


"""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

	__UpperCamelCase		= "0.12"  # assumed parallelism: 8


if is_torch_available():
	import torch


def        lowercase     (SCREAMING_SNAKE_CASE_						:							Dict					,     SCREAMING_SNAKE_CASE_						:							int					,     SCREAMING_SNAKE_CASE_						:							Tuple=None    )  ->		Union[str, Any]:
						if rng is None:
												SCREAMING_SNAKE_CASE						=   random.Random()

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

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

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

						return output


def        lowercase     (SCREAMING_SNAKE_CASE_						:							Tuple					,     SCREAMING_SNAKE_CASE_						:							str=None    )  ->		Optional[int]:
						SCREAMING_SNAKE_CASE						=   ids_tensor(lowerCAmelCase__					,     vocab_size=2					,     rng=lowerCAmelCase__    )
						# make sure that at least one token is attended to for each batch
						SCREAMING_SNAKE_CASE						=   1
						return attn_mask
@require_flax
class    lowerCAmelCase      :




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_		:	str														=	None
		SCREAMING_SNAKE_CASE_		:	List[Any]														=	()







		def 				__A		(      self							)	->				List[str]:
								SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE						=   self.model_tester.prepare_config_and_inputs_for_common()

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

								SCREAMING_SNAKE_CASE						=   jnp.ones_like(_lowercase							)
								SCREAMING_SNAKE_CASE						=   attention_mask[:max_batch_size, :sequence_length]

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







		@is_pt_flax_cross_test
		def 				__A		(      self							)	->				Dict:
								SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE						=   self._get_input_ids_and_config()
								SCREAMING_SNAKE_CASE						=   False
								SCREAMING_SNAKE_CASE						=   max_length
								SCREAMING_SNAKE_CASE						=   0

								for model_class in self.all_generative_model_classes:
														SCREAMING_SNAKE_CASE						=   model_class(_lowercase							)

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

														SCREAMING_SNAKE_CASE						=   flax_model.generate(_lowercase							).sequences
														SCREAMING_SNAKE_CASE						=   pt_model.generate(torch.tensor(_lowercase		,			dtype=torch.long							)							)

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

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







		def 				__A		(      self							)	->				Any:
								SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE						=   self._get_input_ids_and_config()
								SCREAMING_SNAKE_CASE						=   False
								SCREAMING_SNAKE_CASE						=   max_length

								for model_class in self.all_generative_model_classes:
														SCREAMING_SNAKE_CASE						=   model_class(_lowercase							)

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

														SCREAMING_SNAKE_CASE						=   jit(model.generate							)
														SCREAMING_SNAKE_CASE						=   jit_generate(_lowercase							).sequences

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







		def 				__A		(      self							)	->				int:
								SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE						=   self._get_input_ids_and_config()
								SCREAMING_SNAKE_CASE						=   True
								SCREAMING_SNAKE_CASE						=   max_length

								for model_class in self.all_generative_model_classes:
														SCREAMING_SNAKE_CASE						=   model_class(_lowercase							)

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

														SCREAMING_SNAKE_CASE						=   jit(model.generate							)
														SCREAMING_SNAKE_CASE						=   jit_generate(_lowercase							).sequences

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







		def 				__A		(      self							)	->				Any:
								SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE						=   self._get_input_ids_and_config()
								SCREAMING_SNAKE_CASE						=   False
								SCREAMING_SNAKE_CASE						=   max_length
								SCREAMING_SNAKE_CASE						=   2

								for model_class in self.all_generative_model_classes:
														SCREAMING_SNAKE_CASE						=   model_class(_lowercase							)

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

														SCREAMING_SNAKE_CASE						=   jit(model.generate							)
														SCREAMING_SNAKE_CASE						=   jit_generate(_lowercase							).sequences

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







		def 				__A		(      self							)	->				Optional[Any]:
								SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE						=   self._get_input_ids_and_config()
								SCREAMING_SNAKE_CASE						=   False
								SCREAMING_SNAKE_CASE						=   max_length
								SCREAMING_SNAKE_CASE						=   2
								SCREAMING_SNAKE_CASE						=   2

								for model_class in self.all_generative_model_classes:
														SCREAMING_SNAKE_CASE						=   model_class(_lowercase							)

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







		def 				__A		(      self							)	->				Tuple:
								SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE						=   self._get_input_ids_and_config()
								SCREAMING_SNAKE_CASE						=   True
								SCREAMING_SNAKE_CASE						=   max_length
								SCREAMING_SNAKE_CASE						=   0.8
								SCREAMING_SNAKE_CASE						=   10
								SCREAMING_SNAKE_CASE						=   0.3
								SCREAMING_SNAKE_CASE						=   1
								SCREAMING_SNAKE_CASE						=   8
								SCREAMING_SNAKE_CASE						=   9

								for model_class in self.all_generative_model_classes:
														SCREAMING_SNAKE_CASE						=   model_class(_lowercase							)

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

														SCREAMING_SNAKE_CASE						=   jit(model.generate							)
														SCREAMING_SNAKE_CASE						=   jit_generate(_lowercase							).sequences

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







		def 				__A		(      self							)	->				List[str]:
								SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE						=   self._get_input_ids_and_config()
								SCREAMING_SNAKE_CASE						=   max_length
								SCREAMING_SNAKE_CASE						=   1
								SCREAMING_SNAKE_CASE						=   8
								SCREAMING_SNAKE_CASE						=   9

								for model_class in self.all_generative_model_classes:
														SCREAMING_SNAKE_CASE						=   model_class(_lowercase							)

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

														SCREAMING_SNAKE_CASE						=   jit(model.generate							)
														SCREAMING_SNAKE_CASE						=   jit_generate(_lowercase							).sequences

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







		def 				__A		(      self							)	->				Dict:
								SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE						=   self._get_input_ids_and_config()
								SCREAMING_SNAKE_CASE						=   max_length
								SCREAMING_SNAKE_CASE						=   2
								SCREAMING_SNAKE_CASE						=   1
								SCREAMING_SNAKE_CASE						=   8
								SCREAMING_SNAKE_CASE						=   9

								for model_class in self.all_generative_model_classes:
														SCREAMING_SNAKE_CASE						=   model_class(_lowercase							)

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

														SCREAMING_SNAKE_CASE						=   jit(model.generate							)
														SCREAMING_SNAKE_CASE						=   jit_generate(_lowercase							).sequences

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







		def 				__A		(      self							)	->				Optional[int]:
								SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE						=   self._get_input_ids_and_config()

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

								SCREAMING_SNAKE_CASE						=   False
								SCREAMING_SNAKE_CASE						=   max_length

								for model_class in self.all_generative_model_classes:
														SCREAMING_SNAKE_CASE						=   model_class(_lowercase							)

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

														SCREAMING_SNAKE_CASE						=   jit(model.generate							)
														SCREAMING_SNAKE_CASE						=   jit_generate(_lowercase		,			attention_mask=_lowercase							).sequences

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







		def 				__A		(      self							)	->				List[Any]:
								SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE						=   self._get_input_ids_and_config()

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

								SCREAMING_SNAKE_CASE						=   True
								SCREAMING_SNAKE_CASE						=   max_length

								for model_class in self.all_generative_model_classes:
														SCREAMING_SNAKE_CASE						=   model_class(_lowercase							)

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

														SCREAMING_SNAKE_CASE						=   jit(model.generate							)
														SCREAMING_SNAKE_CASE						=   jit_generate(_lowercase		,			attention_mask=_lowercase							).sequences

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







		def 				__A		(      self							)	->				int:
								SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE						=   self._get_input_ids_and_config()

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

								SCREAMING_SNAKE_CASE						=   2
								SCREAMING_SNAKE_CASE						=   max_length

								for model_class in self.all_generative_model_classes:
														SCREAMING_SNAKE_CASE						=   model_class(_lowercase							)

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

														SCREAMING_SNAKE_CASE						=   jit(model.generate							)
														SCREAMING_SNAKE_CASE						=   jit_generate(_lowercase		,			attention_mask=_lowercase							).sequences

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





@require_flax
class    lowerCAmelCase      (   unittest.TestCase       ):




		'''simple docstring'''







		def 				__A		(      self							)	->				Optional[int]:
								SCREAMING_SNAKE_CASE						=   AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'							)
								SCREAMING_SNAKE_CASE						=   FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'							)

								SCREAMING_SNAKE_CASE						=   'Hello world'
								SCREAMING_SNAKE_CASE						=   tokenizer(_lowercase		,			return_tensors='np'							).input_ids

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

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





'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE							:				List[Any]     	=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:				Tuple     	=			{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     """roberta"""



				def __init__(    self,     _lowercase=50265,     _lowercase=768,     _lowercase=12,     _lowercase=12,     _lowercase=3072,     _lowercase="gelu",     _lowercase=0.1,     _lowercase=0.1,     _lowercase=512,     _lowercase=2,     _lowercase=0.02,     _lowercase=1E-12,     _lowercase=1,     _lowercase=0,     _lowercase=2,     _lowercase="absolute",     _lowercase=True,     _lowercase=None,     **_lowercase,     )     ->							Dict:
											super().__init__(pad_token_id=_lowercase,     bos_token_id=_lowercase,     eos_token_id=_lowercase,     **_lowercase       )

											SCREAMING_SNAKE_CASE_				=				vocab_size
											SCREAMING_SNAKE_CASE_				=				hidden_size
											SCREAMING_SNAKE_CASE_				=				num_hidden_layers
											SCREAMING_SNAKE_CASE_				=				num_attention_heads
											SCREAMING_SNAKE_CASE_				=				hidden_act
											SCREAMING_SNAKE_CASE_				=				intermediate_size
											SCREAMING_SNAKE_CASE_				=				hidden_dropout_prob
											SCREAMING_SNAKE_CASE_				=				attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_				=				max_position_embeddings
											SCREAMING_SNAKE_CASE_				=				type_vocab_size
											SCREAMING_SNAKE_CASE_				=				initializer_range
											SCREAMING_SNAKE_CASE_				=				layer_norm_eps
											SCREAMING_SNAKE_CASE_				=				position_embedding_type
											SCREAMING_SNAKE_CASE_				=				use_cache
											SCREAMING_SNAKE_CASE_				=				classifier_dropout






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				@property
				def   a__					(    self       )     ->							Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE_				=				{0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		SCREAMING_SNAKE_CASE_				=				{0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]       )





