'''simple docstring'''




import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


__SCREAMING_SNAKE_CASE :  str  =						logging.getLogger(__name__)

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Any					)					->					Any:


       """simple docstring"""







       random.seed(_UpperCAmelCase					)
       np.random.seed(_UpperCAmelCase					)
       torch.manual_seed(_UpperCAmelCase					)
       torch.cuda.manual_seed_all(_UpperCAmelCase					)

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Dict    ,			_UpperCAmelCase						:   Optional[Any]    ,			_UpperCAmelCase						:   Dict					)					->					List[str]:


       """simple docstring"""







       model.eval()
       _UpperCAmelCase  :						Any							   =					next(model.parameters()					).device
       _UpperCAmelCase  :						int							   =					1
       _UpperCAmelCase  :						List[Any]							   =					torch.zeros((eval_batch_size, context_len)    ,			dtype=torch.long    ,			device=_UpperCAmelCase					)
       _UpperCAmelCase  :						List[str]							   =					DataLoader(_UpperCAmelCase    ,			shuffle=_UpperCAmelCase    ,			batch_size=_UpperCAmelCase					)
       _UpperCAmelCase  :						Dict							   =					torch.zeros(1    ,			device=_UpperCAmelCase					)
       _UpperCAmelCase  :						Optional[Any]							   =					0
       for batch in eval_dataloader:
              batch.to(_UpperCAmelCase					)
              # pad
              context.zero_()
              for i in range(_UpperCAmelCase					):
                     _UpperCAmelCase  :						List[str]							   =					batch[i]
              _UpperCAmelCase  :						List[str]							   =					model(_UpperCAmelCase    ,			labels=_UpperCAmelCase					)
              eval_loss += outputs[0].sum().item()
              nb_eval_examples += batch.size(0					)
       _UpperCAmelCase  :						Optional[int]							   =					eval_loss / nb_eval_examples
       _UpperCAmelCase  :						Any							   =					torch.exp(_UpperCAmelCase					)
       model.train()
       return perplexity

def 			UpperCamelCase_							(      _UpperCAmelCase						:   List[str]="gpt2"					)					->					int:


       """simple docstring"""







       _UpperCAmelCase  :						int							   =					GPTaLMHeadModel.from_pretrained(_UpperCAmelCase    ,			output_hidden_states=_UpperCAmelCase					)
       torch.save(model.state_dict()    ,			model_name + "local.pt"					)
       return model

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[Any]    ,			_UpperCAmelCase						:   Any    ,			_UpperCAmelCase						:   str					)					->					List[Any]:


       """simple docstring"""







       _UpperCAmelCase  :						Any							   =					copy.deepcopy(_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)

       _UpperCAmelCase  :						Union[str, Any]							   =					["bias", "LayerNorm.weight"]
       _UpperCAmelCase  :						Optional[Any]							   =					[
           {
               "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay					)],
               "weight_decay": 0.0,
           },
           {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay					)], "weight_decay": 0.0},
       ]
       _UpperCAmelCase  :						List[Any]							   =					AdamW(_UpperCAmelCase    ,			lr=5e-5    ,			eps=1e-8					)
       _UpperCAmelCase  :						int							   =					get_linear_schedule_with_warmup(_UpperCAmelCase    ,			0    ,			_UpperCAmelCase					)
       torch.cuda.empty_cache()
       return model, lm_optimizer, lm_scheduler

def 			UpperCamelCase_							(      _UpperCAmelCase						:   List[Any]    ,			_UpperCAmelCase						:   Dict    ,			_UpperCAmelCase						:   List[str]    ,			_UpperCAmelCase						:   Dict					)					->					int:


       """simple docstring"""







       _UpperCAmelCase  :						List[Any]							   =					np.array(_UpperCAmelCase					).mean()
       _UpperCAmelCase  :						Union[str, Any]							   =					np.array(_UpperCAmelCase					).std()
       _UpperCAmelCase  :						str							   =					(real_perps - avg) / std
       _UpperCAmelCase  :						Any							   =					list(zip(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)					)
       joblib.dump(_UpperCAmelCase    ,			_UpperCAmelCase					)

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Dict    ,			_UpperCAmelCase						:   List[Any]    ,			_UpperCAmelCase						:   List[Any]    ,			_UpperCAmelCase						:   Union[str, Any]    ,			_UpperCAmelCase						:   Dict    ,			_UpperCAmelCase						:   Optional[Any]    ,			_UpperCAmelCase						:   str    ,			_UpperCAmelCase						:   Optional[int]="dev.jbl"    ,			_UpperCAmelCase						:   Union[str, Any]=recopy_gpta    ,			)					->					Optional[int]:


       """simple docstring"""







       _UpperCAmelCase  :						Optional[int]							   =					[]
       _UpperCAmelCase  :						str							   =					[]
       _UpperCAmelCase  :						List[Any]							   =					[]

       # Initialize the transformer model
       _UpperCAmelCase  :						Optional[int]							   =					copy.deepcopy(_UpperCAmelCase					)
       orig_model.to(device="cpu"					)
       torch.cuda.empty_cache()

       # Compute perplexity of initial transformer model for comparison
       model.train()
       _UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase  :						Union[str, Any]							   =					recopy_model(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)

       for step in tqdm(range(_UpperCAmelCase					)					):
              _UpperCAmelCase  :						Tuple							   =					torch.zeros((1, context_len)    ,			dtype=torch.long    ,			device=_UpperCAmelCase					)
              _UpperCAmelCase  :						str							   =					random.choice(_UpperCAmelCase					)
              _UpperCAmelCase  :						str							   =					random.randint(0    ,			len(story[0]					) - context_len - 1					)
              _UpperCAmelCase  :						Union[str, Any]							   =					story[0][start : start + context_len]
              lm_optimizer.zero_grad()
              _UpperCAmelCase  :						int							   =					model(_UpperCAmelCase    ,			labels=_UpperCAmelCase					)
              _UpperCAmelCase  :						Optional[int]							   =					outputs[0]
              _UpperCAmelCase  :						int							   =					compute_perplexity(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)
              model.train()
              lm_loss.backward()
              # Do LM backprop
              torch.nn.utils.clip_grad_norm_(model.parameters()    ,			3.0					)
              lm_optimizer.step()
              lm_scheduler.step()  # Update learning rate schedule

              # Compute perplexity after back-propagating on the selected context
              _UpperCAmelCase  :						Union[str, Any]							   =					compute_perplexity(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)

              # Periodically save the stored (X, IG(X)) pairs
              if step % 1_000 == 0 and step > 1:
                     intermittent_save(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)

              # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
              _UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase  :						List[str]							   =					recopy_model(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)

              past_perps.append(past_perp.item()					)
              real_perps.append(orig_perp - real_perp.item()					)
              contexts.append(np.array(context.cpu()					)					)

       intermittent_save(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Union[str, Any]    ,			_UpperCAmelCase						:   str="data/tokenized_stories_train_wikitext103.jbl"    ,			_UpperCAmelCase						:   Optional[Any]=100    ,			_UpperCAmelCase						:   Dict=1_026    ,			_UpperCAmelCase						:   Tuple=True					)					->					Union[str, Any]:


       """simple docstring"""







       _UpperCAmelCase  :						Optional[Any]							   =					joblib.load(_UpperCAmelCase					)
       print("data loaded"					)
       _UpperCAmelCase  :						int							   =					[]
       if trim:
              for i, example in enumerate(_UpperCAmelCase					):
                     if len(example[0]					) > min_len:
                            _UpperCAmelCase  :						Dict							   =					random.randint(0    ,			len(example[0]					) - context_len - 1					)
                            objective_set.append(example[0, start : start + context_len]					)
                     if len(_UpperCAmelCase					) >= number:
                            break
              _UpperCAmelCase  :						List[str]							   =					[]
              for j in range(i + 1    ,			len(_UpperCAmelCase					)					):
                     if len(data[j][0]					) > min_len:
                            train_data.append(data[j]					)
       else:
              _UpperCAmelCase  :						Union[str, Any]							   =					data[0:number]
              _UpperCAmelCase  :						Optional[Any]							   =					data[number:]

       joblib.dump(_UpperCAmelCase    ,			"objective_set.jbl"					)
       print("objective set saved"					)
       return train_data, objective_set

def 			UpperCamelCase_							(      _UpperCAmelCase						:   int    ,			_UpperCAmelCase						:   Any    ,			_UpperCAmelCase						:   str    ,			_UpperCAmelCase						:   Dict    ,			_UpperCAmelCase						:   Optional[int]=50    ,			_UpperCAmelCase						:   Optional[int]="secondary_learner.pt"					)					->					Optional[int]:


       """simple docstring"""







       _UpperCAmelCase  :						List[Any]							   =					torch.device("cuda:0" if torch.cuda.is_available() else "cpu"					)
       # We will use the first 512 pairs from our dataset as a test set for
       # our secondary learner and the rest to train
       _UpperCAmelCase  :						List[str]							   =					train_dataset[:512]
       _UpperCAmelCase  :						List[Any]							   =					train_dataset[512:]
       _UpperCAmelCase  :						Any							   =					DataLoader(_UpperCAmelCase    ,			shuffle=_UpperCAmelCase    ,			batch_size=_UpperCAmelCase					)
       _UpperCAmelCase  :						int							   =					DataLoader(_UpperCAmelCase    ,			shuffle=_UpperCAmelCase    ,			batch_size=_UpperCAmelCase					)

       # secondary learner model set up
       _UpperCAmelCase  :						Optional[Any]							   =					nn.MSELoss()
       _UpperCAmelCase  :						Union[str, Any]							   =					nn.MSELoss(reduction="sum"					)
       secondary_learner.to(_UpperCAmelCase					)
       _UpperCAmelCase  :						int							   =					torch.optim.Adam(secondary_learner.parameters()    ,			lr=0.0_0_0_0_1					)
       secondary_learner.train()

       # TODO in original code this is written as number of actual batches seen
       # not number of items seen but other places it is number of items instead.
       # improve consistency! changed this to epochs for clarity
       _UpperCAmelCase  :						str							   =					float("inf"					)
       # Iterate through batches until we've used max_steps batches
       for epoch in range(int(_UpperCAmelCase					)					):
              _UpperCAmelCase  :						Optional[int]							   =					0.0
              secondary_learner.train()
              for step, batch in enumerate(_UpperCAmelCase					):
                     _UpperCAmelCase  :						Any							   =					batch[0].to(_UpperCAmelCase					)
                     _UpperCAmelCase  :						str							   =					batch[1].to(_UpperCAmelCase					)
                     _UpperCAmelCase  :						Dict							   =					secondary_learner(_UpperCAmelCase					)
                     q_optimizer.zero_grad()
                     _UpperCAmelCase  :						Any							   =					loss(_UpperCAmelCase    ,			real_q.float()					)
                     q_loss.backward()
                     q_optimizer.step()
                     tr_q_loss += q_loss.item()

                     # model trains fairly quickly so we won't wait for a full epoch
                     # eval is triggered at eval_freq and end of epochs
                     if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(_UpperCAmelCase					)):
                            _UpperCAmelCase  :						Optional[int]							   =					tr_q_loss / (step + 1)

                            secondary_learner.eval()
                            _UpperCAmelCase  :						int							   =					0.0
                            _UpperCAmelCase  :						List[str]							   =					0.0
                            _UpperCAmelCase  :						Union[str, Any]							   =					[]
                            _UpperCAmelCase  :						Dict							   =					[]
                            # Compute performance of the secondary learner after this batch
                            for stepa, batcha in enumerate(_UpperCAmelCase					):
                                   _UpperCAmelCase  :						Optional[Any]							   =					batcha[0].to(_UpperCAmelCase					)
                                   _UpperCAmelCase  :						Any							   =					batcha[1].to(_UpperCAmelCase					)
                                   _UpperCAmelCase  :						Any							   =					secondary_learner(_UpperCAmelCase					)
                                   q_lossa += test_loss(_UpperCAmelCase    ,			_UpperCAmelCase					).item()
                                   sum_qa += torch.sum(_UpperCAmelCase					).item()
                                   for ei, i in enumerate(predicted_qa.cpu().detach().numpy()					):
                                          predicted.append(i.item()					)
                                   for ei, i in enumerate(real_qa.cpu().detach().numpy()					):
                                          actual.append(i.item()					)

                            q_lossa /= len(_UpperCAmelCase					)
                            print(
                                "Epoch: "    ,			_UpperCAmelCase    ,			"step: "    ,			_UpperCAmelCase    ,			"Avg. q:"    ,			sum_qa / len(_UpperCAmelCase					)    ,			"Train Loss: "    ,			_UpperCAmelCase    ,			"Test Loss: "    ,			_UpperCAmelCase    ,			)
                            if q_lossa < best_test_loss:
                                   joblib.dump((predicted, actual)    ,			"pred_vs_actual.jbl"					)
                                   torch.save(secondary_learner.state_dict()    ,			_UpperCAmelCase					)
                                   _UpperCAmelCase  :						Tuple							   =					q_lossa

                     secondary_learner.train()
       return secondary_learner


class 			lowerCamelCase_			(nn.Module					):
      '''simple docstring'''

      def __init__( self		:       Optional[int]		,      A		:       List[str] ):
             super(A		,      self ).__init__()
             _UpperCAmelCase  :						Optional[int]							   =					model.transformer.wte
             _UpperCAmelCase  :						Dict							   =					copy.deepcopy(model.transformer.wte.weight )
             _UpperCAmelCase  :						List[str]							   =					nn.Convad(self.embeddings.weight.size(1 )		,      256		,      3		,      padding=1 )
             _UpperCAmelCase  :						Optional[int]							   =					nn.Sequential(nn.Linear(256		,      32 )		,      nn.Dropout(p=0.1 )		,      nn.Linear(32		,      32 )		,      nn.Linear(32		,      1 ) )




      def   _A			( self		:       int		,      A		:       int ):
             _UpperCAmelCase  :						Union[str, Any]							   =					torch.max(self.conv(self.embeddings(A ).squeeze(1 ).transpose(1		,      2 ) )		,      2 )[0]
             _UpperCAmelCase  :						int							   =					self.fc(A )
             return qs.squeeze(1 )





      @classmethod
      def   _A			( cls		:       Optional[int]		,      A		:       Any		,      A		:       Optional[int] ):
             _UpperCAmelCase  :						Dict							   =					cls(A )  # this calls __init__
             _UpperCAmelCase  :						Tuple							   =					torch.load(A )
             secondary_learner.load_state_dict(A )
             _UpperCAmelCase  :						str							   =					model.transformer.wte
             _UpperCAmelCase  :						Any							   =					copy.deepcopy(model.transformer.wte.weight )
             return secondary_learner


'''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:
  __SCREAMING_SNAKE_CASE :  Any  =						False


if is_vision_available():
  from PIL import Image
else:

  class 			lowerCamelCase_			:
        '''simple docstring'''

        @staticmethod
        def   _A			( *A		:       str		,      **A		:       Optional[Any] ):
               pass

@is_pipeline_test
@require_vision
class 			lowerCamelCase_			(unittest.TestCase					):
      '''simple docstring'''

      __UpperCamelCase:						Union[str, Any]      			=		MODEL_FOR_VISION_2_SEQ_MAPPING
      __UpperCamelCase:						int      			=		TF_MODEL_FOR_VISION_2_SEQ_MAPPING




      def   _A			( self		:       Any		,      A		:       Any		,      A		:       str		,      A		:       Optional[int] ):
             _UpperCAmelCase  :						Dict							   =					pipeline("image-to-text"		,      model=A		,      tokenizer=A		,      image_processor=A )
             _UpperCAmelCase  :						Union[str, Any]							   =					[
                 Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
             ]
             return pipe, examples




      def   _A			( self		:       Tuple		,      A		:       Union[str, Any]		,      A		:       Optional[Any] ):
             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(
                 A		,      [
                     [{"generated_text": ANY(A )}],
                     [{"generated_text": ANY(A )}],
                 ]		,      )




      @require_tf
      def   _A			( self		:       Any ):
             _UpperCAmelCase  :						Any							   =					pipeline("image-to-text"		,      model="hf-internal-testing/tiny-random-vit-gpt2"		,      framework="tf" )
             _UpperCAmelCase  :						int							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(
                 A		,      [
                     {
                         "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                     },
                 ]		,      )

             _UpperCAmelCase  :						Tuple							   =					pipe([image, image] )
             self.assertEqual(
                 A		,      [
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                 ]		,      )

             _UpperCAmelCase  :						Dict							   =					pipe(A		,      max_new_tokens=1 )
             self.assertEqual(
                 A		,      [{"generated_text": "growth"}]		,      )




      @require_torch
      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						List[str]							   =					pipeline("image-to-text"		,      model="hf-internal-testing/tiny-random-vit-gpt2" )
             _UpperCAmelCase  :						Optional[int]							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						Optional[Any]							   =					pipe(A )
             self.assertEqual(
                 A		,      [
                     {
                         "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                     },
                 ]		,      )

             _UpperCAmelCase  :						str							   =					pipe([image, image] )
             self.assertEqual(
                 A		,      [
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                     [
                         {
                             "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                         }
                     ],
                 ]		,      )




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

             _UpperCAmelCase  :						str							   =					pipe(A		,      prompt=A )
             self.assertTrue(outputs[0]["generated_text"].startswith(A ) )




      @slow
      @require_torch
      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						Dict							   =					pipeline("image-to-text"		,      model="ydshieh/vit-gpt2-coco-en" )
             _UpperCAmelCase  :						int							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}] )

             _UpperCAmelCase  :						int							   =					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		:       Optional[Any] ):
             _UpperCAmelCase  :						Tuple							   =					pipeline("image-to-text"		,      model="Salesforce/blip-image-captioning-base" )
             _UpperCAmelCase  :						int							   =					"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
             _UpperCAmelCase  :						Optional[int]							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						Any							   =					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		:       List[Any] ):
             _UpperCAmelCase  :						List[str]							   =					pipeline("image-to-text"		,      model="microsoft/git-base-coco" )
             _UpperCAmelCase  :						Tuple							   =					"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
             _UpperCAmelCase  :						Dict							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						List[str]							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a cartoon of a purple character."}] )




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

             _UpperCAmelCase  :						Dict							   =					"a photography of"

             _UpperCAmelCase  :						Optional[int]							   =					pipe(A		,      prompt=A )
             self.assertEqual(A		,      [{"generated_text": "a photography of a volcano"}] )

             with self.assertRaises(A ):
                    _UpperCAmelCase  :						List[Any]							   =					pipe([image, image]		,      prompt=[prompt, prompt] )




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

             _UpperCAmelCase  :						List[Any]							   =					"a photo of a"

             _UpperCAmelCase  :						Tuple							   =					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 ):
                    _UpperCAmelCase  :						List[str]							   =					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] ):
             _UpperCAmelCase  :						str							   =					pipeline("image-to-text"		,      model="google/pix2struct-ai2d-base" )
             _UpperCAmelCase  :						List[Any]							   =					"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
             _UpperCAmelCase  :						List[Any]							   =					Image.open(requests.get(A		,      stream=A ).raw )

             _UpperCAmelCase  :						Optional[int]							   =					"What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"

             _UpperCAmelCase  :						str							   =					pipe(A		,      prompt=A )
             self.assertEqual(A		,      [{"generated_text": "ash cloud"}] )

             with self.assertRaises(A ):
                    _UpperCAmelCase  :						List[str]							   =					pipe([image, image]		,      prompt=[prompt, prompt] )





      @slow
      @require_tf
      def   _A			( self		:       List[Any] ):
             _UpperCAmelCase  :						str							   =					pipeline("image-to-text"		,      model="ydshieh/vit-gpt2-coco-en"		,      framework="tf" )
             _UpperCAmelCase  :						str							   =					"./tests/fixtures/tests_samples/COCO/000000039769.png"

             _UpperCAmelCase  :						Any							   =					pipe(A )
             self.assertEqual(A		,      [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}] )

             _UpperCAmelCase  :						Optional[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 "}],
                 ]		,      )

