





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


if is_torch_available():
					import torch






def  _lowerCamelCase				( lowercase						:      Tuple    ,							lowercase						:      str    ,							lowercase						:      int=None						)	->	str:
				if rng is None:
								_a								= random.Random()

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

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

				_a								= np.array(lowercase    ,							dtype=jnp.intaa						).reshape(lowercase						)

				return output






def  _lowerCamelCase				( lowercase						:      str    ,							lowercase						:      Union[str, Any]=None						)	->	List[Any]:
				_a								= ids_tensor(lowercase    ,							vocab_size=2    ,							rng=lowercase						)
				# make sure that at least one token is attended to for each batch
				_a								= 1
				return attn_mask
@require_flax
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				__a            =None
				__a            =()




				def   UpperCamelCase__   (  self     :					int					):
								_a	,					_a								= self.model_tester.prepare_config_and_inputs_for_common()

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

								_a								= jnp.ones_like(__a					)
								_a								= attention_mask[:max_batch_size, :sequence_length]

								# generate max 5 tokens
								_a								= 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()`
												_a								= config.eos_token_id
								return config, input_ids, attention_mask, max_length




				@is_pt_flax_cross_test
				def   UpperCamelCase__   (  self     :					Tuple					):
								_a	,					_a	,					_a	,					_a								= self._get_input_ids_and_config()
								_a								= False
								_a								= max_length
								_a								= 0

								for model_class in self.all_generative_model_classes:
												_a								= model_class(__a					)

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

												_a								= flax_model.generate(__a					).sequences
												_a								= pt_model.generate(torch.tensor(__a					,				dtype=torch.long					)					)

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

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




				def   UpperCamelCase__   (  self     :					Any					):
								_a	,					_a	,					_a	,					_a								= self._get_input_ids_and_config()
								_a								= False
								_a								= max_length

								for model_class in self.all_generative_model_classes:
												_a								= model_class(__a					)

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

												_a								= jit(model.generate					)
												_a								= jit_generate(__a					).sequences

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




				def   UpperCamelCase__   (  self     :					int					):
								_a	,					_a	,					_a	,					_a								= self._get_input_ids_and_config()
								_a								= True
								_a								= max_length

								for model_class in self.all_generative_model_classes:
												_a								= model_class(__a					)

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

												_a								= jit(model.generate					)
												_a								= jit_generate(__a					).sequences

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




				def   UpperCamelCase__   (  self     :					int					):
								_a	,					_a	,					_a	,					_a								= self._get_input_ids_and_config()
								_a								= False
								_a								= max_length
								_a								= 2

								for model_class in self.all_generative_model_classes:
												_a								= model_class(__a					)

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

												_a								= jit(model.generate					)
												_a								= jit_generate(__a					).sequences

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




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a	,					_a	,					_a	,					_a								= self._get_input_ids_and_config()
								_a								= False
								_a								= max_length
								_a								= 2
								_a								= 2

								for model_class in self.all_generative_model_classes:
												_a								= model_class(__a					)

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




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a	,					_a	,					_a	,					_a								= self._get_input_ids_and_config()
								_a								= True
								_a								= max_length
								_a								= 0.8
								_a								= 10
								_a								= 0.3
								_a								= 1
								_a								= 8
								_a								= 9

								for model_class in self.all_generative_model_classes:
												_a								= model_class(__a					)

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

												_a								= jit(model.generate					)
												_a								= jit_generate(__a					).sequences

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




				def   UpperCamelCase__   (  self     :					int					):
								_a	,					_a	,					_a	,					_a								= self._get_input_ids_and_config()
								_a								= max_length
								_a								= 1
								_a								= 8
								_a								= 9

								for model_class in self.all_generative_model_classes:
												_a								= model_class(__a					)

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

												_a								= jit(model.generate					)
												_a								= jit_generate(__a					).sequences

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




				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a	,					_a	,					_a	,					_a								= self._get_input_ids_and_config()
								_a								= max_length
								_a								= 2
								_a								= 1
								_a								= 8
								_a								= 9

								for model_class in self.all_generative_model_classes:
												_a								= model_class(__a					)

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

												_a								= jit(model.generate					)
												_a								= jit_generate(__a					).sequences

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




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a	,					_a	,					_a	,					_a								= self._get_input_ids_and_config()

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

								_a								= False
								_a								= max_length

								for model_class in self.all_generative_model_classes:
												_a								= model_class(__a					)

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

												_a								= jit(model.generate					)
												_a								= jit_generate(__a					,				attention_mask=__a					).sequences

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




				def   UpperCamelCase__   (  self     :					str					):
								_a	,					_a	,					_a	,					_a								= self._get_input_ids_and_config()

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

								_a								= True
								_a								= max_length

								for model_class in self.all_generative_model_classes:
												_a								= model_class(__a					)

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

												_a								= jit(model.generate					)
												_a								= jit_generate(__a					,				attention_mask=__a					).sequences

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




				def   UpperCamelCase__   (  self     :					Any					):
								_a	,					_a	,					_a	,					_a								= self._get_input_ids_and_config()

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

								_a								= 2
								_a								= max_length

								for model_class in self.all_generative_model_classes:
												_a								= model_class(__a					)

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

												_a								= jit(model.generate					)
												_a								= jit_generate(__a					,				attention_mask=__a					).sequences

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


@require_flax
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"					)
								_a								= FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"					)

								_a								= "Hello world"
								_a								= tokenizer(__a					,				return_tensors="np"					).input_ids

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

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







'''simple docstring'''







from ....utils import logging


lowerCAmelCase_					:    Union[str, Any]      	=			logging.get_logger(__name__)
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				def __init__(  self     :					Tuple					,				__a     :					int					,				__a     :					Any=None					,				__a     :					Optional[int]=20_48					):
								_a								= config.__dict__
								_a								= modal_hidden_size
								if num_labels:
												_a								= num_labels
