






'''simple docstring'''

import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
							import jax

							from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
							from transformers.models.bert.modeling_flax_bert import FlaxBertModel
							from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel






@require_flax
class 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	@slow
	def        lowerCamelCase__		(							self       )						->  str:
							for model_name in ["bert-base-cased", "bert-large-uncased"]:
													with self.subTest(_lowercase       ):
																			lowercase_      :   Optional[Any]        =			AutoConfig.from_pretrained(_lowercase       )
																			self.assertIsNotNone(_lowercase       )
																			self.assertIsInstance(_lowercase  ,      _lowercase       )

																			lowercase_      :   int        =			FlaxAutoModel.from_pretrained(_lowercase       )
																			self.assertIsNotNone(_lowercase       )
																			self.assertIsInstance(_lowercase  ,      _lowercase       )
	@slow
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							for model_name in ["roberta-base", "roberta-large"]:
													with self.subTest(_lowercase       ):
																			lowercase_      :   List[str]        =			AutoConfig.from_pretrained(_lowercase       )
																			self.assertIsNotNone(_lowercase       )
																			self.assertIsInstance(_lowercase  ,      _lowercase       )

																			lowercase_      :   Optional[Any]        =			FlaxAutoModel.from_pretrained(_lowercase       )
																			self.assertIsNotNone(_lowercase       )
																			self.assertIsInstance(_lowercase  ,      _lowercase       )
	@slow
	def        lowerCamelCase__		(							self       )						->  Tuple:
							for model_name in ["bert-base-cased", "bert-large-uncased"]:
													lowercase_      :   List[Any]        =			AutoTokenizer.from_pretrained(_lowercase       )
													lowercase_      :   Dict        =			FlaxBertModel.from_pretrained(_lowercase       )
													lowercase_      :   int        =			tokenizer('Do you support jax jitted function?'  ,      return_tensors=TensorType.JAX       )

													@jax.jit
													def eval(**_lowercase       ):
																			return model(**_lowercase       )

													eval(**_lowercase       ).block_until_ready()
	@slow
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							for model_name in ["roberta-base", "roberta-large"]:
													lowercase_      :   Union[str, Any]        =			AutoTokenizer.from_pretrained(_lowercase       )
													lowercase_      :   Optional[int]        =			FlaxRobertaModel.from_pretrained(_lowercase       )
													lowercase_      :   int        =			tokenizer('Do you support jax jitted function?'  ,      return_tensors=TensorType.JAX       )

													@jax.jit
													def eval(**_lowercase       ):
																			return model(**_lowercase       )

													eval(**_lowercase       ).block_until_ready()
	def        lowerCamelCase__		(							self       )						->  Tuple:
							with self.assertRaisesRegex(
							    _lowercase  ,      'bert-base is not a local folder and is not a valid model identifier'       ):
													lowercase_      :   List[Any]        =			FlaxAutoModel.from_pretrained('bert-base'       )
	def        lowerCamelCase__		(							self       )						->  int:
							with self.assertRaisesRegex(
							    _lowercase  ,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'       ):
													lowercase_      :   Union[str, Any]        =			FlaxAutoModel.from_pretrained(_lowercase  ,      revision='aaaaaa'       )
	def        lowerCamelCase__		(							self       )						->  str:
							with self.assertRaisesRegex(
							    _lowercase  ,      'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'  ,      ):
													lowercase_      :   Union[str, Any]        =			FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'       )







	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							with self.assertRaisesRegex(_lowercase  ,      'Use `from_pt=True` to load this model'       ):
													lowercase_      :   Optional[Any]        =			FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'       )








'''simple docstring'''

import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


A:      Any  				=				get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
						A:      List[Any]  				=				json.load(f)






@require_torch
class 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Tuple:
							return FSMTTokenizer.from_pretrained(_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Optional[int]:
							lowercase_      :   str        =			FSMTForConditionalGeneration.from_pretrained(_lowercase       ).to(_lowercase       )
							if torch_device == "cuda":
													model.half()
							return model







	@parameterized.expand(
	    [
	        ['en-ru', 26.0],
	        ['ru-en', 22.0],
	        ['en-de', 22.0],
	        ['de-en', 29.0],
	    ]       )
	@slow
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase       )						->  Optional[int]:
							# note: this test is not testing the best performance since it only evals a small batch
							# but it should be enough to detect a regression in the output quality
							lowercase_      :   Optional[Any]        =			f"facebook/wmt19-{pair}"
							lowercase_      :   str        =			self.get_tokenizer(_lowercase       )
							lowercase_      :   Any        =			self.get_model(_lowercase       )

							lowercase_      :   Any        =			bleu_data[pair]['src']
							lowercase_      :   Any        =			bleu_data[pair]['tgt']

							lowercase_      :   Dict        =			tokenizer(_lowercase  ,      return_tensors='pt'  ,      truncation=_lowercase  ,      padding='longest'       ).to(_lowercase       )
							lowercase_      :   str        =			model.generate(
							    input_ids=batch.input_ids  ,      num_beams=8  ,      )
							lowercase_      :   Any        =			tokenizer.batch_decode(
							    _lowercase  ,      skip_special_tokens=_lowercase  ,      clean_up_tokenization_spaces=_lowercase       )
							lowercase_      :   Union[str, Any]        =			calculate_bleu(_lowercase  ,      _lowercase       )
							print(_lowercase       )
							self.assertGreaterEqual(scores['bleu']  ,      _lowercase       )
