






'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType






class      __SCREAMING_SNAKE_CASE      :




 def __init__(      self	:							int					,      __lowercase	:							Optional[int]=2					,      __lowercase	:							Any=3					,      __lowercase	:							Union[str, Any]=64					,      __lowercase	:							List[Any]=None      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=np.random.default_rng(lowerCAmelCase__      )
       SCREAMING_SNAKE_CASE__     :				str									=length
       SCREAMING_SNAKE_CASE__     :				List[str]									=rng.normal(size=(length,)      ).astype(np.floataa      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=a * self.x + b + rng.normal(scale=0.1					,      size=(length,)      ).astype(np.floataa      )




 def __len__(      self	:							List[str]      )	->						Any:
       return self.length




 def __getitem__(      self	:							Tuple					,      __lowercase	:							List[str]      )	->						str:
       return {"x": self.x[i], "y": self.y[i]}






class      __SCREAMING_SNAKE_CASE      (			torch.nn.Module				):




 def __init__(      self	:							Optional[int]					,      __lowercase	:							int=0					,      __lowercase	:							List[Any]=0					,      __lowercase	:							Optional[Any]=False      )	->						str:
       super().__init__()
       SCREAMING_SNAKE_CASE__     :				int									=torch.nn.Parameter(torch.tensor([2, 3]      ).float()      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.nn.Parameter(torch.tensor([2, 3]      ).float()      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=True




 def      __magic_name__	(      self	:							Optional[Any]					,      __lowercase	:							Union[str, Any]=None      )	->						List[str]:
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"      )
             SCREAMING_SNAKE_CASE__     :				Tuple									=False
       return x * self.a[0] + self.b[0]






class      __SCREAMING_SNAKE_CASE      (			torch.nn.Module				):




 def __init__(      self	:							Tuple					,      __lowercase	:							List[Any]=0					,      __lowercase	:							Any=0					,      __lowercase	:							str=False      )	->						int:
       super().__init__()
       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.nn.Parameter(torch.tensor(lowerCAmelCase__      ).float()      )
       SCREAMING_SNAKE_CASE__     :				Any									=torch.nn.Parameter(torch.tensor(lowerCAmelCase__      ).float()      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=True




 def      __magic_name__	(      self	:							str					,      __lowercase	:							Tuple=None      )	->						Tuple:
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"      )
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=False
       return x * self.a + self.b





def  _a(  UpperCamelCase__				:						List[str],	UpperCamelCase__				:						int = 1_6							):




      '''simple docstring'''






      from datasets import load_dataset
      from transformers import AutoTokenizer

      SCREAMING_SNAKE_CASE__     :				Optional[int]									=AutoTokenizer.from_pretrained('''bert-base-cased'''							)
      SCREAMING_SNAKE_CASE__     :				Optional[int]									={"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
      SCREAMING_SNAKE_CASE__     :				str									=load_dataset('''csv''',	data_files=_lowercase							)
      SCREAMING_SNAKE_CASE__     :				Dict									=datasets["train"].unique('''label'''							)

      SCREAMING_SNAKE_CASE__     :				List[str]									={v: i for i, v in enumerate(_lowercase							)}

      def tokenize_function(UpperCamelCase__				:						Optional[int]							):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE__     :				List[str]									=tokenizer(
                examples['''sentence1'''],	examples['''sentence2'''],	truncation=_lowercase,	max_length=_lowercase,	padding='''max_length'''							)
            if "label" in examples:
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=[label_to_id[l] for l in examples["label"]]
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      SCREAMING_SNAKE_CASE__     :				Any									=datasets.map(
          _lowercase,	batched=_lowercase,	remove_columns=['''sentence1''', '''sentence2''', '''label'''],	)

      def collate_fn(UpperCamelCase__				:						Any							):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(_lowercase,	padding='''max_length''',	max_length=1_2_8,	return_tensors='''pt'''							)
            return tokenizer.pad(_lowercase,	padding='''longest''',	return_tensors='''pt'''							)

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE__     :				List[Any]									=DataLoader(tokenized_datasets['''train'''],	shuffle=_lowercase,	collate_fn=_lowercase,	batch_size=2							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=DataLoader(tokenized_datasets['''validation'''],	shuffle=_lowercase,	collate_fn=_lowercase,	batch_size=1							)

      return train_dataloader, eval_dataloader







'''simple docstring'''
import unittest

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

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:






  class      __SCREAMING_SNAKE_CASE      :




   @staticmethod
   def      __magic_name__	(      *__lowercase	:							int					,      **__lowercase	:							Optional[Any]      )	->						Optional[Any]:
         pass






@is_pipeline_test
@require_vision
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 @require_torch
 def      __magic_name__	(      self	:							Optional[int]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipeline(
           model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''					,      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=image_classifier(__lowercase					,      candidate_labels=['''a''', '''b''', '''c''']      )

       # The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
       # python and torch versions.
       self.assertIn(
           nested_simplify(__lowercase      )					,      [
               [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
               [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
           ]					,      )

       SCREAMING_SNAKE_CASE__     :				int									=image_classifier([image] * 5					,      candidate_labels=['''A''', '''B''', '''C''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
           ]					,      )




 @require_tf
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipeline(
           model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''					,      framework='''tf'''      )
       SCREAMING_SNAKE_CASE__     :				Dict									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=image_classifier(__lowercase					,      candidate_labels=['''a''', '''b''', '''c''']      )

       self.assertEqual(
           nested_simplify(__lowercase      )					,      [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}]					,      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=image_classifier([image] * 5					,      candidate_labels=['''A''', '''B''', '''C''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
           ]					,      )




 @slow
 @require_torch
 def      __magic_name__	(      self	:							List[Any]      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=pipeline(
           task='''zero-shot-image-classification'''					,      model='''openai/clip-vit-base-patch32'''					,      )
       # This is an image of 2 cats with remotes and no planes
       SCREAMING_SNAKE_CASE__     :				Any									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=image_classifier(__lowercase					,      candidate_labels=['''cat''', '''plane''', '''remote''']      )

       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               {'''score''': 0.511, '''label''': '''remote'''},
               {'''score''': 0.485, '''label''': '''cat'''},
               {'''score''': 0.004, '''label''': '''plane'''},
           ]					,      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=image_classifier([image] * 5					,      candidate_labels=['''cat''', '''plane''', '''remote''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.511, '''label''': '''remote'''},
                   {'''score''': 0.485, '''label''': '''cat'''},
                   {'''score''': 0.004, '''label''': '''plane'''},
               ],
           ]
           * 5					,      )




 @slow
 @require_tf
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						int:
       SCREAMING_SNAKE_CASE__     :				str									=pipeline(
           task='''zero-shot-image-classification'''					,      model='''openai/clip-vit-base-patch32'''					,      framework='''tf'''      )
       # This is an image of 2 cats with remotes and no planes
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=image_classifier(__lowercase					,      candidate_labels=['''cat''', '''plane''', '''remote''']      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               {'''score''': 0.511, '''label''': '''remote'''},
               {'''score''': 0.485, '''label''': '''cat'''},
               {'''score''': 0.004, '''label''': '''plane'''},
           ]					,      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=image_classifier([image] * 5					,      candidate_labels=['''cat''', '''plane''', '''remote''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.511, '''label''': '''remote'''},
                   {'''score''': 0.485, '''label''': '''cat'''},
                   {'''score''': 0.004, '''label''': '''plane'''},
               ],
           ]
           * 5					,      )