


'''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 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 @slow
 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    for model_name in ["bert-base-cased", "bert-large-uncased"]:
       with self.subTest(lowerCamelCase_      ):
          _a					:  Dict						=  AutoConfig.from_pretrained(lowerCamelCase_      )
          self.assertIsNotNone(lowerCamelCase_      )
          self.assertIsInstance(lowerCamelCase_		,      lowerCamelCase_      )

          _a					:  str						=  FlaxAutoModel.from_pretrained(lowerCamelCase_      )
          self.assertIsNotNone(lowerCamelCase_      )
          self.assertIsInstance(lowerCamelCase_		,      lowerCamelCase_      )




 @slow
 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    for model_name in ["roberta-base", "roberta-large"]:
       with self.subTest(lowerCamelCase_      ):
          _a					:  Any						=  AutoConfig.from_pretrained(lowerCamelCase_      )
          self.assertIsNotNone(lowerCamelCase_      )
          self.assertIsInstance(lowerCamelCase_		,      lowerCamelCase_      )

          _a					:  Optional[Any]						=  FlaxAutoModel.from_pretrained(lowerCamelCase_      )
          self.assertIsNotNone(lowerCamelCase_      )
          self.assertIsInstance(lowerCamelCase_		,      lowerCamelCase_      )




 @slow
 def   __UpperCamelCase   (    self      )      ->    List[str]:
    for model_name in ["bert-base-cased", "bert-large-uncased"]:
       _a					:  Tuple						=  AutoTokenizer.from_pretrained(lowerCamelCase_      )
       _a					:  List[Any]						=  FlaxBertModel.from_pretrained(lowerCamelCase_      )
       _a					:  List[Any]						=  tokenizer('Do you support jax jitted function?'		,      return_tensors=TensorType.JAX      )

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

       eval(**lowerCamelCase_      ).block_until_ready()




 @slow
 def   __UpperCamelCase   (    self      )      ->    str:
    for model_name in ["roberta-base", "roberta-large"]:
       _a					:  Tuple						=  AutoTokenizer.from_pretrained(lowerCamelCase_      )
       _a					:  Any						=  FlaxRobertaModel.from_pretrained(lowerCamelCase_      )
       _a					:  Optional[Any]						=  tokenizer('Do you support jax jitted function?'		,      return_tensors=TensorType.JAX      )

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

       eval(**lowerCamelCase_      ).block_until_ready()




 def   __UpperCamelCase   (    self      )      ->    Any:
    with self.assertRaisesRegex(
        lowerCamelCase_		,      'bert-base is not a local folder and is not a valid model identifier'      ):
       _a					:  Dict						=  FlaxAutoModel.from_pretrained('bert-base'      )




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    with self.assertRaisesRegex(
        lowerCamelCase_		,      R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'      ):
       _a					:  Union[str, Any]						=  FlaxAutoModel.from_pretrained(lowerCamelCase_		,      revision='aaaaaa'      )




 def   __UpperCamelCase   (    self      )      ->    str:
    with self.assertRaisesRegex(
        lowerCamelCase_		,      'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'		,      ):
       _a					:  Tuple						=  FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'      )




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    with self.assertRaisesRegex(lowerCamelCase_		,      'Use `from_pt=True` to load this model'      ):
       _a					:  Union[str, Any]						=  FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'      )






'''simple docstring'''




import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_      :		Any          =		logging.get_logger(__name__)

UpperCAmelCase_      :		Any          =		{
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Tuple							  =		"""sew"""




 def __init__(    self		,      lowerCamelCase_=3_2		,      lowerCamelCase_=7_6_8		,      lowerCamelCase_=1_2		,      lowerCamelCase_=1_2		,      lowerCamelCase_=3_0_7_2		,      lowerCamelCase_=2		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.0		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.02		,      lowerCamelCase_=1e-5		,      lowerCamelCase_="group"		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)		,      lowerCamelCase_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,      lowerCamelCase_=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,      lowerCamelCase_=False		,      lowerCamelCase_=1_2_8		,      lowerCamelCase_=1_6		,      lowerCamelCase_=True		,      lowerCamelCase_=0.05		,      lowerCamelCase_=1_0		,      lowerCamelCase_=2		,      lowerCamelCase_=0.0		,      lowerCamelCase_=1_0		,      lowerCamelCase_=0		,      lowerCamelCase_="mean"		,      lowerCamelCase_=False		,      lowerCamelCase_=False		,      lowerCamelCase_=2_5_6		,      lowerCamelCase_=0		,      lowerCamelCase_=1		,      lowerCamelCase_=2		,      **lowerCamelCase_		,      )      ->    Tuple:
    super().__init__(**lowerCamelCase_		,      pad_token_id=lowerCamelCase_		,      bos_token_id=lowerCamelCase_		,      eos_token_id=lowerCamelCase_      )
    _a					:  Optional[int]						=  hidden_size
    _a					:  int						=  feat_extract_norm
    _a					:  Optional[int]						=  feat_extract_activation
    _a					:  str						=  list(lowerCamelCase_      )
    _a					:  Union[str, Any]						=  list(lowerCamelCase_      )
    _a					:  List[Any]						=  list(lowerCamelCase_      )
    _a					:  Union[str, Any]						=  conv_bias
    _a					:  Optional[int]						=  num_conv_pos_embeddings
    _a					:  Dict						=  num_conv_pos_embedding_groups
    _a					:  str						=  len(self.conv_dim      )
    _a					:  Any						=  num_hidden_layers
    _a					:  List[Any]						=  intermediate_size
    _a					:  Tuple						=  squeeze_factor
    _a					:  Tuple						=  hidden_act
    _a					:  Any						=  num_attention_heads
    _a					:  Optional[int]						=  hidden_dropout
    _a					:  List[str]						=  attention_dropout
    _a					:  Optional[Any]						=  activation_dropout
    _a					:  str						=  feat_proj_dropout
    _a					:  str						=  final_dropout
    _a					:  str						=  layerdrop
    _a					:  Optional[Any]						=  layer_norm_eps
    _a					:  Optional[Any]						=  initializer_range
    _a					:  Any						=  vocab_size

    if (
        (len(self.conv_stride      ) != self.num_feat_extract_layers)
        or (len(self.conv_kernel      ) != self.num_feat_extract_layers)
        or (len(self.conv_dim      ) != self.num_feat_extract_layers)
    ):
       raise ValueError(
           'Configuration for convolutional layers is incorrect.'
           'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
           F'''but is `len(config.conv_dim) = {len(self.conv_dim      )}`, `len(config.conv_stride)'''
           F'''= {len(self.conv_stride      )}`, `len(config.conv_kernel) = {len(self.conv_kernel      )}`.'''      )

    # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
    _a					:  str						=  apply_spec_augment
    _a					:  List[Any]						=  mask_time_prob
    _a					:  Optional[Any]						=  mask_time_length
    _a					:  Union[str, Any]						=  mask_time_min_masks
    _a					:  List[str]						=  mask_feature_prob
    _a					:  List[str]						=  mask_feature_length
    _a					:  str						=  mask_feature_min_masks

    # ctc loss
    _a					:  Any						=  ctc_loss_reduction
    _a					:  Optional[Any]						=  ctc_zero_infinity

    # sequence classification
    _a					:  List[Any]						=  use_weighted_layer_sum
    _a					:  Tuple						=  classifier_proj_size




 @property
 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    return functools.reduce(operator.mul		,      self.conv_stride		,      1      )


