'''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 _SCREAMING_SNAKE_CASE(							unittest.TestCase					):
						A_			:							List[Any]     		= MODEL_FOR_CAUSAL_LM_MAPPING
						A_			:							Tuple     		= TF_MODEL_FOR_CAUSAL_LM_MAPPING




						@require_torch
						def      __lowerCamelCase       (    self :       Tuple      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :Any						    = pipeline(task='text-generation'							,							model='sshleifer/tiny-ctrl'							,							framework='pt'      )
													# Using `do_sample=False` to force deterministic output
													SCREAMING_SNAKE_CASE__     :List[str]						    = text_generator('This is a test'							,							do_sample=SCREAMING_SNAKE_CASE_      )
													self.assertEqual(
													    SCREAMING_SNAKE_CASE_							,							[
													        {
													            'generated_text': (
													                'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
													                ' oscope. FiliFili@@'
													            )
													        }
													    ]							,							)

													SCREAMING_SNAKE_CASE__     :Dict						    = text_generator(['This is a test', 'This is a second test']      )
													self.assertEqual(
													    SCREAMING_SNAKE_CASE_							,							[
													        [
													            {
													                '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@@'
													                )
													            }
													        ],
													    ]							,							)

													SCREAMING_SNAKE_CASE__     :Dict						    = text_generator('This is a test'							,							do_sample=SCREAMING_SNAKE_CASE_							,							num_return_sequences=2							,							return_tensors=SCREAMING_SNAKE_CASE_      )
													self.assertEqual(
													    SCREAMING_SNAKE_CASE_							,							[
													        {'generated_token_ids': ANY(SCREAMING_SNAKE_CASE_      )},
													        {'generated_token_ids': ANY(SCREAMING_SNAKE_CASE_      )},
													    ]							,							)
													SCREAMING_SNAKE_CASE__     :Dict						    = text_generator.model.config.eos_token_id
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = '<pad>'
													SCREAMING_SNAKE_CASE__     :List[Any]						    = text_generator(
													    ['This is a test', 'This is a second test']							,							do_sample=SCREAMING_SNAKE_CASE_							,							num_return_sequences=2							,							batch_size=2							,							return_tensors=SCREAMING_SNAKE_CASE_							,							)
													self.assertEqual(
													    SCREAMING_SNAKE_CASE_							,							[
													        [
													            {'generated_token_ids': ANY(SCREAMING_SNAKE_CASE_      )},
													            {'generated_token_ids': ANY(SCREAMING_SNAKE_CASE_      )},
													        ],
													        [
													            {'generated_token_ids': ANY(SCREAMING_SNAKE_CASE_      )},
													            {'generated_token_ids': ANY(SCREAMING_SNAKE_CASE_      )},
													        ],
													    ]							,							)




						@require_tf
						def      __lowerCamelCase       (    self :       str      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :str						    = pipeline(task='text-generation'							,							model='sshleifer/tiny-ctrl'							,							framework='tf'      )

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

													SCREAMING_SNAKE_CASE__     :int						    = text_generator(['This is a test', 'This is a second test']							,							do_sample=SCREAMING_SNAKE_CASE_      )
													self.assertEqual(
													    SCREAMING_SNAKE_CASE_							,							[
													        [
													            {
													                '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 :       Optional[Any]							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       List[str]      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :Tuple						    = TextGenerationPipeline(model=SCREAMING_SNAKE_CASE_							,							tokenizer=SCREAMING_SNAKE_CASE_      )
													return text_generator, ["This is a test", "Another test"]




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 'Hello I believe in'
													SCREAMING_SNAKE_CASE__     :int						    = pipeline('text-generation'							,							model='hf-internal-testing/tiny-random-gpt2'      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = text_generator(SCREAMING_SNAKE_CASE_      )
													self.assertEqual(
													    SCREAMING_SNAKE_CASE_							,							[{'generated_text': 'Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'}]							,							)

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




						def      __lowerCamelCase       (    self :       List[Any]							,							UpperCamelCase_ :       Tuple							,							UpperCamelCase_ :       List[str]      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Dict						    = text_generator.model
													SCREAMING_SNAKE_CASE__     :Tuple						    = text_generator.tokenizer

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

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

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

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

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = text_generator(['This is great !', 'Something else']							,							num_return_sequences=2							,							do_sample=SCREAMING_SNAKE_CASE_      )
													self.assertEqual(
													    SCREAMING_SNAKE_CASE_							,							[
													        [{'generated_text': ANY(SCREAMING_SNAKE_CASE_      )}, {'generated_text': ANY(SCREAMING_SNAKE_CASE_      )}],
													        [{'generated_text': ANY(SCREAMING_SNAKE_CASE_      )}, {'generated_text': ANY(SCREAMING_SNAKE_CASE_      )}],
													    ]							,							)

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

													with self.assertRaises(SCREAMING_SNAKE_CASE_      ):
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = text_generator('test'							,							return_full_text=SCREAMING_SNAKE_CASE_							,							return_text=SCREAMING_SNAKE_CASE_      )
													with self.assertRaises(SCREAMING_SNAKE_CASE_      ):
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = text_generator('test'							,							return_full_text=SCREAMING_SNAKE_CASE_							,							return_tensors=SCREAMING_SNAKE_CASE_      )
													with self.assertRaises(SCREAMING_SNAKE_CASE_      ):
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = text_generator('test'							,							return_text=SCREAMING_SNAKE_CASE_							,							return_tensors=SCREAMING_SNAKE_CASE_      )

													# 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__
													):
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = text_generator(''      )
																				self.assertEqual(SCREAMING_SNAKE_CASE_							,							[{'generated_text': ANY(SCREAMING_SNAKE_CASE_      )}]      )
													else:
																				with self.assertRaises((ValueError, AssertionError)      ):
																											SCREAMING_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.
													SCREAMING_SNAKE_CASE__     :int						    = ['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      )

																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = text_generator('This is a test' * 5_00							,							handle_long_generation='hole'							,							max_new_tokens=20      )
																				# Hole strategy cannot work
																				with self.assertRaises(SCREAMING_SNAKE_CASE_      ):
																											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      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Tuple:
													import torch

													# Classic `model_kwargs`
													SCREAMING_SNAKE_CASE__     :Union[str, 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      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = pipe('This is a test'      )
													self.assertEqual(
													    SCREAMING_SNAKE_CASE_							,							[
													        {
													            '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.)
													SCREAMING_SNAKE_CASE__     :Tuple						    = 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      )
													SCREAMING_SNAKE_CASE__     :Dict						    = pipe('This is a test'      )
													self.assertEqual(
													    SCREAMING_SNAKE_CASE_							,							[
													        {
													            '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
													SCREAMING_SNAKE_CASE__     :List[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      )
													SCREAMING_SNAKE_CASE__     :Any						    = pipe('This is a test'      )
													self.assertEqual(
													    SCREAMING_SNAKE_CASE_							,							[
													        {
													            '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 :       Optional[int]      )     ->				List[Any]:
													import torch

													SCREAMING_SNAKE_CASE__     :List[Any]						    = 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 :       int      )     ->				Dict:
													import torch

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




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Any						    = 'Hello world'
													SCREAMING_SNAKE_CASE__     :Any						    = pipeline('text-generation'							,							model='hf-internal-testing/tiny-random-gpt2'      )
													if text_generator.model.framework == "tf":
																				SCREAMING_SNAKE_CASE__     :int						    = logging.get_logger('transformers.generation.tf_utils'      )
													else:
																				SCREAMING_SNAKE_CASE__     :str						    = logging.get_logger('transformers.generation.utils'      )
													SCREAMING_SNAKE_CASE__     :Dict						    = '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(SCREAMING_SNAKE_CASE_      ) as cl:
																				SCREAMING_SNAKE_CASE__     :int						    = text_generator(SCREAMING_SNAKE_CASE_							,							max_length=10							,							max_new_tokens=1      )
													self.assertIn(SCREAMING_SNAKE_CASE_							,							cl.out      )

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

													with CaptureLogger(SCREAMING_SNAKE_CASE_      ) as cl:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = text_generator(SCREAMING_SNAKE_CASE_							,							max_length=10      )
													self.assertNotIn(SCREAMING_SNAKE_CASE_							,							cl.out      )








from __future__ import annotations

from math import pi, sqrt




def 							a		(				snake_case__:   float		,  snake_case__:   float   ):


				'''simple docstring'''

				if inductance <= 0:
								raise ValueError('''Inductance cannot be 0 or negative'''   )

				elif capacitance <= 0:
								raise ValueError('''Capacitance cannot be 0 or negative'''   )

				else:
								return (
								    "Resonant frequency",
								    float(1 / (2 * pi * (sqrt(inductance * capacitance   )))   ),
								)


if __name__ == "__main__":
						import doctest

						doctest.testmod()




