"""simple docstring"""
import functools
import operator

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


lowercase__ 	=     logging.get_logger(__name__)

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




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		"""sew-d"""



      def __init__(						self   ,      lowercase=32   ,      lowercase=7_68   ,      lowercase=12   ,      lowercase=12   ,      lowercase=30_72   ,      lowercase=2   ,      lowercase=5_12   ,      lowercase=2_56   ,      lowercase=True   ,      lowercase=True   ,      lowercase=("p2c", "c2p")   ,      lowercase="layer_norm"   ,      lowercase="gelu_python"   ,      lowercase=0.1   ,      lowercase=0.1   ,      lowercase=0.1   ,      lowercase=0.0   ,      lowercase=0.1   ,      lowercase=0.02   ,      lowercase=1e-7   ,      lowercase=1e-5   ,      lowercase="group"   ,      lowercase="gelu"   ,      lowercase=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)   ,      lowercase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,      lowercase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,      lowercase=False   ,      lowercase=1_28   ,      lowercase=16   ,      lowercase=True   ,      lowercase=0.05   ,      lowercase=10   ,      lowercase=2   ,      lowercase=0.0   ,      lowercase=10   ,      lowercase=0   ,      lowercase="mean"   ,      lowercase=False   ,      lowercase=False   ,      lowercase=2_56   ,      lowercase=0   ,      lowercase=1   ,      lowercase=2   ,      **lowercase   ,      )     ->      Optional[int]:





        '''simple docstring'''





        super().__init__(**lowercase   ,      pad_token_id=lowercase   ,      bos_token_id=lowercase   ,      eos_token_id=lowercase)
        a__:					Dict      							=     hidden_size
        a__:					List[str]      							=     feat_extract_norm
        a__:					Tuple      							=     feat_extract_activation
        a__:					Union[str, Any]      							=     list(lowercase)
        a__:					Union[str, Any]      							=     list(lowercase)
        a__:					str      							=     list(lowercase)
        a__:					List[Any]      							=     conv_bias
        a__:					List[Any]      							=     num_conv_pos_embeddings
        a__:					Any      							=     num_conv_pos_embedding_groups
        a__:					Union[str, Any]      							=     len(self.conv_dim)
        a__:					Optional[Any]      							=     num_hidden_layers
        a__:					str      							=     intermediate_size
        a__:					int      							=     squeeze_factor
        a__:					Dict      							=     max_position_embeddings
        a__:					List[str]      							=     position_buckets
        a__:					Tuple      							=     share_att_key
        a__:					str      							=     relative_attention
        a__:					str      							=     norm_rel_ebd
        a__:					Any      							=     list(lowercase)
        a__:					str      							=     hidden_act
        a__:					str      							=     num_attention_heads
        a__:					Dict      							=     hidden_dropout
        a__:					int      							=     attention_dropout
        a__:					Optional[Any]      							=     activation_dropout
        a__:					Dict      							=     feat_proj_dropout
        a__:					Dict      							=     final_dropout
        a__:					List[str]      							=     layer_norm_eps
        a__:					List[Any]      							=     feature_layer_norm_eps
        a__:					int      							=     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__:					Tuple      							=     apply_spec_augment
        a__:					List[str]      							=     mask_time_prob
        a__:					List[Any]      							=     mask_time_length
        a__:					Optional[int]      							=     mask_time_min_masks
        a__:					List[str]      							=     mask_feature_prob
        a__:					List[Any]      							=     mask_feature_length
        a__:					Union[str, Any]      							=     mask_feature_min_masks

        # ctc loss
        a__:					Optional[Any]      							=     ctc_loss_reduction
        a__:					str      							=     ctc_zero_infinity

        # sequence classification
        a__:					Any      							=     use_weighted_layer_sum
        a__:					Optional[Any]      							=     classifier_proj_size



      @property
      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        return functools.reduce(operator.mul   ,      self.conv_stride   ,      1)



"""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        __snake_case	(					unittest.TestCase    ):



      @slow
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





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

            a__:					Any      							=     FlaxAutoModel.from_pretrained(lowercase)
            self.assertIsNotNone(lowercase)
            self.assertIsInstance(lowercase   ,      lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





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

            a__:					Any      							=     FlaxAutoModel.from_pretrained(lowercase)
            self.assertIsNotNone(lowercase)
            self.assertIsInstance(lowercase   ,      lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        for model_name in ["bert-base-cased", "bert-large-uncased"]:
          a__:					Dict      							=     AutoTokenizer.from_pretrained(lowercase)
          a__:					Union[str, Any]      							=     FlaxBertModel.from_pretrained(lowercase)
          a__:					List[str]      							=     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)     ->      str:





        '''simple docstring'''





        for model_name in ["roberta-base", "roberta-large"]:
          a__:					Optional[Any]      							=     AutoTokenizer.from_pretrained(lowercase)
          a__:					Any      							=     FlaxRobertaModel.from_pretrained(lowercase)
          a__:					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)     ->      Any:





        '''simple docstring'''





        with self.assertRaisesRegex(
            lowercase   ,      'bert-base is not a local folder and is not a valid model identifier'):
          a__:					str      							=     FlaxAutoModel.from_pretrained('bert-base')



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        with self.assertRaisesRegex(
            lowercase   ,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
          a__:					List[str]      							=     FlaxAutoModel.from_pretrained(lowercase   ,      revision='aaaaaa')



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        with self.assertRaisesRegex(
            lowercase   ,      'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'   ,      ):
          a__:					List[str]      							=     FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model')



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        with self.assertRaisesRegex(lowercase   ,      'Use `from_pt=True` to load this model'):
          a__:					List[str]      							=     FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only')


