




import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

lowerCamelCase       :	int									=     collections.namedtuple("_Datasets", ["train", "validation", "test"])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCamelCase       :	Optional[Any]									=     "https://storage.googleapis.com/cvdf-datasets/mnist/"

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



      lowerCamelCase_	  =					numpy.dtype(numpy.uintaa  ).newbyteorder('>'  )
      return numpy.frombuffer(bytestream.read(4  )			,							dtype=lowerCamelCase_  )[0]

@deprecated(lowerCamelCase_			,							'Please use tf.data to implement this functionality.'  )
def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]  ):


      '''simple docstring'''



      print('Extracting'			,							f.name  )
      with gzip.GzipFile(fileobj=lowerCamelCase_  ) as bytestream:
            lowerCamelCase_	  =					_readaa(lowerCamelCase_  )
            if magic != 20_51:
                  raise ValueError(
                      'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)  )
            lowerCamelCase_	  =					_readaa(lowerCamelCase_  )
            lowerCamelCase_	  =					_readaa(lowerCamelCase_  )
            lowerCamelCase_	  =					_readaa(lowerCamelCase_  )
            lowerCamelCase_	  =					bytestream.read(rows * cols * num_images  )
            lowerCamelCase_	  =					numpy.frombuffer(lowerCamelCase_			,							dtype=numpy.uinta  )
            lowerCamelCase_	  =					data.reshape(lowerCamelCase_			,							lowerCamelCase_			,							lowerCamelCase_			,							1  )
            return data

@deprecated(lowerCamelCase_			,							'Please use tf.one_hot on tensors.'  )
def     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, Any]			,							lowercase			: int  ):


      '''simple docstring'''



      lowerCamelCase_	  =					labels_dense.shape[0]
      lowerCamelCase_	  =					numpy.arange(lowerCamelCase_  ) * num_classes
      lowerCamelCase_	  =					numpy.zeros((num_labels, num_classes)  )
      lowerCamelCase_	  =					1
      return labels_one_hot

@deprecated(lowerCamelCase_			,							'Please use tf.data to implement this functionality.'  )
def     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple			,							lowercase			: Union[str, Any]=False			,							lowercase			: Dict=10  ):


      '''simple docstring'''



      print('Extracting'			,							f.name  )
      with gzip.GzipFile(fileobj=lowerCamelCase_  ) as bytestream:
            lowerCamelCase_	  =					_readaa(lowerCamelCase_  )
            if magic != 20_49:
                  raise ValueError(
                      'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)  )
            lowerCamelCase_	  =					_readaa(lowerCamelCase_  )
            lowerCamelCase_	  =					bytestream.read(lowerCamelCase_  )
            lowerCamelCase_	  =					numpy.frombuffer(lowerCamelCase_			,							dtype=numpy.uinta  )
            if one_hot:
                  return _dense_to_one_hot(lowerCamelCase_			,							lowerCamelCase_  )
            return labels







class 						A:



 '''simple docstring'''


 @deprecated(
     UpperCAmelCase_		,							'Please use alternatives such as official/mnist/_DataSet.py'
     ' from tensorflow/models.'		,							)
 def __init__(       self      :						Dict		,							A_      :						Optional[int]		,							A_      :						Optional[int]		,							A_      :						List[str]=False		,							A_      :						Optional[int]=False		,							A_      :						Tuple=dtypes.floataa		,							A_      :						int=True		,							A_      :						Union[str, Any]=None		,							)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					random_seed.get_seed(UpperCAmelCase_						)
       # If op level seed is not set, use whatever graph level seed is returned
       numpy.random.seed(seeda if seed is None else seeda						)
       lowerCamelCase_	  =					dtypes.as_dtype(UpperCAmelCase_						).base_dtype
       if dtype not in (dtypes.uinta, dtypes.floataa):
             raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype						)
       if fake_data:
             lowerCamelCase_	  =					10000
             lowerCamelCase_	  =					one_hot
       else:
             assert (
                 images.shape[0] == labels.shape[0]
             ), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
             lowerCamelCase_	  =					images.shape[0]

             # Convert shape from [num examples, rows, columns, depth]
             # to [num examples, rows*columns] (assuming depth == 1)
             if reshape:
                   assert images.shape[3] == 1
                   lowerCamelCase_	  =					images.reshape(
                       images.shape[0]		,							images.shape[1] * images.shape[2]						)
             if dtype == dtypes.floataa:
                   # Convert from [0, 255] -> [0.0, 1.0].
                   lowerCamelCase_	  =					images.astype(numpy.floataa						)
                   lowerCamelCase_	  =					numpy.multiply(UpperCAmelCase_		,							1.0 / 255.0						)
       lowerCamelCase_	  =					images
       lowerCamelCase_	  =					labels
       lowerCamelCase_	  =					0
       lowerCamelCase_	  =					0

 @property
 def        a__				(       self      :						List[Any]						)     ->			str:

       """simple docstring"""






       return self._images

 @property
 def        a__				(       self      :						List[Any]						)     ->			List[str]:

       """simple docstring"""






       return self._labels

 @property
 def        a__				(       self      :						Union[str, Any]						)     ->			Optional[int]:

       """simple docstring"""






       return self._num_examples

 @property
 def        a__				(       self      :						Dict						)     ->			Any:

       """simple docstring"""






       return self._epochs_completed





 def        a__				(       self      :						Optional[int]		,							A_      :						int		,							A_      :						List[Any]=False		,							A_      :						Dict=True						)     ->			List[Any]:

       """simple docstring"""






       if fake_data:
             lowerCamelCase_	  =					[1] * 784
             lowerCamelCase_	  =					[1] + [0] * 9 if self.one_hot else 0
             return (
                 [fake_image for _ in range(UpperCAmelCase_						)],
                 [fake_label for _ in range(UpperCAmelCase_						)],
             )
       lowerCamelCase_	  =					self._index_in_epoch
       # Shuffle for the first epoch
       if self._epochs_completed == 0 and start == 0 and shuffle:
             lowerCamelCase_	  =					numpy.arange(self._num_examples						)
             numpy.random.shuffle(UpperCAmelCase_						)
             lowerCamelCase_	  =					self.images[perma]
             lowerCamelCase_	  =					self.labels[perma]
       # Go to the next epoch
       if start + batch_size > self._num_examples:
             # Finished epoch
             self._epochs_completed += 1
             # Get the rest examples in this epoch
             lowerCamelCase_	  =					self._num_examples - start
             lowerCamelCase_	  =					self._images[start : self._num_examples]
             lowerCamelCase_	  =					self._labels[start : self._num_examples]
             # Shuffle the data
             if shuffle:
                   lowerCamelCase_	  =					numpy.arange(self._num_examples						)
                   numpy.random.shuffle(UpperCAmelCase_						)
                   lowerCamelCase_	  =					self.images[perm]
                   lowerCamelCase_	  =					self.labels[perm]
             # Start next epoch
             lowerCamelCase_	  =					0
             lowerCamelCase_	  =					batch_size - rest_num_examples
             lowerCamelCase_	  =					self._index_in_epoch
             lowerCamelCase_	  =					self._images[start:end]
             lowerCamelCase_	  =					self._labels[start:end]
             return (
                 numpy.concatenate((images_rest_part, images_new_part)		,							axis=0						),
                 numpy.concatenate((labels_rest_part, labels_new_part)		,							axis=0						),
             )
       else:
             self._index_in_epoch += batch_size
             lowerCamelCase_	  =					self._index_in_epoch
             return self._images[start:end], self._labels[start:end]

@deprecated(lowerCamelCase_			,							'Please write your own downloading logic.'  )
def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: Dict			,							lowercase			: int  ):


      '''simple docstring'''



      if not gfile.Exists(lowerCamelCase_  ):
            gfile.MakeDirs(lowerCamelCase_  )
      lowerCamelCase_	  =					os.path.join(lowerCamelCase_			,							lowerCamelCase_  )
      if not gfile.Exists(lowerCamelCase_  ):
            urllib.request.urlretrieve(lowerCamelCase_			,							lowerCamelCase_  )  # noqa: S310
            with gfile.GFile(lowerCamelCase_  ) as f:
                  lowerCamelCase_	  =					f.size()
            print('Successfully downloaded'			,							lowerCamelCase_			,							lowerCamelCase_			,							'bytes.'  )
      return filepath






@deprecated(
    lowerCamelCase_			,							'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'  )
def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]			,							lowercase			: str=False			,							lowercase			: Union[str, Any]=False			,							lowercase			: Union[str, Any]=dtypes.floataa			,							lowercase			: Optional[int]=True			,							lowercase			: Dict=50_00			,							lowercase			: Any=None			,							lowercase			: Tuple=DEFAULT_SOURCE_URL			,							):


      '''simple docstring'''



      if fake_data:

            def fake():
                  return _DataSet(
                      []			,							[]			,							fake_data=lowerCamelCase_			,							one_hot=lowerCamelCase_			,							dtype=lowerCamelCase_			,							seed=lowerCamelCase_  )

            lowerCamelCase_	  =					fake()
            lowerCamelCase_	  =					fake()
            lowerCamelCase_	  =					fake()
            return _Datasets(train=lowerCamelCase_			,							validation=lowerCamelCase_			,							test=lowerCamelCase_  )

      if not source_url:  # empty string check
            lowerCamelCase_	  =					DEFAULT_SOURCE_URL

      lowerCamelCase_	  =					'train-images-idx3-ubyte.gz'
      lowerCamelCase_	  =					'train-labels-idx1-ubyte.gz'
      lowerCamelCase_	  =					't10k-images-idx3-ubyte.gz'
      lowerCamelCase_	  =					't10k-labels-idx1-ubyte.gz'

      lowerCamelCase_	  =					_maybe_download(
          lowerCamelCase_			,							lowerCamelCase_			,							source_url + train_images_file  )
      with gfile.Open(lowerCamelCase_			,							'rb'  ) as f:
            lowerCamelCase_	  =					_extract_images(lowerCamelCase_  )

      lowerCamelCase_	  =					_maybe_download(
          lowerCamelCase_			,							lowerCamelCase_			,							source_url + train_labels_file  )
      with gfile.Open(lowerCamelCase_			,							'rb'  ) as f:
            lowerCamelCase_	  =					_extract_labels(lowerCamelCase_			,							one_hot=lowerCamelCase_  )

      lowerCamelCase_	  =					_maybe_download(
          lowerCamelCase_			,							lowerCamelCase_			,							source_url + test_images_file  )
      with gfile.Open(lowerCamelCase_			,							'rb'  ) as f:
            lowerCamelCase_	  =					_extract_images(lowerCamelCase_  )

      lowerCamelCase_	  =					_maybe_download(
          lowerCamelCase_			,							lowerCamelCase_			,							source_url + test_labels_file  )
      with gfile.Open(lowerCamelCase_			,							'rb'  ) as f:
            lowerCamelCase_	  =					_extract_labels(lowerCamelCase_			,							one_hot=lowerCamelCase_  )

      if not 0 <= validation_size <= len(lowerCamelCase_  ):
            lowerCamelCase_	  =					(
                'Validation size should be between 0 and '
                f"""{len(lowerCamelCase_  )}. Received: {validation_size}."""
            )
            raise ValueError(lowerCamelCase_  )

      lowerCamelCase_	  =					train_images[:validation_size]
      lowerCamelCase_	  =					train_labels[:validation_size]
      lowerCamelCase_	  =					train_images[validation_size:]
      lowerCamelCase_	  =					train_labels[validation_size:]

      lowerCamelCase_	  =					{'dtype': dtype, 'reshape': reshape, 'seed': seed}

      lowerCamelCase_	  =					_DataSet(lowerCamelCase_			,							lowerCamelCase_			,							**lowerCamelCase_  )
      lowerCamelCase_	  =					_DataSet(lowerCamelCase_			,							lowerCamelCase_			,							**lowerCamelCase_  )
      lowerCamelCase_	  =					_DataSet(lowerCamelCase_			,							lowerCamelCase_			,							**lowerCamelCase_  )

      return _Datasets(train=lowerCamelCase_			,							validation=lowerCamelCase_			,							test=lowerCamelCase_  )










import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY







@is_pipeline_test
@require_torch_or_tf
class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		MODEL_FOR_CAUSAL_LM_MAPPING
 UpperCamelCase		     =		TF_MODEL_FOR_CAUSAL_LM_MAPPING

 @require_torch
 def        a__				(       self      :						Any						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					pipeline(task='text-generation'		,							model='sshleifer/tiny-ctrl'		,							framework='pt'						)
       # Using `do_sample=False` to force deterministic output
       lowerCamelCase_	  =					text_generator('This is a test'		,							do_sample=A_						)
       self.assertEqual(
           A_		,							[
               {
                   'generated_text': (
                       'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
                       ' oscope. FiliFili@@'
                   )
               }
           ]		,							)

       lowerCamelCase_	  =					text_generator(['This is a test', 'This is a second test']						)
       self.assertEqual(
           A_		,							[
               [
                   {
                       'generated_text': (
                           'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
                           ' oscope. FiliFili@@'
                       )
                   }
               ],
               [
                   {
                       'generated_text': (
                           'This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'
                           ' oscope. oscope. FiliFili@@'
                       )
                   }
               ],
           ]		,							)

       lowerCamelCase_	  =					text_generator('This is a test'		,							do_sample=A_		,							num_return_sequences=2		,							return_tensors=A_						)
       self.assertEqual(
           A_		,							[
               {'generated_token_ids': ANY(A_						)},
               {'generated_token_ids': ANY(A_						)},
           ]		,							)
       lowerCamelCase_	  =					text_generator.model.config.eos_token_id
       lowerCamelCase_	  =					'<pad>'
       lowerCamelCase_	  =					text_generator(
           ['This is a test', 'This is a second test']		,							do_sample=A_		,							num_return_sequences=2		,							batch_size=2		,							return_tensors=A_		,							)
       self.assertEqual(
           A_		,							[
               [
                   {'generated_token_ids': ANY(A_						)},
                   {'generated_token_ids': ANY(A_						)},
               ],
               [
                   {'generated_token_ids': ANY(A_						)},
                   {'generated_token_ids': ANY(A_						)},
               ],
           ]		,							)

 @require_tf
 def        a__				(       self      :						Optional[int]						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					pipeline(task='text-generation'		,							model='sshleifer/tiny-ctrl'		,							framework='tf'						)

       # Using `do_sample=False` to force deterministic output
       lowerCamelCase_	  =					text_generator('This is a test'		,							do_sample=A_						)
       self.assertEqual(
           A_		,							[
               {
                   'generated_text': (
                       'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
                       ' please,'
                   )
               }
           ]		,							)

       lowerCamelCase_	  =					text_generator(['This is a test', 'This is a second test']		,							do_sample=A_						)
       self.assertEqual(
           A_		,							[
               [
                   {
                       'generated_text': (
                           'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
                           ' please,'
                       )
                   }
               ],
               [
                   {
                       'generated_text': (
                           'This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'
                           ' Cannes 閲閲Cannes Cannes Cannes 攵 please,'
                       )
                   }
               ],
           ]		,							)

 def        a__				(       self      :						Optional[int]		,							A_      :						Dict		,							A_      :						int		,							A_      :						List[str]						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					TextGenerationPipeline(model=A_		,							tokenizer=A_						)
       return text_generator, ["This is a test", "Another test"]

 def        a__				(       self      :						Dict						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					'Hello I believe in'
       lowerCamelCase_	  =					pipeline('text-generation'		,							model='hf-internal-testing/tiny-random-gpt2'						)
       lowerCamelCase_	  =					text_generator(A_						)
       self.assertEqual(
           A_		,							[{'generated_text': 'Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'}]		,							)

       lowerCamelCase_	  =					text_generator(A_		,							stop_sequence=' fe'						)
       self.assertEqual(A_		,							[{'generated_text': 'Hello I believe in fe'}]						)

 def        a__				(       self      :						Any		,							A_      :						Optional[Any]		,							A_      :						Union[str, Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					text_generator.model
       lowerCamelCase_	  =					text_generator.tokenizer

       lowerCamelCase_	  =					text_generator('This is a test'						)
       self.assertEqual(A_		,							[{'generated_text': ANY(A_						)}]						)
       self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'						)						)

       lowerCamelCase_	  =					text_generator('This is a test'		,							return_full_text=A_						)
       self.assertEqual(A_		,							[{'generated_text': ANY(A_						)}]						)
       self.assertNotIn('This is a test'		,							outputs[0]['generated_text']						)

       lowerCamelCase_	  =					pipeline(task='text-generation'		,							model=A_		,							tokenizer=A_		,							return_full_text=A_						)
       lowerCamelCase_	  =					text_generator('This is a test'						)
       self.assertEqual(A_		,							[{'generated_text': ANY(A_						)}]						)
       self.assertNotIn('This is a test'		,							outputs[0]['generated_text']						)

       lowerCamelCase_	  =					text_generator('This is a test'		,							return_full_text=A_						)
       self.assertEqual(A_		,							[{'generated_text': ANY(A_						)}]						)
       self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'						)						)

       lowerCamelCase_	  =					text_generator(['This is great !', 'Something else']		,							num_return_sequences=2		,							do_sample=A_						)
       self.assertEqual(
           A_		,							[
               [{'generated_text': ANY(A_						)}, {'generated_text': ANY(A_						)}],
               [{'generated_text': ANY(A_						)}, {'generated_text': ANY(A_						)}],
           ]		,							)

       if text_generator.tokenizer.pad_token is not None:
             lowerCamelCase_	  =					text_generator(
                 ['This is great !', 'Something else']		,							num_return_sequences=2		,							batch_size=2		,							do_sample=A_						)
             self.assertEqual(
                 A_		,							[
                     [{'generated_text': ANY(A_						)}, {'generated_text': ANY(A_						)}],
                     [{'generated_text': ANY(A_						)}, {'generated_text': ANY(A_						)}],
                 ]		,							)

       with self.assertRaises(A_						):
             lowerCamelCase_	  =					text_generator('test'		,							return_full_text=A_		,							return_text=A_						)
       with self.assertRaises(A_						):
             lowerCamelCase_	  =					text_generator('test'		,							return_full_text=A_		,							return_tensors=A_						)
       with self.assertRaises(A_						):
             lowerCamelCase_	  =					text_generator('test'		,							return_text=A_		,							return_tensors=A_						)

       # Empty prompt is slighly special
       # it requires BOS token to exist.
       # Special case for Pegasus which will always append EOS so will
       # work even without BOS.
       if (
           text_generator.tokenizer.bos_token_id is not None
           or "Pegasus" in tokenizer.__class__.__name__
           or "Git" in model.__class__.__name__
       ):
             lowerCamelCase_	  =					text_generator(''						)
             self.assertEqual(A_		,							[{'generated_text': ANY(A_						)}]						)
       else:
             with self.assertRaises((ValueError, AssertionError)						):
                   lowerCamelCase_	  =					text_generator(''						)

       if text_generator.framework == "tf":
             # TF generation does not support max_new_tokens, and it's impossible
             # to control long generation with only max_length without
             # fancy calculation, dismissing tests for now.
             return
       # We don't care about infinite range models.
       # They already work.
       # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
       lowerCamelCase_	  =					['RwkvForCausalLM', 'XGLMForCausalLM', 'GPTNeoXForCausalLM']
       if (
           tokenizer.model_max_length < 10000
           and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
       ):
             # Handling of large generations
             with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)						):
                   text_generator('This is a test' * 500		,							max_new_tokens=20						)

             lowerCamelCase_	  =					text_generator('This is a test' * 500		,							handle_long_generation='hole'		,							max_new_tokens=20						)
             # Hole strategy cannot work
             with self.assertRaises(A_						):
                   text_generator(
                       'This is a test' * 500		,							handle_long_generation='hole'		,							max_new_tokens=tokenizer.model_max_length + 10		,							)

 @require_torch
 @require_accelerate
 @require_torch_gpu
 def        a__				(       self      :						Union[str, Any]						)     ->			Any:

       """simple docstring"""






       import torch

       # Classic `model_kwargs`
       lowerCamelCase_	  =					pipeline(
           model='hf-internal-testing/tiny-random-bloom'		,							model_kwargs={'device_map': 'auto', 'torch_dtype': torch.bfloataa}		,							)
       self.assertEqual(pipe.model.device		,							torch.device(0						)						)
       self.assertEqual(pipe.model.lm_head.weight.dtype		,							torch.bfloataa						)
       lowerCamelCase_	  =					pipe('This is a test'						)
       self.assertEqual(
           A_		,							[
               {
                   'generated_text': (
                       'This is a test test test test test test test test test test test test test test test test'
                       ' test'
                   )
               }
           ]		,							)

       # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
       lowerCamelCase_	  =					pipeline(model='hf-internal-testing/tiny-random-bloom'		,							device_map='auto'		,							torch_dtype=torch.bfloataa						)
       self.assertEqual(pipe.model.device		,							torch.device(0						)						)
       self.assertEqual(pipe.model.lm_head.weight.dtype		,							torch.bfloataa						)
       lowerCamelCase_	  =					pipe('This is a test'						)
       self.assertEqual(
           A_		,							[
               {
                   'generated_text': (
                       'This is a test test test test test test test test test test test test test test test test'
                       ' test'
                   )
               }
           ]		,							)

       # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
       lowerCamelCase_	  =					pipeline(model='hf-internal-testing/tiny-random-bloom'		,							device_map='auto'						)
       self.assertEqual(pipe.model.device		,							torch.device(0						)						)
       self.assertEqual(pipe.model.lm_head.weight.dtype		,							torch.floataa						)
       lowerCamelCase_	  =					pipe('This is a test'						)
       self.assertEqual(
           A_		,							[
               {
                   'generated_text': (
                       'This is a test test test test test test test test test test test test test test test test'
                       ' test'
                   )
               }
           ]		,							)

 @require_torch
 @require_torch_gpu
 def        a__				(       self      :						int						)     ->			str:

       """simple docstring"""






       import torch

       lowerCamelCase_	  =					pipeline(model='hf-internal-testing/tiny-random-bloom'		,							device=0		,							torch_dtype=torch.floataa						)
       pipe('This is a test'						)

 @require_torch
 @require_accelerate
 @require_torch_gpu
 def        a__				(       self      :						List[Any]						)     ->			Dict:

       """simple docstring"""






       import torch

       lowerCamelCase_	  =					pipeline(model='hf-internal-testing/tiny-random-bloom'		,							device_map='auto'		,							torch_dtype=torch.floataa						)
       pipe('This is a test'		,							do_sample=A_		,							top_p=0.5						)





 def        a__				(       self      :						Tuple						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					'Hello world'
       lowerCamelCase_	  =					pipeline('text-generation'		,							model='hf-internal-testing/tiny-random-gpt2'						)
       if text_generator.model.framework == "tf":
             lowerCamelCase_	  =					logging.get_logger('transformers.generation.tf_utils'						)
       else:
             lowerCamelCase_	  =					logging.get_logger('transformers.generation.utils'						)
       lowerCamelCase_	  =					'Both `max_new_tokens`'  # The beggining of the message to be checked in this test

       # Both are set by the user -> log warning
       with CaptureLogger(A_						) as cl:
             lowerCamelCase_	  =					text_generator(A_		,							max_length=10		,							max_new_tokens=1						)
       self.assertIn(A_		,							cl.out						)

       # The user only sets one -> no warning
       with CaptureLogger(A_						) as cl:
             lowerCamelCase_	  =					text_generator(A_		,							max_new_tokens=1						)
       self.assertNotIn(A_		,							cl.out						)

       with CaptureLogger(A_						) as cl:
             lowerCamelCase_	  =					text_generator(A_		,							max_length=10						)
       self.assertNotIn(A_		,							cl.out						)



