"""simple docstring"""




import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
			import os

			import jax.numpy as jnp
			from jax import jit

			from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
			from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

			__lowerCAmelCase													=						"0.12"  # assumed parallelism: 8


if is_torch_available():
			import torch







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=None     ):





		"""simple docstring"""




		if rng is None:
				lowerCAmelCase__        =   random.Random()

		lowerCAmelCase__        =   1
		for dim in shape:
				total_dims *= dim

		lowerCAmelCase__        =   []
		for _ in range(lowerCamelCase__     ):
				values.append(rng.randint(0			,  vocab_size - 1     )     )

		lowerCAmelCase__        =   np.array(lowerCamelCase__			,  dtype=jnp.intaa     ).reshape(lowerCamelCase__     )

		return output







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__=None     ):





		"""simple docstring"""




		lowerCAmelCase__        =   ids_tensor(lowerCamelCase__			,  vocab_size=2			,  rng=lowerCamelCase__     )
		# make sure that at least one token is attended to for each batch
		lowerCAmelCase__        =   1
		return attn_mask

@require_flax
class       a_      :
					UpperCamelCase_ :	List[str]			=    None
					UpperCamelCase_ :	Any			=    ()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.model_tester.prepare_config_and_inputs_for_common()

							# cut to half length & take max batch_size 3
							lowerCAmelCase__        =   2
							lowerCAmelCase__        =   inputs["""input_ids"""].shape[-1] // 2
							lowerCAmelCase__        =   inputs["""input_ids"""][:max_batch_size, :sequence_length]

							lowerCAmelCase__        =   jnp.ones_like(snake_case__     )
							lowerCAmelCase__        =   attention_mask[:max_batch_size, :sequence_length]

							# generate max 5 tokens
							lowerCAmelCase__        =   input_ids.shape[-1] + 5
							if config.eos_token_id is not None and config.pad_token_id is None:
									# hack to allow generate for models such as GPT2 as is done in `generate()`
									lowerCAmelCase__        =   config.eos_token_id
							return config, input_ids, attention_mask, max_length




					@is_pt_flax_cross_test
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self._get_input_ids_and_config()
							lowerCAmelCase__        =   False
							lowerCAmelCase__        =   max_length
							lowerCAmelCase__        =   0

							for model_class in self.all_generative_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )

									lowerCAmelCase__        =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
									lowerCAmelCase__        =   getattr(snake_case__  ,						snake_case__     )
									lowerCAmelCase__        =   pt_model_class(snake_case__     ).eval()
									lowerCAmelCase__        =   load_flax_weights_in_pytorch_model(snake_case__  ,						flax_model.params     )

									lowerCAmelCase__        =   flax_model.generate(snake_case__     ).sequences
									lowerCAmelCase__        =   pt_model.generate(torch.tensor(snake_case__  ,						dtype=torch.long     )     )

									if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
											lowerCAmelCase__        =   flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

									self.assertListEqual(pt_generation_outputs.numpy().tolist()  ,						flax_generation_outputs.tolist()     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self._get_input_ids_and_config()
							lowerCAmelCase__        =   False
							lowerCAmelCase__        =   max_length

							for model_class in self.all_generative_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )

									lowerCAmelCase__        =   model.generate(snake_case__     ).sequences
									self.assertEqual(generation_outputs.shape[-1]  ,						snake_case__     )

									lowerCAmelCase__        =   jit(model.generate     )
									lowerCAmelCase__        =   jit_generate(snake_case__     ).sequences

									self.assertListEqual(generation_outputs.tolist()  ,						jit_generation_outputs.tolist()     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self._get_input_ids_and_config()
							lowerCAmelCase__        =   True
							lowerCAmelCase__        =   max_length

							for model_class in self.all_generative_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )

									lowerCAmelCase__        =   model.generate(snake_case__     ).sequences
									self.assertEqual(generation_outputs.shape[-1]  ,						snake_case__     )

									lowerCAmelCase__        =   jit(model.generate     )
									lowerCAmelCase__        =   jit_generate(snake_case__     ).sequences

									self.assertListEqual(generation_outputs.tolist()  ,						jit_generation_outputs.tolist()     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self._get_input_ids_and_config()
							lowerCAmelCase__        =   False
							lowerCAmelCase__        =   max_length
							lowerCAmelCase__        =   2

							for model_class in self.all_generative_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )

									lowerCAmelCase__        =   model.generate(snake_case__     ).sequences
									self.assertEqual(generation_outputs.shape[-1]  ,						snake_case__     )

									lowerCAmelCase__        =   jit(model.generate     )
									lowerCAmelCase__        =   jit_generate(snake_case__     ).sequences

									self.assertListEqual(generation_outputs.tolist()  ,						jit_generation_outputs.tolist()     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self._get_input_ids_and_config()
							lowerCAmelCase__        =   False
							lowerCAmelCase__        =   max_length
							lowerCAmelCase__        =   2
							lowerCAmelCase__        =   2

							for model_class in self.all_generative_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )

									lowerCAmelCase__        =   model.generate(snake_case__     ).sequences
									self.assertEqual(generation_outputs.shape[0]  ,						input_ids.shape[0] * config.num_return_sequences     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self._get_input_ids_and_config()
							lowerCAmelCase__        =   True
							lowerCAmelCase__        =   max_length
							lowerCAmelCase__        =   0.8
							lowerCAmelCase__        =   10
							lowerCAmelCase__        =   0.3
							lowerCAmelCase__        =   1
							lowerCAmelCase__        =   8
							lowerCAmelCase__        =   9

							for model_class in self.all_generative_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )

									lowerCAmelCase__        =   model.generate(snake_case__     ).sequences
									self.assertEqual(generation_outputs.shape[-1]  ,						snake_case__     )

									lowerCAmelCase__        =   jit(model.generate     )
									lowerCAmelCase__        =   jit_generate(snake_case__     ).sequences

									self.assertListEqual(generation_outputs.tolist()  ,						jit_generation_outputs.tolist()     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self._get_input_ids_and_config()
							lowerCAmelCase__        =   max_length
							lowerCAmelCase__        =   1
							lowerCAmelCase__        =   8
							lowerCAmelCase__        =   9

							for model_class in self.all_generative_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )

									lowerCAmelCase__        =   model.generate(snake_case__     ).sequences
									self.assertEqual(generation_outputs.shape[-1]  ,						snake_case__     )

									lowerCAmelCase__        =   jit(model.generate     )
									lowerCAmelCase__        =   jit_generate(snake_case__     ).sequences

									self.assertListEqual(generation_outputs.tolist()  ,						jit_generation_outputs.tolist()     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self._get_input_ids_and_config()
							lowerCAmelCase__        =   max_length
							lowerCAmelCase__        =   2
							lowerCAmelCase__        =   1
							lowerCAmelCase__        =   8
							lowerCAmelCase__        =   9

							for model_class in self.all_generative_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )

									lowerCAmelCase__        =   model.generate(snake_case__     ).sequences
									self.assertEqual(generation_outputs.shape[-1]  ,						snake_case__     )

									lowerCAmelCase__        =   jit(model.generate     )
									lowerCAmelCase__        =   jit_generate(snake_case__     ).sequences

									self.assertListEqual(generation_outputs.tolist()  ,						jit_generation_outputs.tolist()     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self._get_input_ids_and_config()

							# pad attention mask on the left
							lowerCAmelCase__        =   attention_mask.at[(0, 0)].set(0     )

							lowerCAmelCase__        =   False
							lowerCAmelCase__        =   max_length

							for model_class in self.all_generative_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )

									lowerCAmelCase__        =   model.generate(snake_case__  ,						attention_mask=snake_case__     ).sequences
									self.assertEqual(generation_outputs.shape[-1]  ,						snake_case__     )

									lowerCAmelCase__        =   jit(model.generate     )
									lowerCAmelCase__        =   jit_generate(snake_case__  ,						attention_mask=snake_case__     ).sequences

									self.assertListEqual(generation_outputs.tolist()  ,						jit_generation_outputs.tolist()     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self._get_input_ids_and_config()

							# pad attention mask on the left
							lowerCAmelCase__        =   attention_mask.at[(0, 0)].set(0     )

							lowerCAmelCase__        =   True
							lowerCAmelCase__        =   max_length

							for model_class in self.all_generative_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )

									lowerCAmelCase__        =   model.generate(snake_case__  ,						attention_mask=snake_case__     ).sequences
									self.assertEqual(generation_outputs.shape[-1]  ,						snake_case__     )

									lowerCAmelCase__        =   jit(model.generate     )
									lowerCAmelCase__        =   jit_generate(snake_case__  ,						attention_mask=snake_case__     ).sequences

									self.assertListEqual(generation_outputs.tolist()  ,						jit_generation_outputs.tolist()     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   self._get_input_ids_and_config()

							# pad attention mask on the left
							lowerCAmelCase__        =   attention_mask.at[(0, 0)].set(0     )

							lowerCAmelCase__        =   2
							lowerCAmelCase__        =   max_length

							for model_class in self.all_generative_model_classes:
									lowerCAmelCase__        =   model_class(snake_case__     )

									lowerCAmelCase__        =   model.generate(snake_case__  ,						attention_mask=snake_case__     ).sequences
									self.assertEqual(generation_outputs.shape[-1]  ,						snake_case__     )

									lowerCAmelCase__        =   jit(model.generate     )
									lowerCAmelCase__        =   jit_generate(snake_case__  ,						attention_mask=snake_case__     ).sequences

									self.assertListEqual(generation_outputs.tolist()  ,						jit_generation_outputs.tolist()     )

@require_flax
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""     )
							lowerCAmelCase__        =   FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""     )

							lowerCAmelCase__        =   """Hello world"""
							lowerCAmelCase__        =   tokenizer(snake_case__  ,						return_tensors="""np"""     ).input_ids

							# typos are quickly detected (the correct argument is `do_sample`)
							with self.assertRaisesRegex(snake_case__  ,						"""do_samples"""     ):
									model.generate(snake_case__  ,						do_samples=snake_case__     )

							# arbitrary arguments that will not be used anywhere are also not accepted
							with self.assertRaisesRegex(snake_case__  ,						"""foo"""     ):
									lowerCAmelCase__        =   {"""foo""": """bar"""}
									model.generate(snake_case__  ,						**snake_case__     )





"""simple docstring"""




from math import pi, sqrt







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		if num <= 0:
				raise ValueError("""math domain error"""     )
		if num > 1_71.5:
				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 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		assert gamma(0.5     ) == sqrt(lowerCamelCase__     )
		assert gamma(1     ) == 1.0
		assert gamma(2     ) == 1.0


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

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




