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    _UpperCamelCase      (						unittest.TestCase ):


			"""simple docstring"""






			__a  : int			 =						MODEL_FOR_CAUSAL_LM_MAPPING
			__a  : Any			 =						TF_MODEL_FOR_CAUSAL_LM_MAPPING





			@require_torch
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							__lowercase 			=							pipeline(task='''text-generation'''      ,   model='''sshleifer/tiny-ctrl'''      ,   framework='''pt'''						)
							# Using `do_sample=False` to force deterministic output
							__lowercase 			=							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@@'''
							            )
							        }
							    ]      ,   )

							__lowercase 			=							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@@'''
							                )
							            }
							        ],
							    ]      ,   )

							__lowercase 			=							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__						)},
							    ]      ,   )
							__lowercase 			=							text_generator.model.config.eos_token_id
							__lowercase 			=							'''<pad>'''
							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


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

							# Using `do_sample=False` to force deterministic output
							__lowercase 			=							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,'''
							            )
							        }
							    ]      ,   )

							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							TextGenerationPipeline(model=lowerCAmelCase__      ,   tokenizer=lowerCAmelCase__						)
							return text_generator, ["This is a test", "Another test"]





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


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

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





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							text_generator.model
							__lowercase 			=							text_generator.tokenizer

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

							__lowercase 			=							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''']						)

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

							__lowercase 			=							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'''						)						)

							__lowercase 			=							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:
											__lowercase 			=							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__						):
											__lowercase 			=							text_generator('''test'''      ,   return_full_text=lowerCAmelCase__      ,   return_text=lowerCAmelCase__						)
							with self.assertRaises(lowerCAmelCase__						):
											__lowercase 			=							text_generator('''test'''      ,   return_full_text=lowerCAmelCase__      ,   return_tensors=lowerCAmelCase__						)
							with self.assertRaises(lowerCAmelCase__						):
											__lowercase 			=							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__
							):
											__lowercase 			=							text_generator(''''''						)
											self.assertEqual(lowerCAmelCase__      ,   [{'''generated_text''': ANY(lowerCAmelCase__						)}]						)
							else:
											with self.assertRaises((ValueError, AssertionError)						):
															__lowercase 			=							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.
							__lowercase 			=							['''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						)

											__lowercase 			=							text_generator('''This is a test''' * 5_00      ,   handle_long_generation='''hole'''      ,   max_new_tokens=20						)
											# Hole strategy cannot work
											with self.assertRaises(lowerCAmelCase__						):
															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        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							import torch

							# Classic `model_kwargs`
							__lowercase 			=							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						)
							__lowercase 			=							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.)
							__lowercase 			=							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						)
							__lowercase 			=							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
							__lowercase 			=							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						)
							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


							import torch

							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							import torch

							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							'''Hello world'''
							__lowercase 			=							pipeline('''text-generation'''      ,   model='''hf-internal-testing/tiny-random-gpt2'''						)
							if text_generator.model.framework == "tf":
											__lowercase 			=							logging.get_logger('''transformers.generation.tf_utils'''						)
							else:
											__lowercase 			=							logging.get_logger('''transformers.generation.utils'''						)
							__lowercase 			=							'''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:
											__lowercase 			=							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:
											__lowercase 			=							text_generator(lowerCAmelCase__      ,   max_new_tokens=1						)
							self.assertNotIn(lowerCAmelCase__      ,   cl.out						)

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





from math import pi, sqrt





def 		__A		(	__lowerCamelCase    )      ->       float:
	if num <= 0:
		raise ValueError("""math domain error"""    )
	if num > 171.5:
		raise OverflowError("""math range error"""    )
	elif num - int(__lowerCamelCase    ) not in (0, 0.5):
		raise NotImplementedError("""num must be an integer or a half-integer"""    )
	elif num == 0.5:
		return sqrt(__lowerCamelCase    )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1    )





def 		__A		(	)      ->       None:
	assert gamma(0.5    ) == sqrt(__lowerCamelCase    )
	assert gamma(1    ) == 1.0
	assert gamma(2    ) == 1.0


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		__UpperCamelCase					:       str    					=      1.0
		while num:
				__UpperCamelCase					:       Dict    					=      float(input("Gamma of: "))
				print(F'gamma({num}) = {gamma(num)}')
				print("\nEnter 0 to exit...")

