





import unittest

from diffusers.models.unet_ad_blocks import *  # noqa F403
from diffusers.utils import torch_device

from .test_unet_blocks_common import UNetBlockTesterMixin

class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Optional[int]						     =       DownBlockaD  # noqa F405
							snake_case     :      Tuple						     =       """down"""






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[-0.0232, -0.9869, 0.8054, -0.0637, -0.1688, -1.4264, 0.4470, -1.3394, 0.0904]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Dict						     =       ResnetDownsampleBlockaD  # noqa F405
							snake_case     :      str						     =       """down"""






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[0.0710, 0.2410, -0.7320, -1.0757, -1.1343, 0.3540, -0.0133, -0.2576, 0.0948]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      int						     =       AttnDownBlockaD  # noqa F405
							snake_case     :      int						     =       """down"""






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[0.0636, 0.8964, -0.6234, -1.0131, 0.0844, 0.4935, 0.3437, 0.0911, -0.2957]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Tuple						     =       CrossAttnDownBlockaD  # noqa F405
							snake_case     :      Optional[int]						     =       """down"""






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			super().prepare_init_args_and_inputs_for_common()
														UpperCamelCase__				     =			32
														return init_dict, inputs_dict







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[0.2238, -0.7396, -0.2255, -0.3829, 0.1925, 1.1665, 0.0603, -0.7295, 0.1983]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Dict						     =       SimpleCrossAttnDownBlockaD  # noqa F405
							snake_case     :      List[Any]						     =       """down"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_encoder_hidden_states=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			super().prepare_init_args_and_inputs_for_common()
														UpperCamelCase__				     =			32
														return init_dict, inputs_dict







							@unittest.skipIf(torch_device == """mps"""  , """MPS result is not consistent"""  )
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[0.7921, -0.0992, -0.1962, -0.7695, -0.4242, 0.7804, 0.4737, 0.2765, 0.3338]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Dict						     =       SkipDownBlockaD  # noqa F405
							snake_case     :      str						     =       """down"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_skip_sample=__lowerCAmelCase  )







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[-0.0845, -0.2087, -0.2465, 0.0971, 0.1900, -0.0484, 0.2664, 0.4179, 0.5069]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      List[str]						     =       AttnSkipDownBlockaD  # noqa F405
							snake_case     :      Any						     =       """down"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_skip_sample=__lowerCAmelCase  )







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[0.5539, 0.1609, 0.4924, 0.0537, -0.1995, 0.4050, 0.0979, -0.2721, -0.0642]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Union[str, Any]						     =       DownEncoderBlockaD  # noqa F405
							snake_case     :      str						     =       """down"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_temb=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			{
														    """in_channels""": 32,
														    """out_channels""": 32,
														}
														UpperCamelCase__				     =			self.dummy_input
														return init_dict, inputs_dict







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[1.1102, 0.5302, 0.4872, -0.0023, -0.8042, 0.0483, -0.3489, -0.5632, 0.7626]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      str						     =       AttnDownEncoderBlockaD  # noqa F405
							snake_case     :      Optional[int]						     =       """down"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_temb=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			{
														    """in_channels""": 32,
														    """out_channels""": 32,
														}
														UpperCamelCase__				     =			self.dummy_input
														return init_dict, inputs_dict







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[0.8966, -0.1486, 0.8568, 0.8141, -0.9046, -0.1342, -0.0972, -0.7417, 0.1538]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Any						     =       UNetMidBlockaD  # noqa F405
							snake_case     :      Dict						     =       """mid"""






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			{
														    """in_channels""": 32,
														    """temb_channels""": 128,
														}
														UpperCamelCase__				     =			self.dummy_input
														return init_dict, inputs_dict







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[-0.1062, 1.7248, 0.3494, 1.4569, -0.0910, -1.2421, -0.9984, 0.6736, 1.0028]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Dict						     =       UNetMidBlockaDCrossAttn  # noqa F405
							snake_case     :      List[str]						     =       """mid"""






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			super().prepare_init_args_and_inputs_for_common()
														UpperCamelCase__				     =			32
														return init_dict, inputs_dict







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[0.0187, 2.4220, 0.4484, 1.1203, -0.6121, -1.5122, -0.8270, 0.7851, 1.8335]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Dict						     =       UNetMidBlockaDSimpleCrossAttn  # noqa F405
							snake_case     :      List[Any]						     =       """mid"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_encoder_hidden_states=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			super().prepare_init_args_and_inputs_for_common()
														UpperCamelCase__				     =			32
														return init_dict, inputs_dict







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[0.7143, 1.9974, 0.5448, 1.3977, 0.1282, -1.1237, -1.4238, 0.5530, 0.8880]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Optional[int]						     =       UpBlockaD  # noqa F405
							snake_case     :      int						     =       """up"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCAmelCase  )







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[-0.2041, -0.4165, -0.3022, 0.0041, -0.6628, -0.7053, 0.1928, -0.0325, 0.0523]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Optional[Any]						     =       ResnetUpsampleBlockaD  # noqa F405
							snake_case     :      List[Any]						     =       """up"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCAmelCase  )







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[0.2287, 0.3549, -0.1346, 0.4797, -0.1715, -0.9649, 0.7305, -0.5864, -0.6244]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Union[str, Any]						     =       CrossAttnUpBlockaD  # noqa F405
							snake_case     :      int						     =       """up"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			super().prepare_init_args_and_inputs_for_common()
														UpperCamelCase__				     =			32
														return init_dict, inputs_dict







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[-0.1403, -0.3515, -0.0420, -0.1425, 0.3167, 0.5094, -0.2181, 0.5931, 0.5582]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      str						     =       SimpleCrossAttnUpBlockaD  # noqa F405
							snake_case     :      Optional[int]						     =       """up"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCAmelCase  , include_encoder_hidden_states=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			super().prepare_init_args_and_inputs_for_common()
														UpperCamelCase__				     =			32
														return init_dict, inputs_dict







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[0.2645, 0.1480, 0.0909, 0.8044, -0.9758, -0.9083, 0.0994, -1.1453, -0.7402]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Dict						     =       AttnUpBlockaD  # noqa F405
							snake_case     :      Optional[Any]						     =       """up"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCAmelCase  )







							@unittest.skipIf(torch_device == """mps"""  , """MPS result is not consistent"""  )
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[0.0979, 0.1326, 0.0021, 0.0659, 0.2249, 0.0059, 0.1132, 0.5952, 0.1033]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      List[str]						     =       SkipUpBlockaD  # noqa F405
							snake_case     :      Optional[Any]						     =       """up"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCAmelCase  )







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[-0.0893, -0.1234, -0.1506, -0.0332, 0.0123, -0.0211, 0.0566, 0.0143, 0.0362]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      str						     =       AttnSkipUpBlockaD  # noqa F405
							snake_case     :      List[Any]						     =       """up"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_res_hidden_states_tuple=__lowerCAmelCase  )







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[0.0361, 0.0617, 0.2787, -0.0350, 0.0342, 0.3421, -0.0843, 0.0913, 0.3015]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      str						     =       UpDecoderBlockaD  # noqa F405
							snake_case     :      Tuple						     =       """up"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_temb=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			{"""in_channels""": 32, """out_channels""": 32}

														UpperCamelCase__				     =			self.dummy_input
														return init_dict, inputs_dict







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[0.4404, 0.1998, -0.9886, -0.3320, -0.3128, -0.7034, -0.6955, -0.2338, -0.3137]
														super().test_output(__lowerCAmelCase  )






class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      Optional[int]						     =       AttnUpDecoderBlockaD  # noqa F405
							snake_case     :      List[Any]						     =       """up"""






							@property
							def 				_lowerCamelCase		(						self  ):
														return super().get_dummy_input(include_temb=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			{"""in_channels""": 32, """out_channels""": 32}

														UpperCamelCase__				     =			self.dummy_input
														return init_dict, inputs_dict







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			[0.6738, 0.4491, 0.1055, 1.0710, 0.7316, 0.3339, 0.3352, 0.1023, 0.3568]
														super().test_output(__lowerCAmelCase  )











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  )




