASE__			:				int       						= '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
         SCREAMING_SNAKE_CASE__			:				Any       						= Image.open(requests.get(a__ ,  stream=a__					).raw					)

         SCREAMING_SNAKE_CASE__			:				int       						= '''What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'''

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipe(a__ ,  prompt=a__					)
         self.assertEqual(a__ ,  [{'''generated_text''': '''ash cloud'''}]					)

         with self.assertRaises(a__					):
            SCREAMING_SNAKE_CASE__			:				Any       						= pipe([image, image] ,  prompt=[prompt, prompt]					)






      @slow
      @require_tf
      def    A__(self					)       ->   str:
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= pipeline('''image-to-text''' ,  model='''ydshieh/vit-gpt2-coco-en''' ,  framework='''tf'''					)
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= '''./tests/fixtures/tests_samples/COCO/000000039769.png'''

         SCREAMING_SNAKE_CASE__			:				Dict       						= pipe(a__					)
         self.assertEqual(a__ ,  [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}]					)

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= pipe([image, image]					)
         self.assertEqual(
             a__ ,  [
                 [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}],
                 [{'''generated_text''': '''a cat laying on a blanket next to a cat laying on a bed '''}],
             ] ,  )

