from collections.abc import Callable
class     lowerCAmelCase_     :







							def __init__(   self      :     Tuple ,			_A      :     Callable | None = None		):
												# Stores actual heap items.
												_UpperCamelCase			     =     []
												# Stores indexes of each item for supporting updates and deletion.
												_UpperCamelCase			     =     {}
												# Stores current size of heap.
												_UpperCamelCase			     =     0
												# Stores function used to evaluate the score of an item on which basis ordering
												# will be done.
												_UpperCamelCase			     =     key or (lambda _A		: x)







							def  UpperCamelCase_						(   self      :     List[Any] ,			_A      :     int		):
												return int((i - 1) / 2		) if i > 0 else None







							def  UpperCamelCase_						(   self      :     str ,			_A      :     int		):
												_UpperCamelCase			     =     int(2 * i + 1		)
												return left if 0 < left < self.size else None







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     int		):
												_UpperCamelCase			     =     int(2 * i + 2		)
												return right if 0 < right < self.size else None







							def  UpperCamelCase_						(   self      :     List[Any] ,			_A      :     int ,			_A      :     int		):
												_UpperCamelCase     ,    _UpperCamelCase			     =     (
												    self.pos_map[self.arr[j][0]],
												    self.pos_map[self.arr[i][0]],
												)
												# Then swap the items in the list.
												_UpperCamelCase     ,    _UpperCamelCase			     =     self.arr[j], self.arr[i]







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     int ,			_A      :     int		):
												return self.arr[i][1] < self.arr[j][1]







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     int		):
												_UpperCamelCase			     =     self._left(_A		)
												_UpperCamelCase			     =     self._right(_A		)
												_UpperCamelCase			     =     i

												if left is not None and not self._cmp(_A ,			_A		):
																	_UpperCamelCase			     =     left
												if right is not None and not self._cmp(_A ,			_A		):
																	_UpperCamelCase			     =     right

												return valid_parent







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     int		):
												_UpperCamelCase			     =     self._parent(_A		)
												while parent is not None and not self._cmp(_A ,			_A		):
																	self._swap(_A ,			_A		)
																	_UpperCamelCase     ,    _UpperCamelCase			     =     parent, self._parent(_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     int		):
												_UpperCamelCase			     =     self._get_valid_parent(_A		)
												while valid_parent != index:
																	self._swap(_A ,			_A		)
																	_UpperCamelCase     ,    _UpperCamelCase			     =     valid_parent, self._get_valid_parent(_A		)







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     int ,			_A      :     int		):
												if item not in self.pos_map:
																	return
												_UpperCamelCase			     =     self.pos_map[item]
												_UpperCamelCase			     =     [item, self.key(_A		)]
												# Make sure heap is right in both up and down direction.
												# Ideally only one of them will make any change.
												self._heapify_up(_A		)
												self._heapify_down(_A		)







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     int		):
												if item not in self.pos_map:
																	return
												_UpperCamelCase			     =     self.pos_map[item]
												del self.pos_map[item]
												_UpperCamelCase			     =     self.arr[self.size - 1]
												_UpperCamelCase			     =     index
												self.size -= 1
												# Make sure heap is right in both up and down direction. Ideally only one
												# of them will make any change- so no performance loss in calling both.
												if self.size > index:
																	self._heapify_up(_A		)
																	self._heapify_down(_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     int ,			_A      :     int		):
												_UpperCamelCase			     =     len(self.arr		)
												if arr_len == self.size:
																	self.arr.append([item, self.key(_A		)]		)
												else:
																	_UpperCamelCase			     =     [item, self.key(_A		)]
												_UpperCamelCase			     =     self.size
												self.size += 1
												self._heapify_up(self.size - 1		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												return self.arr[0] if self.size else None







							def  UpperCamelCase_						(   self      :     Optional[int]		):
												_UpperCamelCase			     =     self.get_top()
												if top_item_tuple:
																	self.delete_item(top_item_tuple[0]		)
												return top_item_tuple




def 					_snake_case	(  ):
					pass


if __name__ == "__main__":
					import doctest

					doctest.testmod()

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     lowerCAmelCase_     (       unittest.TestCase ):
							UpperCAmelCase				   =     MODEL_FOR_CAUSAL_LM_MAPPING
							UpperCAmelCase				   =     TF_MODEL_FOR_CAUSAL_LM_MAPPING







							@require_torch
							def  UpperCamelCase_						(   self      :     str		):
												_UpperCamelCase			     =     pipeline(task='''text-generation''' ,			model='''sshleifer/tiny-ctrl''' ,			framework='''pt'''		)
												# Using `do_sample=False` to force deterministic output
												_UpperCamelCase			     =     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@@'''
												            )
												        }
												    ] ,			)

												_UpperCamelCase			     =     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@@'''
												                )
												            }
												        ],
												    ] ,			)

												_UpperCamelCase			     =     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		)},
												    ] ,			)
												_UpperCamelCase			     =     text_generator.model.config.eos_token_id
												_UpperCamelCase			     =     '''<pad>'''
												_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     pipeline(task='''text-generation''' ,			model='''sshleifer/tiny-ctrl''' ,			framework='''tf'''		)

												# Using `do_sample=False` to force deterministic output
												_UpperCamelCase			     =     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,'''
												            )
												        }
												    ] ,			)

												_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     int ,			_A      :     str ,			_A      :     Union[str, Any] ,			_A      :     Any		):
												_UpperCamelCase			     =     TextGenerationPipeline(model=_A ,			tokenizer=_A		)
												return text_generator, ["This is a test", "Another test"]







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     '''Hello I believe in'''
												_UpperCamelCase			     =     pipeline('''text-generation''' ,			model='''hf-internal-testing/tiny-random-gpt2'''		)
												_UpperCamelCase			     =     text_generator(_A		)
												self.assertEqual(
												    _A ,			[{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}] ,			)

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







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     List[Any] ,			_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     text_generator.model
												_UpperCamelCase			     =     text_generator.tokenizer

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

												_UpperCamelCase			     =     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''']		)

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

												_UpperCamelCase			     =     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'''		)		)

												_UpperCamelCase			     =     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:
																	_UpperCamelCase			     =     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		):
																	_UpperCamelCase			     =     text_generator('''test''' ,			return_full_text=_A ,			return_text=_A		)
												with self.assertRaises(_A		):
																	_UpperCamelCase			     =     text_generator('''test''' ,			return_full_text=_A ,			return_tensors=_A		)
												with self.assertRaises(_A		):
																	_UpperCamelCase			     =     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__
												):
																	_UpperCamelCase			     =     text_generator(''''''		)
																	self.assertEqual(_A ,			[{'''generated_text''': ANY(_A		)}]		)
												else:
																	with self.assertRaises((ValueError, AssertionError)		):
																						_UpperCamelCase			     =     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.
												_UpperCamelCase			     =     ['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
												if (
												    tokenizer.model_max_length < 1_0000
												    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		)

																	_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     Optional[int]		):
												import torch

												# Classic `model_kwargs`
												_UpperCamelCase			     =     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		)
												_UpperCamelCase			     =     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.)
												_UpperCamelCase			     =     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		)
												_UpperCamelCase			     =     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
												_UpperCamelCase			     =     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		)
												_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     Union[str, Any]		):
												import torch

												_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     Optional[int]		):
												import torch

												_UpperCamelCase			     =     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  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     '''Hello world'''
												_UpperCamelCase			     =     pipeline('''text-generation''' ,			model='''hf-internal-testing/tiny-random-gpt2'''		)
												if text_generator.model.framework == "tf":
																	_UpperCamelCase			     =     logging.get_logger('''transformers.generation.tf_utils'''		)
												else:
																	_UpperCamelCase			     =     logging.get_logger('''transformers.generation.utils'''		)
												_UpperCamelCase			     =     '''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:
																	_UpperCamelCase			     =     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:
																	_UpperCamelCase			     =     text_generator(_A ,			max_new_tokens=1		)
												self.assertNotIn(_A ,			cl.out		)

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