
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 		lowercase__    (		unittest.TestCase    ):
	@slow
	def 	A_							(     self				:  str				):

						for model_name in ["bert-base-cased", "bert-large-uncased"]:
											with self.subTest(snake_case_				):
																SCREAMING_SNAKE_CASE__						=					AutoConfig.from_pretrained(snake_case_				)
																self.assertIsNotNone(snake_case_				)
																self.assertIsInstance(snake_case_			,  snake_case_				)

																SCREAMING_SNAKE_CASE__						=					FlaxAutoModel.from_pretrained(snake_case_				)
																self.assertIsNotNone(snake_case_				)
																self.assertIsInstance(snake_case_			,  snake_case_				)
	@slow
	def 	A_							(     self				:  Optional[int]				):

						for model_name in ["roberta-base", "roberta-large"]:
											with self.subTest(snake_case_				):
																SCREAMING_SNAKE_CASE__						=					AutoConfig.from_pretrained(snake_case_				)
																self.assertIsNotNone(snake_case_				)
																self.assertIsInstance(snake_case_			,  snake_case_				)

																SCREAMING_SNAKE_CASE__						=					FlaxAutoModel.from_pretrained(snake_case_				)
																self.assertIsNotNone(snake_case_				)
																self.assertIsInstance(snake_case_			,  snake_case_				)
	@slow
	def 	A_							(     self				:  str				):

						for model_name in ["bert-base-cased", "bert-large-uncased"]:
											SCREAMING_SNAKE_CASE__						=					AutoTokenizer.from_pretrained(snake_case_				)
											SCREAMING_SNAKE_CASE__						=					FlaxBertModel.from_pretrained(snake_case_				)
											SCREAMING_SNAKE_CASE__						=					tokenizer('Do you support jax jitted function?'			,  return_tensors=TensorType.JAX				)

											@jax.jit
											def eval(**UpperCAmelCase_				:  Dict				):
																return model(**snake_case_				)

											eval(**snake_case_				).block_until_ready()
	@slow
	def 	A_							(     self				:  Dict				):

						for model_name in ["roberta-base", "roberta-large"]:
											SCREAMING_SNAKE_CASE__						=					AutoTokenizer.from_pretrained(snake_case_				)
											SCREAMING_SNAKE_CASE__						=					FlaxRobertaModel.from_pretrained(snake_case_				)
											SCREAMING_SNAKE_CASE__						=					tokenizer('Do you support jax jitted function?'			,  return_tensors=TensorType.JAX				)

											@jax.jit
											def eval(**UpperCAmelCase_				:  List[Any]				):
																return model(**snake_case_				)

											eval(**snake_case_				).block_until_ready()
	def 	A_							(     self				:  Any				):

						with self.assertRaisesRegex(
						    snake_case_			,  'bert-base is not a local folder and is not a valid model identifier'				):
											SCREAMING_SNAKE_CASE__						=					FlaxAutoModel.from_pretrained('bert-base'				)
	def 	A_							(     self				:  Optional[Any]				):

						with self.assertRaisesRegex(
						    snake_case_			,  r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'				):
											SCREAMING_SNAKE_CASE__						=					FlaxAutoModel.from_pretrained(snake_case_			,  revision='aaaaaa'				)
	def 	A_							(     self				:  str				):

						with self.assertRaisesRegex(
						    snake_case_			,  'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'			,  ):
											SCREAMING_SNAKE_CASE__						=					FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'				)





	def 	A_							(     self				:  Optional[int]				):

						with self.assertRaisesRegex(snake_case_			,  'Use `from_pt=True` to load this model'				):
											SCREAMING_SNAKE_CASE__						=					FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'				)









from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = False			,							snake_case_ = False			,							snake_case_ = None			,							**snake_case_			,							) ->      Dict:







								'''simple docstring'''

								super().__init__(
								    snake_case_			,							split=snake_case_			,							features=snake_case_			,							cache_dir=snake_case_			,							keep_in_memory=snake_case_			,							streaming=snake_case_			,							num_proc=snake_case_			,							**snake_case_			,							)
								__lowercase			       = path_or_paths if isinstance(snake_case_			,							snake_case_    ) else {self.split: path_or_paths}
								__lowercase			       = Text(
								    cache_dir=snake_case_			,							data_files=snake_case_			,							features=snake_case_			,							**snake_case_			,							)






					def A   (					self    ) ->      int:







								'''simple docstring'''

								if self.streaming:
											__lowercase			       = self.builder.as_streaming_dataset(split=self.split    )
								# Build regular (map-style) dataset
								else:
											__lowercase			       = None
											__lowercase			       = None
											__lowercase			       = None
											__lowercase			       = None

											self.builder.download_and_prepare(
											    download_config=snake_case_			,							download_mode=snake_case_			,							verification_mode=snake_case_			,							base_path=snake_case_			,							num_proc=self.num_proc			,							)
											__lowercase			       = self.builder.as_dataset(
											    split=self.split			,							verification_mode=snake_case_			,							in_memory=self.keep_in_memory    )
								return dataset


