

"""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 lowerCAmelCase__			(  unittest.TestCase		):






			'''simple docstring'''



			@slow
			def       A_					(						self						):
							for model_name in ["bert-base-cased", "bert-large-uncased"]:
											with self.subTest(__A						):
															_lowerCamelCase  :    Union[str, Any]					=					AutoConfig.from_pretrained(__A						)
															self.assertIsNotNone(__A						)
															self.assertIsInstance(__A						,   __A						)

															_lowerCamelCase  :    Optional[int]					=					FlaxAutoModel.from_pretrained(__A						)
															self.assertIsNotNone(__A						)
															self.assertIsInstance(__A						,   __A						)
			@slow
			def       A_					(						self						):
							for model_name in ["roberta-base", "roberta-large"]:
											with self.subTest(__A						):
															_lowerCamelCase  :    Dict					=					AutoConfig.from_pretrained(__A						)
															self.assertIsNotNone(__A						)
															self.assertIsInstance(__A						,   __A						)

															_lowerCamelCase  :    Any					=					FlaxAutoModel.from_pretrained(__A						)
															self.assertIsNotNone(__A						)
															self.assertIsInstance(__A						,   __A						)
			@slow
			def       A_					(						self						):
							for model_name in ["bert-base-cased", "bert-large-uncased"]:
											_lowerCamelCase  :    str					=					AutoTokenizer.from_pretrained(__A						)
											_lowerCamelCase  :    Any					=					FlaxBertModel.from_pretrained(__A						)
											_lowerCamelCase  :    Tuple					=					tokenizer('Do you support jax jitted function?'						,   return_tensors=TensorType.JAX						)

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

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

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

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






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





"""simple docstring"""






# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{}
lowercase__  		=		{}
lowercase__  		=		{}







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__ = None							,					):
				_lowerCamelCase  :    str					=					aliases if aliases is not None else []
				if format_type in _FORMAT_TYPES:
								logger.warning(
								    f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''	)
				_lowerCamelCase  :    str					=					formatter_cls
				for alias in set(aliases + [format_type]	):
								if alias in _FORMAT_TYPES_ALIASES:
												logger.warning(
												    f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''	)
								_lowerCamelCase  :    Dict					=					format_type







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__ = None	):
				_lowerCamelCase  :    Dict					=					aliases if aliases is not None else []
				for alias in set(aliases + [format_type]	):
								_lowerCamelCase  :    Optional[Any]					=					unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
			from .torch_formatter import TorchFormatter

			_register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
			lowercase__  		=		ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
			_register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
			from .tf_formatter import TFFormatter

			_register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
			lowercase__  		=		ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
			_register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
			from .jax_formatter import JaxFormatter

			_register_formatter(JaxFormatter, """jax""", aliases=[])
else:
			lowercase__  		=		ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
			_register_unavailable_formatter(_jax_error, """jax""", aliases=[])







def   _snake_case    (       lowercase__	):
				if format_type in _FORMAT_TYPES_ALIASES:
								return _FORMAT_TYPES_ALIASES[format_type]
				else:
								return format_type







def   _snake_case    (       lowercase__							,					**lowercase__	):
				_lowerCamelCase  :    List[str]					=					get_format_type_from_alias(lowercase__	)
				if format_type in _FORMAT_TYPES:
								return _FORMAT_TYPES[format_type](**lowercase__	)
				if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
								raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
				else:
								raise ValueError(
								    f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None	)}, but got \'{format_type}\''''	)