






"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


lowerCamelCase_							      = logging.get_logger(__name__)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							['pixel_values']





	def __init__( self   :					int      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Dict[str, int] = None      ,      lowerCAmelCase_   :					PILImageResampling = PIL.Image.BICUBIC      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Dict[str, int] = None      ,      lowerCAmelCase_   :					Union[int, float] = 1 / 255      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Optional[Union[float, List[float]]] = None      ,      lowerCAmelCase_   :					Optional[Union[float, List[float]]] = None      ,      **lowerCAmelCase_   :					int      ,      )		->     None:
				super().__init__(**SCREAMING_SNAKE_CASE_		)
				UpperCAmelCase_	:					int     	=   size if size is not None else {"height": 256, "width": 256}
				UpperCAmelCase_	:					Optional[int]     	=   get_size_dict(SCREAMING_SNAKE_CASE_		)
				UpperCAmelCase_	:					List[Any]     	=   crop_size if crop_size is not None else {"height": 224, "width": 224}
				UpperCAmelCase_	:					List[Any]     	=   get_size_dict(SCREAMING_SNAKE_CASE_      ,      param_name="crop_size"		)

				UpperCAmelCase_	:					Tuple     	=   do_resize
				UpperCAmelCase_	:					List[Any]     	=   size
				UpperCAmelCase_	:					Optional[Any]     	=   resample
				UpperCAmelCase_	:					Optional[Any]     	=   do_center_crop
				UpperCAmelCase_	:					List[str]     	=   crop_size
				UpperCAmelCase_	:					List[Any]     	=   do_rescale
				UpperCAmelCase_	:					Optional[Any]     	=   rescale_factor
				UpperCAmelCase_	:					Dict     	=   do_normalize
				UpperCAmelCase_	:					List[str]     	=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				UpperCAmelCase_	:					Dict     	=   image_std if image_std is not None else IMAGENET_STANDARD_STD





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					np.ndarray      ,      lowerCAmelCase_   :					Dict[str, int]      ,      lowerCAmelCase_   :					PILImageResampling = PIL.Image.BICUBIC      ,      lowerCAmelCase_   :					Optional[Union[str, ChannelDimension]] = None      ,      **lowerCAmelCase_   :					List[Any]      ,      )		->     np.ndarray:
				UpperCAmelCase_	:					List[Any]     	=   get_size_dict(SCREAMING_SNAKE_CASE_		)
				if "height" not in size or "width" not in size:
							raise ValueError(f"""The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}"""		)
				return resize(
				    SCREAMING_SNAKE_CASE_      ,      size=(size["height"], size["width"])      ,      resample=SCREAMING_SNAKE_CASE_      ,      data_format=SCREAMING_SNAKE_CASE_      ,      **SCREAMING_SNAKE_CASE_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					np.ndarray      ,      lowerCAmelCase_   :					Dict[str, int]      ,      lowerCAmelCase_   :					Optional[Union[str, ChannelDimension]] = None      ,      **lowerCAmelCase_   :					Tuple      ,      )		->     np.ndarray:
				UpperCAmelCase_	:					Dict     	=   get_size_dict(SCREAMING_SNAKE_CASE_		)
				if "height" not in size or "width" not in size:
							raise ValueError(f"""The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}"""		)
				return center_crop(SCREAMING_SNAKE_CASE_      ,      size=(size["height"], size["width"])      ,      data_format=SCREAMING_SNAKE_CASE_      ,      **SCREAMING_SNAKE_CASE_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					np.ndarray      ,      lowerCAmelCase_   :					Union[int, float]      ,      lowerCAmelCase_   :					Optional[Union[str, ChannelDimension]] = None      ,      **lowerCAmelCase_   :					List[str]      ,      )		->     str:
				return rescale(SCREAMING_SNAKE_CASE_      ,      scale=SCREAMING_SNAKE_CASE_      ,      data_format=SCREAMING_SNAKE_CASE_      ,      **SCREAMING_SNAKE_CASE_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					np.ndarray      ,      lowerCAmelCase_   :					Union[float, List[float]]      ,      lowerCAmelCase_   :					Union[float, List[float]]      ,      lowerCAmelCase_   :					Optional[Union[str, ChannelDimension]] = None      ,      **lowerCAmelCase_   :					Optional[int]      ,      )		->     np.ndarray:
				return normalize(SCREAMING_SNAKE_CASE_      ,      mean=SCREAMING_SNAKE_CASE_      ,      std=SCREAMING_SNAKE_CASE_      ,      data_format=SCREAMING_SNAKE_CASE_      ,      **SCREAMING_SNAKE_CASE_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					ImageInput      ,      lowerCAmelCase_   :					bool = None      ,      lowerCAmelCase_   :					Dict[str, int] = None      ,      lowerCAmelCase_   :					Union[str, Any]=None      ,      lowerCAmelCase_   :					bool = None      ,      lowerCAmelCase_   :					Dict[str, int] = None      ,      lowerCAmelCase_   :					bool = None      ,      lowerCAmelCase_   :					float = None      ,      lowerCAmelCase_   :					bool = None      ,      lowerCAmelCase_   :					Optional[Union[float, List[float]]] = None      ,      lowerCAmelCase_   :					Optional[Union[float, List[float]]] = None      ,      lowerCAmelCase_   :					Optional[Union[str, TensorType]] = None      ,      lowerCAmelCase_   :					ChannelDimension = ChannelDimension.FIRST      ,      **lowerCAmelCase_   :					Optional[int]      ,      )		->     PIL.Image.Image:
				UpperCAmelCase_	:					List[Any]     	=   do_resize if do_resize is not None else self.do_resize
				UpperCAmelCase_	:					Dict     	=   resample if resample is not None else self.resample
				UpperCAmelCase_	:					str     	=   do_center_crop if do_center_crop is not None else self.do_center_crop
				UpperCAmelCase_	:					Dict     	=   do_rescale if do_rescale is not None else self.do_rescale
				UpperCAmelCase_	:					Tuple     	=   rescale_factor if rescale_factor is not None else self.rescale_factor
				UpperCAmelCase_	:					List[str]     	=   do_normalize if do_normalize is not None else self.do_normalize
				UpperCAmelCase_	:					Tuple     	=   image_mean if image_mean is not None else self.image_mean
				UpperCAmelCase_	:					Optional[Any]     	=   image_std if image_std is not None else self.image_std

				UpperCAmelCase_	:					str     	=   size if size is not None else self.size
				UpperCAmelCase_	:					str     	=   get_size_dict(SCREAMING_SNAKE_CASE_		)
				UpperCAmelCase_	:					Optional[Any]     	=   crop_size if crop_size is not None else self.crop_size
				UpperCAmelCase_	:					int     	=   get_size_dict(SCREAMING_SNAKE_CASE_      ,      param_name="crop_size"		)

				UpperCAmelCase_	:					List[str]     	=   make_list_of_images(SCREAMING_SNAKE_CASE_		)

				if not valid_images(SCREAMING_SNAKE_CASE_		):
							raise ValueError(
							    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
							    "torch.Tensor, tf.Tensor or jax.ndarray."		)

				if do_resize and size is None or resample is None:
							raise ValueError("Size and resample must be specified if do_resize is True."		)

				if do_center_crop and crop_size is None:
							raise ValueError("Crop size must be specified if do_center_crop is True."		)

				if do_rescale and rescale_factor is None:
							raise ValueError("Rescale factor must be specified if do_rescale is True."		)

				if do_normalize and (image_mean is None or image_std is None):
							raise ValueError("Image mean and std must be specified if do_normalize is True."		)

				# All transformations expect numpy arrays.
				UpperCAmelCase_	:					Tuple     	=   [to_numpy_array(SCREAMING_SNAKE_CASE_		) for image in images]

				if do_resize:
							UpperCAmelCase_	:					List[Any]     	=   [self.resize(image=SCREAMING_SNAKE_CASE_      ,      size=SCREAMING_SNAKE_CASE_      ,      resample=SCREAMING_SNAKE_CASE_		) for image in images]

				if do_center_crop:
							UpperCAmelCase_	:					Dict     	=   [self.center_crop(image=SCREAMING_SNAKE_CASE_      ,      size=SCREAMING_SNAKE_CASE_		) for image in images]

				if do_rescale:
							UpperCAmelCase_	:					Any     	=   [self.rescale(image=SCREAMING_SNAKE_CASE_      ,      scale=SCREAMING_SNAKE_CASE_		) for image in images]

				if do_normalize:
							UpperCAmelCase_	:					Any     	=   [self.normalize(image=SCREAMING_SNAKE_CASE_      ,      mean=SCREAMING_SNAKE_CASE_      ,      std=SCREAMING_SNAKE_CASE_		) for image in images]

				UpperCAmelCase_	:					Optional[int]     	=   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_		) for image in images]

				UpperCAmelCase_	:					Union[str, Any]     	=   {"pixel_values": images}
				return BatchFeature(data=SCREAMING_SNAKE_CASE_      ,      tensor_type=SCREAMING_SNAKE_CASE_		)





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 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	a     :List[Any]       	=      MODEL_FOR_CAUSAL_LM_MAPPING
	a     :Tuple       	=      TF_MODEL_FOR_CAUSAL_LM_MAPPING
	@require_torch
	def 		_lowercase    (			self    :     Tuple			)			->	Dict:
					lowercase_	    =   pipeline(task='''text-generation'''			,				model='''sshleifer/tiny-ctrl'''			,				framework='''pt'''			)
					# Using `do_sample=False` to force deterministic output
					lowercase_	    =   text_generator('''This is a test'''			,				do_sample=SCREAMING_SNAKE_CASE_			)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE_			,				[
					        {
					            '''generated_text''': (
					                '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
					                ''' oscope. FiliFili@@'''
					            )
					        }
					    ]			,				)

					lowercase_	    =   text_generator(['''This is a test''', '''This is a second test''']			)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE_			,				[
					        [
					            {
					                '''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@@'''
					                )
					            }
					        ],
					    ]			,				)

					lowercase_	    =   text_generator('''This is a test'''			,				do_sample=SCREAMING_SNAKE_CASE_			,				num_return_sequences=2			,				return_tensors=SCREAMING_SNAKE_CASE_			)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE_			,				[
					        {'''generated_token_ids''': ANY(SCREAMING_SNAKE_CASE_			)},
					        {'''generated_token_ids''': ANY(SCREAMING_SNAKE_CASE_			)},
					    ]			,				)
					lowercase_	    =   text_generator.model.config.eos_token_id
					lowercase_	    =   '''<pad>'''
					lowercase_	    =   text_generator(
					    ['''This is a test''', '''This is a second test''']			,				do_sample=SCREAMING_SNAKE_CASE_			,				num_return_sequences=2			,				batch_size=2			,				return_tensors=SCREAMING_SNAKE_CASE_			,				)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE_			,				[
					        [
					            {'''generated_token_ids''': ANY(SCREAMING_SNAKE_CASE_			)},
					            {'''generated_token_ids''': ANY(SCREAMING_SNAKE_CASE_			)},
					        ],
					        [
					            {'''generated_token_ids''': ANY(SCREAMING_SNAKE_CASE_			)},
					            {'''generated_token_ids''': ANY(SCREAMING_SNAKE_CASE_			)},
					        ],
					    ]			,				)
	@require_tf
	def 		_lowercase    (			self    :     str			)			->	Optional[int]:
					lowercase_	    =   pipeline(task='''text-generation'''			,				model='''sshleifer/tiny-ctrl'''			,				framework='''tf'''			)

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

					lowercase_	    =   text_generator(['''This is a test''', '''This is a second test''']			,				do_sample=SCREAMING_SNAKE_CASE_			)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE_			,				[
					        [
					            {
					                '''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 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			)			->	Union[str, Any]:
					lowercase_	    =   TextGenerationPipeline(model=SCREAMING_SNAKE_CASE_			,				tokenizer=SCREAMING_SNAKE_CASE_			)
					return text_generator, ["This is a test", "Another test"]
	def 		_lowercase    (			self    :     List[Any]			)			->	Optional[int]:
					lowercase_	    =   '''Hello I believe in'''
					lowercase_	    =   pipeline('''text-generation'''			,				model='''hf-internal-testing/tiny-random-gpt2'''			)
					lowercase_	    =   text_generator(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE_			,				[{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}]			,				)

					lowercase_	    =   text_generator(SCREAMING_SNAKE_CASE_			,				stop_sequence=''' fe'''			)
					self.assertEqual(SCREAMING_SNAKE_CASE_			,				[{'''generated_text''': '''Hello I believe in fe'''}]			)
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     List[str]			)			->	str:
					lowercase_	    =   text_generator.model
					lowercase_	    =   text_generator.tokenizer

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

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

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

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

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

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

					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   text_generator('''test'''			,				return_full_text=SCREAMING_SNAKE_CASE_			,				return_text=SCREAMING_SNAKE_CASE_			)
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   text_generator('''test'''			,				return_full_text=SCREAMING_SNAKE_CASE_			,				return_tensors=SCREAMING_SNAKE_CASE_			)
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   text_generator('''test'''			,				return_text=SCREAMING_SNAKE_CASE_			,				return_tensors=SCREAMING_SNAKE_CASE_			)

					# 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__
					):
									lowercase_	    =   text_generator(''''''			)
									self.assertEqual(SCREAMING_SNAKE_CASE_			,				[{'''generated_text''': ANY(SCREAMING_SNAKE_CASE_			)}]			)
					else:
									with self.assertRaises((ValueError, AssertionError)			):
													lowercase_	    =   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.
					lowercase_	    =   ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
					if (
					    tokenizer.model_max_length < 1_0_0_0_0
					    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''' * 5_0_0			,				max_new_tokens=2_0			)

									lowercase_	    =   text_generator('''This is a test''' * 5_0_0			,				handle_long_generation='''hole'''			,				max_new_tokens=2_0			)
									# Hole strategy cannot work
									with self.assertRaises(SCREAMING_SNAKE_CASE_			):
													text_generator(
													    '''This is a test''' * 5_0_0			,				handle_long_generation='''hole'''			,				max_new_tokens=tokenizer.model_max_length + 1_0			,				)
	@require_torch
	@require_accelerate
	@require_torch_gpu
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Tuple:
					import torch

					# Classic `model_kwargs`
					lowercase_	    =   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			)
					lowercase_	    =   pipe('''This is a test'''			)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE_			,				[
					        {
					            '''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.)
					lowercase_	    =   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			)
					lowercase_	    =   pipe('''This is a test'''			)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE_			,				[
					        {
					            '''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
					lowercase_	    =   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			)
					lowercase_	    =   pipe('''This is a test'''			)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE_			,				[
					        {
					            '''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 		_lowercase    (			self    :     Optional[int]			)			->	List[Any]:
					import torch

					lowercase_	    =   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 		_lowercase    (			self    :     int			)			->	Dict:
					import torch

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




	def 		_lowercase    (			self    :     List[Any]			)			->	int:
					lowercase_	    =   '''Hello world'''
					lowercase_	    =   pipeline('''text-generation'''			,				model='''hf-internal-testing/tiny-random-gpt2'''			)
					if text_generator.model.framework == "tf":
									lowercase_	    =   logging.get_logger('''transformers.generation.tf_utils'''			)
					else:
									lowercase_	    =   logging.get_logger('''transformers.generation.utils'''			)
					lowercase_	    =   '''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(SCREAMING_SNAKE_CASE_			) as cl:
									lowercase_	    =   text_generator(SCREAMING_SNAKE_CASE_			,				max_length=1_0			,				max_new_tokens=1			)
					self.assertIn(SCREAMING_SNAKE_CASE_			,				cl.out			)

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

					with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
									lowercase_	    =   text_generator(SCREAMING_SNAKE_CASE_			,				max_length=1_0			)
					self.assertNotIn(SCREAMING_SNAKE_CASE_			,				cl.out			)




