






"""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

						A__	:    Optional[int]			 = '0.12'  # assumed parallelism: 8


if is_torch_available():
						import torch

def 			_snake_case    (					lowerCamelCase__     :   List[Any]    ,						lowerCamelCase__     :   Union[str, Any]    ,						lowerCamelCase__     :   Any=None  )    ->     List[Any]:
							if rng is None:
														lowerCamelCase_	:							Any									=random.Random()

							lowerCamelCase_	:							List[str]									=1
							for dim in shape:
														total_dims *= dim

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

							lowerCamelCase_	:							Union[str, Any]									=np.array(lowerCamelCase__    ,						dtype=jnp.intaa  ).reshape(lowerCamelCase__  )

							return output

def 			_snake_case    (					lowerCamelCase__     :   Tuple    ,						lowerCamelCase__     :   Optional[Any]=None  )    ->     Dict:
							lowerCamelCase_	:							Dict									=ids_tensor(lowerCamelCase__    ,						vocab_size=2    ,						rng=lowerCamelCase__  )
							# make sure that at least one token is attended to for each batch
							lowerCamelCase_	:							List[Any]									=1
							return attn_mask







@require_flax
class        lowercase__      :
			_UpperCAmelCase		:Dict             =			None
			_UpperCAmelCase		:Dict             =			()


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_							,						lowerCamelCase_	:							Optional[int]									=self.model_tester.prepare_config_and_inputs_for_common()

										# cut to half length & take max batch_size 3
										lowerCamelCase_	:							Union[str, Any]									=2
										lowerCamelCase_	:							int									=inputs["input_ids"].shape[-1] // 2
										lowerCamelCase_	:							List[str]									=inputs["input_ids"][:max_batch_size, :sequence_length]

										lowerCamelCase_	:							Tuple									=jnp.ones_like(snake_case__ )
										lowerCamelCase_	:							Union[str, Any]									=attention_mask[:max_batch_size, :sequence_length]

										# generate max 5 tokens
										lowerCamelCase_	:							int									=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_	:							List[Any]									=config.eos_token_id
										return config, input_ids, attention_mask, max_length


			@is_pt_flax_cross_test
			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							List[str]									=self._get_input_ids_and_config()
										lowerCamelCase_	:							Union[str, Any]									=False
										lowerCamelCase_	:							Union[str, Any]									=max_length
										lowerCamelCase_	:							Dict									=0

										for model_class in self.all_generative_model_classes:
																	lowerCamelCase_	:							Optional[Any]									=model_class(snake_case__ )

																	lowerCamelCase_	:							int									=model_class.__name__[4:]  # Skip the "Flax" at the beginning
																	lowerCamelCase_	:							Union[str, Any]									=getattr(snake_case__       ,	snake_case__ )
																	lowerCamelCase_	:							Any									=pt_model_class(snake_case__ ).eval()
																	lowerCamelCase_	:							Tuple									=load_flax_weights_in_pytorch_model(snake_case__       ,	flax_model.params )

																	lowerCamelCase_	:							Optional[int]									=flax_model.generate(snake_case__ ).sequences
																	lowerCamelCase_	:							List[Any]									=pt_model.generate(torch.tensor(snake_case__       ,	dtype=torch.long ) )

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

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


			def      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							List[Any]									=self._get_input_ids_and_config()
										lowerCamelCase_	:							List[str]									=False
										lowerCamelCase_	:							int									=max_length

										for model_class in self.all_generative_model_classes:
																	lowerCamelCase_	:							Any									=model_class(snake_case__ )

																	lowerCamelCase_	:							Union[str, Any]									=model.generate(snake_case__ ).sequences
																	self.assertEqual(generation_outputs.shape[-1]       ,	snake_case__ )

																	lowerCamelCase_	:							List[str]									=jit(model.generate )
																	lowerCamelCase_	:							Optional[Any]									=jit_generate(snake_case__ ).sequences

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


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Optional[int]									=self._get_input_ids_and_config()
										lowerCamelCase_	:							int									=True
										lowerCamelCase_	:							List[str]									=max_length

										for model_class in self.all_generative_model_classes:
																	lowerCamelCase_	:							Any									=model_class(snake_case__ )

																	lowerCamelCase_	:							Optional[Any]									=model.generate(snake_case__ ).sequences
																	self.assertEqual(generation_outputs.shape[-1]       ,	snake_case__ )

																	lowerCamelCase_	:							int									=jit(model.generate )
																	lowerCamelCase_	:							int									=jit_generate(snake_case__ ).sequences

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


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Tuple									=self._get_input_ids_and_config()
										lowerCamelCase_	:							Any									=False
										lowerCamelCase_	:							List[str]									=max_length
										lowerCamelCase_	:							List[Any]									=2

										for model_class in self.all_generative_model_classes:
																	lowerCamelCase_	:							Dict									=model_class(snake_case__ )

																	lowerCamelCase_	:							Optional[Any]									=model.generate(snake_case__ ).sequences
																	self.assertEqual(generation_outputs.shape[-1]       ,	snake_case__ )

																	lowerCamelCase_	:							Tuple									=jit(model.generate )
																	lowerCamelCase_	:							Optional[Any]									=jit_generate(snake_case__ ).sequences

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


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Dict									=self._get_input_ids_and_config()
										lowerCamelCase_	:							Any									=False
										lowerCamelCase_	:							Any									=max_length
										lowerCamelCase_	:							int									=2
										lowerCamelCase_	:							Tuple									=2

										for model_class in self.all_generative_model_classes:
																	lowerCamelCase_	:							Any									=model_class(snake_case__ )

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


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							List[str]									=self._get_input_ids_and_config()
										lowerCamelCase_	:							List[str]									=True
										lowerCamelCase_	:							Union[str, Any]									=max_length
										lowerCamelCase_	:							str									=0.8
										lowerCamelCase_	:							Dict									=10
										lowerCamelCase_	:							Tuple									=0.3
										lowerCamelCase_	:							Optional[Any]									=1
										lowerCamelCase_	:							str									=8
										lowerCamelCase_	:							Tuple									=9

										for model_class in self.all_generative_model_classes:
																	lowerCamelCase_	:							int									=model_class(snake_case__ )

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

																	lowerCamelCase_	:							Tuple									=jit(model.generate )
																	lowerCamelCase_	:							Optional[Any]									=jit_generate(snake_case__ ).sequences

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


			def      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Union[str, Any]									=self._get_input_ids_and_config()
										lowerCamelCase_	:							Dict									=max_length
										lowerCamelCase_	:							List[str]									=1
										lowerCamelCase_	:							str									=8
										lowerCamelCase_	:							Dict									=9

										for model_class in self.all_generative_model_classes:
																	lowerCamelCase_	:							Optional[Any]									=model_class(snake_case__ )

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

																	lowerCamelCase_	:							Optional[int]									=jit(model.generate )
																	lowerCamelCase_	:							List[Any]									=jit_generate(snake_case__ ).sequences

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


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Any									=self._get_input_ids_and_config()
										lowerCamelCase_	:							int									=max_length
										lowerCamelCase_	:							List[Any]									=2
										lowerCamelCase_	:							List[Any]									=1
										lowerCamelCase_	:							List[str]									=8
										lowerCamelCase_	:							Tuple									=9

										for model_class in self.all_generative_model_classes:
																	lowerCamelCase_	:							Optional[int]									=model_class(snake_case__ )

																	lowerCamelCase_	:							List[Any]									=model.generate(snake_case__ ).sequences
																	self.assertEqual(generation_outputs.shape[-1]       ,	snake_case__ )

																	lowerCamelCase_	:							int									=jit(model.generate )
																	lowerCamelCase_	:							Dict									=jit_generate(snake_case__ ).sequences

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


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							int									=self._get_input_ids_and_config()

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

										lowerCamelCase_	:							List[Any]									=False
										lowerCamelCase_	:							List[Any]									=max_length

										for model_class in self.all_generative_model_classes:
																	lowerCamelCase_	:							str									=model_class(snake_case__ )

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

																	lowerCamelCase_	:							List[Any]									=jit(model.generate )
																	lowerCamelCase_	:							Optional[Any]									=jit_generate(snake_case__       ,	attention_mask=snake_case__ ).sequences

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


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Union[str, Any]									=self._get_input_ids_and_config()

										# pad attention mask on the left
										lowerCamelCase_	:							Optional[Any]									=attention_mask.at[(0, 0)].set(0 )

										lowerCamelCase_	:							List[str]									=True
										lowerCamelCase_	:							Tuple									=max_length

										for model_class in self.all_generative_model_classes:
																	lowerCamelCase_	:							List[str]									=model_class(snake_case__ )

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

																	lowerCamelCase_	:							Any									=jit(model.generate )
																	lowerCamelCase_	:							Dict									=jit_generate(snake_case__       ,	attention_mask=snake_case__ ).sequences

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


			def      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Optional[int]									=self._get_input_ids_and_config()

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

										lowerCamelCase_	:							Optional[Any]									=2
										lowerCamelCase_	:							Any									=max_length

										for model_class in self.all_generative_model_classes:
																	lowerCamelCase_	:							Tuple									=model_class(snake_case__ )

																	lowerCamelCase_	:							List[str]									=model.generate(snake_case__       ,	attention_mask=snake_case__ ).sequences
																	self.assertEqual(generation_outputs.shape[-1]       ,	snake_case__ )

																	lowerCamelCase_	:							List[Any]									=jit(model.generate )
																	lowerCamelCase_	:							Any									=jit_generate(snake_case__       ,	attention_mask=snake_case__ ).sequences

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







@require_flax
class        lowercase__      (							unittest.TestCase       ):


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							Union[str, Any]									=AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert" )
										lowerCamelCase_	:							Optional[int]									=FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )

										lowerCamelCase_	:							Dict									="Hello world"
										lowerCamelCase_	:							Optional[int]									=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_	:							int									={"foo": "bar"}
																	model.generate(snake_case__       ,	**snake_case__ )











"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   int  )    ->     int:

							if not isinstance(lowerCamelCase__    ,						lowerCamelCase__  ):
														raise TypeError("only integers accepted as input"  )
							else:
														lowerCamelCase_	:							str									=str(abs(lowerCamelCase__  )  )
														lowerCamelCase_	:							Tuple									=[list(lowerCamelCase__  ) for char in range(len(lowerCamelCase__  )  )]
														for index in range(len(lowerCamelCase__  )  ):
																					num_transpositions[index].pop(lowerCamelCase__  )
														return max(
														    int("".join(list(lowerCamelCase__  )  )  ) for transposition in num_transpositions  )


if __name__ == "__main__":
						__import__('doctest').testmod()



