






'''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  _lowercase   (   unittest.TestCase						):
      _a							:						List[str]										=							MODEL_FOR_CAUSAL_LM_MAPPING
      _a							:						Any										=							TF_MODEL_FOR_CAUSAL_LM_MAPPING
      @require_torch
      def 						lowercase__					(     self   ):
       snake_case__  :    int							       =pipeline(task="""text-generation"""				,			model="""sshleifer/tiny-ctrl"""				,			framework="""pt"""   )
       # Using `do_sample=False` to force deterministic output
       snake_case__  :    Optional[Any]							       =text_generator("""This is a test"""				,			do_sample=a   )
       self.assertEqual(
           a				,			[
               {
                   """generated_text""": (
                       """This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
                       """ oscope. FiliFili@@"""
                   )
               }
           ]				,			)

       snake_case__  :    List[str]							       =text_generator(["""This is a test""", """This is a second test"""]   )
       self.assertEqual(
           a				,			[
               [
                   {
                       """generated_text""": (
                           """This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
                           """ oscope. FiliFili@@"""
                       )
                   }
               ],
               [
                   {
                       """generated_text""": (
                           """This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"""
                           """ oscope. oscope. FiliFili@@"""
                       )
                   }
               ],
           ]				,			)

       snake_case__  :    int							       =text_generator("""This is a test"""				,			do_sample=a				,			num_return_sequences=2				,			return_tensors=a   )
       self.assertEqual(
           a				,			[
               {"""generated_token_ids""": ANY(a   )},
               {"""generated_token_ids""": ANY(a   )},
           ]				,			)
       snake_case__  :    Optional[int]							       =text_generator.model.config.eos_token_id
       snake_case__  :    List[str]							       ="""<pad>"""
       snake_case__  :    str							       =text_generator(
           ["""This is a test""", """This is a second test"""]				,			do_sample=a				,			num_return_sequences=2				,			batch_size=2				,			return_tensors=a				,			)
       self.assertEqual(
           a				,			[
               [
                   {"""generated_token_ids""": ANY(a   )},
                   {"""generated_token_ids""": ANY(a   )},
               ],
               [
                   {"""generated_token_ids""": ANY(a   )},
                   {"""generated_token_ids""": ANY(a   )},
               ],
           ]				,			)
      @require_tf
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =pipeline(task="""text-generation"""				,			model="""sshleifer/tiny-ctrl"""				,			framework="""tf"""   )

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

       snake_case__  :    Optional[int]							       =text_generator(["""This is a test""", """This is a second test"""]				,			do_sample=a   )
       self.assertEqual(
           a				,			[
               [
                   {
                       """generated_text""": (
                           """This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
                           """ please,"""
                       )
                   }
               ],
               [
                   {
                       """generated_text""": (
                           """This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"""
                           """ Cannes 閲閲Cannes Cannes Cannes 攵 please,"""
                       )
                   }
               ],
           ]				,			)
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       snake_case__  :    str							       =TextGenerationPipeline(model=a				,			tokenizer=a   )
       return text_generator, ["This is a test", "Another test"]
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       ="""Hello I believe in"""
       snake_case__  :    Any							       =pipeline("""text-generation"""				,			model="""hf-internal-testing/tiny-random-gpt2"""   )
       snake_case__  :    Union[str, Any]							       =text_generator(a   )
       self.assertEqual(
           a				,			[{"""generated_text""": """Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"""}]				,			)

       snake_case__  :    Optional[Any]							       =text_generator(a				,			stop_sequence=""" fe"""   )
       self.assertEqual(a				,			[{"""generated_text""": """Hello I believe in fe"""}]   )
      def 						lowercase__					(     self				,			a				,			a   ):
       snake_case__  :    Optional[Any]							       =text_generator.model
       snake_case__  :    Any							       =text_generator.tokenizer

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

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

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

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

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

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

       with self.assertRaises(a   ):
        snake_case__  :    int							       =text_generator("""test"""				,			return_full_text=a				,			return_text=a   )
       with self.assertRaises(a   ):
        snake_case__  :    Optional[Any]							       =text_generator("""test"""				,			return_full_text=a				,			return_tensors=a   )
       with self.assertRaises(a   ):
        snake_case__  :    Optional[int]							       =text_generator("""test"""				,			return_text=a				,			return_tensors=a   )

       # Empty prompt is slighly special
       # it requires BOS token to exist.
       # Special case for Pegasus which will always append EOS so will
       # work even without BOS.
       if (
           text_generator.tokenizer.bos_token_id is not None
           or "Pegasus" in tokenizer.__class__.__name__
           or "Git" in model.__class__.__name__
       ):
        snake_case__  :    List[Any]							       =text_generator(""""""   )
        self.assertEqual(a				,			[{"""generated_text""": ANY(a   )}]   )
       else:
        with self.assertRaises((ValueError, AssertionError)   ):
         snake_case__  :    List[str]							       =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.
       snake_case__  :    Optional[Any]							       =["""RwkvForCausalLM""", """XGLMForCausalLM""", """GPTNeoXForCausalLM"""]
       if (
           tokenizer.model_max_length < 1_0_0_0_0
           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_0_0				,			max_new_tokens=2_0   )

        snake_case__  :    int							       =text_generator("""This is a test""" * 5_0_0				,			handle_long_generation="""hole"""				,			max_new_tokens=2_0   )
        # Hole strategy cannot work
        with self.assertRaises(a   ):
         text_generator(
             """This is a test""" * 5_0_0				,			handle_long_generation="""hole"""				,			max_new_tokens=tokenizer.model_max_length + 1_0				,			)
      @require_torch
      @require_accelerate
      @require_torch_gpu
      def 						lowercase__					(     self   ):
       import torch

       # Classic `model_kwargs`
       snake_case__  :    List[Any]							       =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   )
       snake_case__  :    List[str]							       =pipe("""This is a test"""   )
       self.assertEqual(
           a				,			[
               {
                   """generated_text""": (
                       """This is a test test test test test test test test test test test test test test test test"""
                       """ test"""
                   )
               }
           ]				,			)

       # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
       snake_case__  :    Union[str, Any]							       =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   )
       snake_case__  :    Optional[int]							       =pipe("""This is a test"""   )
       self.assertEqual(
           a				,			[
               {
                   """generated_text""": (
                       """This is a test test test test test test test test test test test test test test test test"""
                       """ test"""
                   )
               }
           ]				,			)

       # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
       snake_case__  :    Union[str, Any]							       =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   )
       snake_case__  :    int							       =pipe("""This is a test"""   )
       self.assertEqual(
           a				,			[
               {
                   """generated_text""": (
                       """This is a test test test test test test test test test test test test test test test test"""
                       """ test"""
                   )
               }
           ]				,			)
      @require_torch
      @require_torch_gpu
      def 						lowercase__					(     self   ):
       import torch

       snake_case__  :    Tuple							       =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 						lowercase__					(     self   ):
       import torch

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



      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       ="""Hello world"""
       snake_case__  :    Dict							       =pipeline("""text-generation"""				,			model="""hf-internal-testing/tiny-random-gpt2"""   )
       if text_generator.model.framework == "tf":
        snake_case__  :    Union[str, Any]							       =logging.get_logger("""transformers.generation.tf_utils"""   )
       else:
        snake_case__  :    Tuple							       =logging.get_logger("""transformers.generation.utils"""   )
       snake_case__  :    Union[str, Any]							       ="""Both `max_new_tokens`"""  # The beggining of the message to be checked in this test

       # Both are set by the user -> log warning
       with CaptureLogger(a   ) as cl:
        snake_case__  :    Tuple							       =text_generator(a				,			max_length=1_0				,			max_new_tokens=1   )
       self.assertIn(a				,			cl.out   )

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

       with CaptureLogger(a   ) as cl:
        snake_case__  :    Optional[int]							       =text_generator(a				,			max_length=1_0   )
       self.assertNotIn(a				,			cl.out   )













__lowerCamelCase	:		dict[str, float]										=   {
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.60_93_44,
    "knot": 1.8_52,
}

__lowerCamelCase	:		dict[str, float]										=   {
    "km/h": 1.0,
    "m/s": 0.2_77_77_77_78,
    "mph": 0.6_21_37_11_92,
    "knot": 0.5_39_95_68_03,
}







def 			A__							(      _a  :					float		,  _a  :					str		,  _a  :					str	):





 '''simple docstring'''







 if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
  snake_case__  :    Tuple							       =(
      f"Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"
      f"Valid values are: {', '.join(_a	)}"
  )
  raise ValueError(_a	)
 return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to]		,  3	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





