





'''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 				__snake_case     (					unittest.TestCase							):
	__lowerCAmelCase       						=  MODEL_FOR_CAUSAL_LM_MAPPING
	__lowerCAmelCase       						=  TF_MODEL_FOR_CAUSAL_LM_MAPPING





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

		snake_case__	       =   text_generator(['This is a test', 'This is a second test']		)
		self.assertEqual(
		    UpperCamelCase_      ,       [
		        [
		            {
		                '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__	       =   text_generator('This is a test'      ,       do_sample=UpperCamelCase_      ,       num_return_sequences=2      ,       return_tensors=UpperCamelCase_		)
		self.assertEqual(
		    UpperCamelCase_      ,       [
		        {'generated_token_ids': ANY(UpperCamelCase_		)},
		        {'generated_token_ids': ANY(UpperCamelCase_		)},
		    ]      ,       )
		snake_case__	       =   text_generator.model.config.eos_token_id
		snake_case__	       =   '<pad>'
		snake_case__	       =   text_generator(
		    ['This is a test', 'This is a second test']      ,       do_sample=UpperCamelCase_      ,       num_return_sequences=2      ,       batch_size=2      ,       return_tensors=UpperCamelCase_      ,       )
		self.assertEqual(
		    UpperCamelCase_      ,       [
		        [
		            {'generated_token_ids': ANY(UpperCamelCase_		)},
		            {'generated_token_ids': ANY(UpperCamelCase_		)},
		        ],
		        [
		            {'generated_token_ids': ANY(UpperCamelCase_		)},
		            {'generated_token_ids': ANY(UpperCamelCase_		)},
		        ],
		    ]      ,       )





	@require_tf
	def   _snake_case   (		self		)      ->		int:
		snake_case__	       =   pipeline(task='text-generation'      ,       model='sshleifer/tiny-ctrl'      ,       framework='tf'		)

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

		snake_case__	       =   text_generator(['This is a test', 'This is a second test']      ,       do_sample=UpperCamelCase_		)
		self.assertEqual(
		    UpperCamelCase_      ,       [
		        [
		            {
		                '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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[int]:
		snake_case__	       =   TextGenerationPipeline(model=UpperCamelCase_      ,       tokenizer=UpperCamelCase_		)
		return text_generator, ["This is a test", "Another test"]





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

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





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		str:
		snake_case__	       =   text_generator.model
		snake_case__	       =   text_generator.tokenizer

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

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

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

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

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

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

		with self.assertRaises(UpperCamelCase_		):
			snake_case__	       =   text_generator('test'      ,       return_full_text=UpperCamelCase_      ,       return_text=UpperCamelCase_		)
		with self.assertRaises(UpperCamelCase_		):
			snake_case__	       =   text_generator('test'      ,       return_full_text=UpperCamelCase_      ,       return_tensors=UpperCamelCase_		)
		with self.assertRaises(UpperCamelCase_		):
			snake_case__	       =   text_generator('test'      ,       return_text=UpperCamelCase_      ,       return_tensors=UpperCamelCase_		)

		# 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__	       =   text_generator(''		)
			self.assertEqual(UpperCamelCase_      ,       [{'generated_text': ANY(UpperCamelCase_		)}]		)
		else:
			with self.assertRaises((ValueError, AssertionError)		):
				snake_case__	       =   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__	       =   ['RwkvForCausalLM', 'XGLMForCausalLM', 'GPTNeoXForCausalLM']
		if (
		    tokenizer.model_max_length < 1_0000
		    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		)

			snake_case__	       =   text_generator('This is a test' * 500      ,       handle_long_generation='hole'      ,       max_new_tokens=20		)
			# Hole strategy cannot work
			with self.assertRaises(UpperCamelCase_		):
				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   _snake_case   (		self		)      ->		List[Any]:
		import torch

		# Classic `model_kwargs`
		snake_case__	       =   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__	       =   pipe('This is a test'		)
		self.assertEqual(
		    UpperCamelCase_      ,       [
		        {
		            '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__	       =   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__	       =   pipe('This is a test'		)
		self.assertEqual(
		    UpperCamelCase_      ,       [
		        {
		            '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__	       =   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__	       =   pipe('This is a test'		)
		self.assertEqual(
		    UpperCamelCase_      ,       [
		        {
		            '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   _snake_case   (		self		)      ->		List[str]:
		import torch

		snake_case__	       =   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   _snake_case   (		self		)      ->		List[Any]:
		import torch

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





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

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

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










'''simple docstring'''




from __future__ import annotations

import math




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->int:

	if depth < 0:
		raise ValueError('Depth cannot be less than 0'							)

	if not scores:
		raise ValueError('Scores cannot be empty'							)

	if depth == height:
		return scores[node_index]

	return (
	    max(
	        minimax(depth + 1					,				node_index * 2					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				minimax(depth + 1					,				node_index * 2 + 1					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				)
	    if is_max
	    else min(
	        minimax(depth + 1					,				node_index * 2					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				minimax(depth + 1					,				node_index * 2 + 1					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				)
	)




def 						__lowerCamelCase			(	)   ->None:
	snake_case__	       =   [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
	snake_case__	       =   math.log(len(UpperCAmelCase_							)					,				2							)
	print(f'''Optimal value : {minimax(0					,				0					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)}'''							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



