






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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
			import PIL

# soft dependency
if is_pytesseract_available():
			import pytesseract

lowerCamelCase_							      = logging.get_logger(__name__)

def 							snake_case (  A__      ,A__      ,A__		):
			return [
			    int(10_00 * (box[0] / width)		),
			    int(10_00 * (box[1] / height)		),
			    int(10_00 * (box[2] / width)		),
			    int(10_00 * (box[3] / height)		),
			]

def 							snake_case (  A__      ,A__      ,A__ = None		):
			UpperCAmelCase_	:					Optional[Any]     	=   tesseract_config if tesseract_config is not None else ""

			# apply OCR
			UpperCAmelCase_	:					Tuple     	=   to_pil_image(snake_case__		)
			UpperCAmelCase_	,      UpperCAmelCase_	:					str     	=   pil_image.size
			UpperCAmelCase_	:					Optional[int]     	=   pytesseract.image_to_data(snake_case__      ,lang=snake_case__      ,output_type="dict"      ,config=snake_case__		)
			UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Any     	=   data["text"], data["left"], data["top"], data["width"], data["height"]

			# filter empty words and corresponding coordinates
			UpperCAmelCase_	:					Optional[Any]     	=   [idx for idx, word in enumerate(snake_case__		) if not word.strip()]
			UpperCAmelCase_	:					Any     	=   [word for idx, word in enumerate(snake_case__		) if idx not in irrelevant_indices]
			UpperCAmelCase_	:					str     	=   [coord for idx, coord in enumerate(snake_case__		) if idx not in irrelevant_indices]
			UpperCAmelCase_	:					Optional[int]     	=   [coord for idx, coord in enumerate(snake_case__		) if idx not in irrelevant_indices]
			UpperCAmelCase_	:					Dict     	=   [coord for idx, coord in enumerate(snake_case__		) if idx not in irrelevant_indices]
			UpperCAmelCase_	:					List[Any]     	=   [coord for idx, coord in enumerate(snake_case__		) if idx not in irrelevant_indices]

			# turn coordinates into (left, top, left+width, top+height) format
			UpperCAmelCase_	:					int     	=   []
			for x, y, w, h in zip(snake_case__      ,snake_case__      ,snake_case__      ,snake_case__		):
						UpperCAmelCase_	:					Optional[int]     	=   [x, y, x + w, y + h]
						actual_boxes.append(snake_case__		)

			# finally, normalize the bounding boxes
			UpperCAmelCase_	:					int     	=   []
			for box in actual_boxes:
						normalized_boxes.append(normalize_box(snake_case__      ,snake_case__      ,snake_case__		)		)

			assert len(snake_case__		) == len(snake_case__		), "Not as many words as there are bounding boxes"

			return words, normalized_boxes







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





	def __init__( self   :					int      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Dict[str, int] = None      ,      lowerCAmelCase_   :					PILImageResampling = PILImageResampling.BILINEAR      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Optional[str] = None      ,      lowerCAmelCase_   :					Optional[str] = ""      ,      **lowerCAmelCase_   :					Optional[Any]      ,      )		->     None:
				super().__init__(**SCREAMING_SNAKE_CASE_		)
				UpperCAmelCase_	:					int     	=   size if size is not None else {"height": 224, "width": 224}
				UpperCAmelCase_	:					str     	=   get_size_dict(SCREAMING_SNAKE_CASE_		)

				UpperCAmelCase_	:					List[str]     	=   do_resize
				UpperCAmelCase_	:					List[Any]     	=   size
				UpperCAmelCase_	:					Dict     	=   resample
				UpperCAmelCase_	:					List[Any]     	=   apply_ocr
				UpperCAmelCase_	:					Union[str, Any]     	=   ocr_lang
				UpperCAmelCase_	:					Optional[int]     	=   tesseract_config





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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					ImageInput      ,      lowerCAmelCase_   :					bool = None      ,      lowerCAmelCase_   :					Dict[str, int] = None      ,      lowerCAmelCase_   :					PILImageResampling = None      ,      lowerCAmelCase_   :					bool = None      ,      lowerCAmelCase_   :					Optional[str] = None      ,      lowerCAmelCase_   :					Optional[str] = None      ,      lowerCAmelCase_   :					Optional[Union[str, TensorType]] = None      ,      lowerCAmelCase_   :					ChannelDimension = ChannelDimension.FIRST      ,      **lowerCAmelCase_   :					Union[str, Any]      ,      )		->     PIL.Image.Image:
				UpperCAmelCase_	:					Optional[Any]     	=   do_resize if do_resize is not None else self.do_resize
				UpperCAmelCase_	:					List[str]     	=   size if size is not None else self.size
				UpperCAmelCase_	:					Union[str, Any]     	=   get_size_dict(SCREAMING_SNAKE_CASE_		)
				UpperCAmelCase_	:					Any     	=   resample if resample is not None else self.resample
				UpperCAmelCase_	:					List[str]     	=   apply_ocr if apply_ocr is not None else self.apply_ocr
				UpperCAmelCase_	:					str     	=   ocr_lang if ocr_lang is not None else self.ocr_lang
				UpperCAmelCase_	:					List[str]     	=   tesseract_config if tesseract_config is not None else self.tesseract_config

				UpperCAmelCase_	:					Optional[Any]     	=   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:
							raise ValueError("Size must be specified if do_resize is True."		)

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

				if apply_ocr:
							requires_backends(self      ,      "pytesseract"		)
							UpperCAmelCase_	:					Union[str, Any]     	=   []
							UpperCAmelCase_	:					Optional[Any]     	=   []
							for image in images:
										UpperCAmelCase_	,      UpperCAmelCase_	:					Union[str, Any]     	=   apply_tesseract(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_		)
										words_batch.append(SCREAMING_SNAKE_CASE_		)
										boxes_batch.append(SCREAMING_SNAKE_CASE_		)

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

				# flip color channels from RGB to BGR (as Detectron2 requires this)
				UpperCAmelCase_	:					str     	=   [flip_channel_order(SCREAMING_SNAKE_CASE_		) for image in images]
				UpperCAmelCase_	:					Optional[Any]     	=   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_		) for image in images]

				UpperCAmelCase_	:					Tuple     	=   BatchFeature(data={"pixel_values": images}      ,      tensor_type=SCREAMING_SNAKE_CASE_		)

				if apply_ocr:
							UpperCAmelCase_	:					List[Any]     	=   words_batch
							UpperCAmelCase_	:					int     	=   boxes_batch
				return data





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			)




