


'''simple docstring'''






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'''






							lowercase__     :  List[Any]      		= MODEL_FOR_CAUSAL_LM_MAPPING
							lowercase__     :  str      		= TF_MODEL_FOR_CAUSAL_LM_MAPPING







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







							@require_tf
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														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=lowerCamelCase_  )
														self.assertEqual(
														    lowerCamelCase_   ,       [
														        {
														            '''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=lowerCamelCase_  )
														self.assertEqual(
														    lowerCamelCase_   ,       [
														        [
														            {
														                '''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     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Tuple:
														lowerCAmelCase__			  =      TextGenerationPipeline(model=lowerCamelCase_   ,       tokenizer=lowerCamelCase_  )
														return text_generator, ["This is a test", "Another test"]







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

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







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[Any]:
														lowerCAmelCase__			  =      text_generator.model
														lowerCAmelCase__			  =      text_generator.tokenizer

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

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

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

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

														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=lowerCamelCase_  )
																					self.assertEqual(
																					    lowerCamelCase_   ,       [
																					        [{'''generated_text''': ANY(lowerCamelCase_  )}, {'''generated_text''': ANY(lowerCamelCase_  )}],
																					        [{'''generated_text''': ANY(lowerCamelCase_  )}, {'''generated_text''': ANY(lowerCamelCase_  )}],
																					    ]   ,       )

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

														# 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(lowerCamelCase_   ,       [{'''generated_text''': ANY(lowerCamelCase_  )}]  )
														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 < 1_00_00
														    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_00   ,       max_new_tokens=20  )

																					lowerCAmelCase__			  =      text_generator('''This is a test''' * 5_00   ,       handle_long_generation='''hole'''   ,       max_new_tokens=20  )
																					# Hole strategy cannot work
																					with self.assertRaises(lowerCamelCase_  ):
																												text_generator(
																												    '''This is a test''' * 5_00   ,       handle_long_generation='''hole'''   ,       max_new_tokens=tokenizer.model_max_length + 10   ,       )







							@require_torch
							@require_accelerate
							@require_torch_gpu
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														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(
														    lowerCamelCase_   ,       [
														        {
														            '''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(
														    lowerCamelCase_   ,       [
														        {
														            '''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(
														    lowerCamelCase_   ,       [
														        {
														            '''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     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														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     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														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=lowerCamelCase_   ,       top_p=0.5  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														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(lowerCamelCase_  ) as cl:
																					lowerCAmelCase__			  =      text_generator(lowerCamelCase_   ,       max_length=10   ,       max_new_tokens=1  )
														self.assertIn(lowerCamelCase_   ,       cl.out  )

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

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



'''simple docstring'''






from __future__ import annotations

__UpperCAmelCase       							=				list[list[int]]

# assigning initial values to the grid
__UpperCAmelCase       							=				[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
__UpperCAmelCase       							=				[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]


def    _snake_case  (	A					,       A					,       A					,       A			)		->       bool:
							for i in range(9			):
														if grid[row][i] == n or grid[i][column] == n:
																					return False

							for i in range(3			):
														for j in range(3			):
																					if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
																												return False

							return True


def    _snake_case  (	A			)		->       tuple[int, int] | None:
							for i in range(9			):
														for j in range(9			):
																					if grid[i][j] == 0:
																												return i, j
							return None


def    _snake_case  (	A			)		->       Matrix | None:
							if location := find_empty_location(A			):
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      location
							else:
														# If the location is ``None``, then the grid is solved.
														return grid

							for digit in range(1					,       10			):
														if is_safe(A					,       A					,       A					,       A			):
																					lowerCAmelCase__			  =      digit

																					if sudoku(A			) is not None:
																												return grid

																					lowerCAmelCase__			  =      0

							return None


def    _snake_case  (	A			)		->       None:
							for row in grid:
														for cell in row:
																					print(A					,       end=''' '''			)
														print()


if __name__ == "__main__":
		# make a copy of grid so that you can compare with the unmodified grid
		for example_grid in (initial_grid, no_solution):
				print('''\nExample grid:\n''' + '''=''' * 20)
				print_solution(example_grid)
				print('''\nExample grid solution:''')
				__UpperCAmelCase       							=				sudoku(example_grid)
				if solution is not None:
						print_solution(solution)
				else:
						print('''Cannot find a solution.''')