




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       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







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

								a										=							text_generator(["""This is a test""", """This is a second test"""]				)
								self.assertEqual(
								    __magic_name__		,      [
								        [
								            {
								                """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@@"""
								                )
								            }
								        ],
								    ]		,      )

								a										=							text_generator("""This is a test"""		,      do_sample=__magic_name__		,      num_return_sequences=2		,      return_tensors=__magic_name__				)
								self.assertEqual(
								    __magic_name__		,      [
								        {"""generated_token_ids""": ANY(__magic_name__				)},
								        {"""generated_token_ids""": ANY(__magic_name__				)},
								    ]		,      )
								a										=							text_generator.model.config.eos_token_id
								a										=							"""<pad>"""
								a										=							text_generator(
								    ["""This is a test""", """This is a second test"""]		,      do_sample=__magic_name__		,      num_return_sequences=2		,      batch_size=2		,      return_tensors=__magic_name__		,      )
								self.assertEqual(
								    __magic_name__		,      [
								        [
								            {"""generated_token_ids""": ANY(__magic_name__				)},
								            {"""generated_token_ids""": ANY(__magic_name__				)},
								        ],
								        [
								            {"""generated_token_ids""": ANY(__magic_name__				)},
								            {"""generated_token_ids""": ANY(__magic_name__				)},
								        ],
								    ]		,      )
							@require_tf
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







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

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

								a										=							text_generator(["""This is a test""", """This is a second test"""]		,      do_sample=__magic_name__				)
								self.assertEqual(
								    __magic_name__		,      [
								        [
								            {
								                """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       lowerCamelCase__							(						self						:List[str]		,      __magic_name__						:str		,      __magic_name__						:int		,      __magic_name__						:Any				):







								'''simple docstring'''







								a										=							TextGenerationPipeline(model=__magic_name__		,      tokenizer=__magic_name__				)
								return text_generator, ["This is a test", "Another test"]
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







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

								a										=							text_generator(__magic_name__		,      stop_sequence=""" fe"""				)
								self.assertEqual(__magic_name__		,      [{"""generated_text""": """Hello I believe in fe"""}]				)
							def       lowerCamelCase__							(						self						:Optional[Any]		,      __magic_name__						:str		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							text_generator.model
								a										=							text_generator.tokenizer

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

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

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

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

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

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

								with self.assertRaises(__magic_name__				):
									a										=							text_generator("""test"""		,      return_full_text=__magic_name__		,      return_text=__magic_name__				)
								with self.assertRaises(__magic_name__				):
									a										=							text_generator("""test"""		,      return_full_text=__magic_name__		,      return_tensors=__magic_name__				)
								with self.assertRaises(__magic_name__				):
									a										=							text_generator("""test"""		,      return_text=__magic_name__		,      return_tensors=__magic_name__				)

								# 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__
								):
									a										=							text_generator(""""""				)
									self.assertEqual(__magic_name__		,      [{"""generated_text""": ANY(__magic_name__				)}]				)
								else:
									with self.assertRaises((ValueError, AssertionError)				):
										a										=							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.
								a										=							["""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				)

									a										=							text_generator("""This is a test""" * 500		,      handle_long_generation="""hole"""		,      max_new_tokens=20				)
									# Hole strategy cannot work
									with self.assertRaises(__magic_name__				):
										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       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								import torch

								# Classic `model_kwargs`
								a										=							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				)
								a										=							pipe("""This is a test"""				)
								self.assertEqual(
								    __magic_name__		,      [
								        {
								            """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.)
								a										=							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				)
								a										=							pipe("""This is a test"""				)
								self.assertEqual(
								    __magic_name__		,      [
								        {
								            """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
								a										=							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				)
								a										=							pipe("""This is a test"""				)
								self.assertEqual(
								    __magic_name__		,      [
								        {
								            """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       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								import torch

								a										=							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       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								import torch

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





							def       lowerCamelCase__							(						self						:Optional[Any]				):







								'''simple docstring'''







								a										=							"""Hello world"""
								a										=							pipeline("""text-generation"""		,      model="""hf-internal-testing/tiny-random-gpt2"""				)
								if text_generator.model.framework == "tf":
									a										=							logging.get_logger("""transformers.generation.tf_utils"""				)
								else:
									a										=							logging.get_logger("""transformers.generation.utils"""				)
								a										=							"""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(__magic_name__				) as cl:
									a										=							text_generator(__magic_name__		,      max_length=10		,      max_new_tokens=1				)
								self.assertIn(__magic_name__		,      cl.out				)

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

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







from __future__ import annotations





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase = None    )      ->       list[list[str]]:
	a										=							word_bank or []
	# create a table
	a										=							len(__lowerCamelCase    ) + 1

	a										=							[]
	for _ in range(__lowerCamelCase    ):
		table.append([]    )
	# seed value
	a										=							[[]]  # because empty string has empty combination

	# iterate through the indices
	for i in range(__lowerCamelCase    ):
		# condition
		if table[i] != []:
			for word in word_bank:
				# slice condition
				if target[i : i + len(__lowerCamelCase    )] == word:
					a										=							[
					    [word, *way] for way in table[i]
					]
					# adds the word to every combination the current position holds
					# now,push that combination to the table[i+len(word)]
					table[i + len(__lowerCamelCase    )] += new_combinations

    # combinations are in reverse order so reverse for better output
	for combination in table[len(__lowerCamelCase    )]:
		combination.reverse()

	return table[len(__lowerCamelCase    )]


if __name__ == "__main__":
		print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
		print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
		print(
		    all_construct(
		        "hexagonosaurus",
		        ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
		    )
		)

