





import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Union[str, Any]						     =       None
							snake_case     :      int						     =       BloomTokenizerFast
							snake_case     :      Union[str, Any]						     =       BloomTokenizerFast
							snake_case     :      List[Any]						     =       True
							snake_case     :      List[str]						     =       False
							snake_case     :      Dict						     =       """tokenizer_file"""
							snake_case     :      Optional[Any]						     =       {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}






							def 				_lowerCamelCase		(						self  ):
														super().setUp()
														UpperCamelCase__				     =			BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""  )
														tokenizer.save_pretrained(self.tmpdirname  )






							def 				_lowerCamelCase		(						self  , **__lowerCAmelCase  ):
														kwargs.update(self.special_tokens_map  )
														return BloomTokenizerFast.from_pretrained(self.tmpdirname  , **__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.get_rust_tokenizer()

														UpperCamelCase__				     =			["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
														UpperCamelCase__				     =			[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

														UpperCamelCase__				     =			tokenizer.batch_encode_plus(__lowerCAmelCase  )["""input_ids"""]
														self.assertListEqual(__lowerCAmelCase  , __lowerCAmelCase  )

														UpperCamelCase__				     =			tokenizer.batch_decode(__lowerCAmelCase  )
														self.assertListEqual(__lowerCAmelCase  , __lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase=6  ):
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
																												UpperCamelCase__				     =			self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase  , **__lowerCAmelCase  )
																												# tokenizer_r.pad_token = None # Hotfixing padding = None
																												# Simple input
																												UpperCamelCase__				     =			"""This is a simple input"""
																												UpperCamelCase__				     =			["""This is a simple input 1""", """This is a simple input 2"""]
																												UpperCamelCase__				     =			("""This is a simple input""", """This is a pair""")
																												UpperCamelCase__				     =			[
																												    ("""This is a simple input 1""", """This is a simple input 2"""),
																												    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																												]

																												# Simple input tests
																												try:
																																			tokenizer_r.encode(__lowerCAmelCase  , max_length=__lowerCAmelCase  )
																																			tokenizer_r.encode_plus(__lowerCAmelCase  , max_length=__lowerCAmelCase  )

																																			tokenizer_r.batch_encode_plus(__lowerCAmelCase  , max_length=__lowerCAmelCase  )
																																			tokenizer_r.encode(__lowerCAmelCase  , max_length=__lowerCAmelCase  )
																																			tokenizer_r.batch_encode_plus(__lowerCAmelCase  , max_length=__lowerCAmelCase  )
																												except ValueError:
																																			self.fail("""Bloom Tokenizer should be able to deal with padding"""  )

																												UpperCamelCase__				     =			None  # Hotfixing padding = None
																												self.assertRaises(__lowerCAmelCase  , tokenizer_r.encode  , __lowerCAmelCase  , max_length=__lowerCAmelCase  , padding="""max_length"""  )

																												# Simple input
																												self.assertRaises(__lowerCAmelCase  , tokenizer_r.encode_plus  , __lowerCAmelCase  , max_length=__lowerCAmelCase  , padding="""max_length"""  )

																												# Simple input
																												self.assertRaises(
																												    __lowerCAmelCase  , tokenizer_r.batch_encode_plus  , __lowerCAmelCase  , max_length=__lowerCAmelCase  , padding="""max_length"""  , )

																												# Pair input
																												self.assertRaises(__lowerCAmelCase  , tokenizer_r.encode  , __lowerCAmelCase  , max_length=__lowerCAmelCase  , padding="""max_length"""  )

																												# Pair input
																												self.assertRaises(__lowerCAmelCase  , tokenizer_r.encode_plus  , __lowerCAmelCase  , max_length=__lowerCAmelCase  , padding="""max_length"""  )

																												# Pair input
																												self.assertRaises(
																												    __lowerCAmelCase  , tokenizer_r.batch_encode_plus  , __lowerCAmelCase  , max_length=__lowerCAmelCase  , padding="""max_length"""  , )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.get_rust_tokenizer()
														UpperCamelCase__				     =			load_dataset("""xnli"""  , """all_languages"""  , split="""test"""  , streaming=__lowerCAmelCase  )

														UpperCamelCase__				     =			next(iter(__lowerCAmelCase  )  )["""premise"""]  # pick up one data
														UpperCamelCase__				     =			list(sample_data.values()  )

														UpperCamelCase__				     =			list(map(tokenizer.encode  , __lowerCAmelCase  )  )
														UpperCamelCase__				     =			[tokenizer.decode(__lowerCAmelCase  , clean_up_tokenization_spaces=__lowerCAmelCase  ) for x in output_tokens]
														self.assertListEqual(__lowerCAmelCase  , __lowerCAmelCase  )







							def 				_lowerCamelCase		(						self  ):
														# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
														# any sequence length constraints. This test of the parent class will fail since it relies on the
														# maximum sequence length of the positoonal embeddings.
														self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map  )  , 1  )
														self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()  )[0]  )  , 1  )











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        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):
							snake_case     :      Union[str, Any]						     =       MODEL_FOR_CAUSAL_LM_MAPPING
							snake_case     :      List[str]						     =       TF_MODEL_FOR_CAUSAL_LM_MAPPING






							@require_torch
							def 				_lowerCamelCase		(						self  ):
														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=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """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(
														    __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@@"""
														                )
														            }
														        ],
														    ]  , )

														UpperCamelCase__				     =			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  )},
														    ]  , )
														UpperCamelCase__				     =			text_generator.model.config.eos_token_id
														UpperCamelCase__				     =			"""<pad>"""
														UpperCamelCase__				     =			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 				_lowerCamelCase		(						self  ):
														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=__lowerCAmelCase  )
														self.assertEqual(
														    __lowerCAmelCase  , [
														        {
														            """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=__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 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			TextGenerationPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )
														return text_generator, ["This is a test", "Another test"]






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

														UpperCamelCase__				     =			text_generator(__lowerCAmelCase  , stop_sequence=""" fe"""  )
														self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": """Hello I believe in fe"""}]  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			text_generator.model
														UpperCamelCase__				     =			text_generator.tokenizer

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

														UpperCamelCase__				     =			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"""]  )

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

														UpperCamelCase__				     =			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"""  )  )

														UpperCamelCase__				     =			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:
																					UpperCamelCase__				     =			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  ):
																					UpperCamelCase__				     =			text_generator("""test"""  , return_full_text=__lowerCAmelCase  , return_text=__lowerCAmelCase  )
														with self.assertRaises(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			text_generator("""test"""  , return_full_text=__lowerCAmelCase  , return_tensors=__lowerCAmelCase  )
														with self.assertRaises(__lowerCAmelCase  ):
																					UpperCamelCase__				     =			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__
														):
																					UpperCamelCase__				     =			text_generator(""""""  )
																					self.assertEqual(__lowerCAmelCase  , [{"""generated_text""": ANY(__lowerCAmelCase  )}]  )
														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 < 10000
														    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(__lowerCAmelCase  ):
																												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  ):
														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(
														    __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.)
														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(
														    __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
														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(
														    __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 				_lowerCamelCase		(						self  ):
														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 				_lowerCamelCase		(						self  ):
														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=__lowerCAmelCase  , top_p=0.5  )







							def 				_lowerCamelCase		(						self  ):
														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(__lowerCAmelCase  ) as cl:
																					UpperCamelCase__				     =			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:
																					UpperCamelCase__				     =			text_generator(__lowerCAmelCase  , max_new_tokens=1  )
														self.assertNotIn(__lowerCAmelCase  , cl.out  )

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




