



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    __UpperCAmelCase			  =			'''platform'''

    import jax
    import jax.numpy as jnp

    from transformers import BlenderbotTokenizer
    from transformers.models.blenderbot.modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        shift_tokens_right,
    )
def 						UpperCamelCase      (  snake_case__			:       Union[str, Any]			,	snake_case__			:       Dict			,	snake_case__			:       int=None			,	snake_case__			:       Optional[int]=None			,	snake_case__			:       Any=None			,	snake_case__			:       Optional[int]=None			,	snake_case__			:       Any=None			,	snake_case__			:       Tuple=None			,	)      ->	Optional[int]:
   if attention_mask is None:
      UpperCamelCase       :    Optional[Any]		      =      np.where(input_ids != config.pad_token_id			,	1			,	0      )
   if decoder_attention_mask is None:
      UpperCamelCase       :    List[Any]		      =      np.where(decoder_input_ids != config.pad_token_id			,	1			,	0      )
   if head_mask is None:
      UpperCamelCase       :    List[str]		      =      np.ones((config.encoder_layers, config.encoder_attention_heads)      )
   if decoder_head_mask is None:
      UpperCamelCase       :    Optional[int]		      =      np.ones((config.decoder_layers, config.decoder_attention_heads)      )
   if cross_attn_head_mask is None:
      UpperCamelCase       :    Optional[int]		      =      np.ones((config.decoder_layers, config.decoder_attention_heads)      )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
   }



class 							lowerCAmelCase_						:
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=13,     SCREAMING_SNAKE_CASE_=7,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=99,     SCREAMING_SNAKE_CASE_=16,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=4,     SCREAMING_SNAKE_CASE_=4,     SCREAMING_SNAKE_CASE_="gelu",     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=32,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=1,     SCREAMING_SNAKE_CASE_=0,     SCREAMING_SNAKE_CASE_=0.02,     )	->						int:
          UpperCamelCase       :    Dict		      =      parent
          UpperCamelCase       :    Dict		      =      batch_size
          UpperCamelCase       :    Optional[int]		      =      seq_length
          UpperCamelCase       :    Any		      =      is_training
          UpperCamelCase       :    Optional[Any]		      =      use_labels
          UpperCamelCase       :    str		      =      vocab_size
          UpperCamelCase       :    Dict		      =      hidden_size
          UpperCamelCase       :    Any		      =      num_hidden_layers
          UpperCamelCase       :    Union[str, Any]		      =      num_attention_heads
          UpperCamelCase       :    str		      =      intermediate_size
          UpperCamelCase       :    List[str]		      =      hidden_act
          UpperCamelCase       :    Optional[Any]		      =      hidden_dropout_prob
          UpperCamelCase       :    List[str]		      =      attention_probs_dropout_prob
          UpperCamelCase       :    Union[str, Any]		      =      max_position_embeddings
          UpperCamelCase       :    Optional[Any]		      =      eos_token_id
          UpperCamelCase       :    int		      =      pad_token_id
          UpperCamelCase       :    Tuple		      =      bos_token_id
          UpperCamelCase       :    List[str]		      =      initializer_range
       def    snake_case_			(     self			)	->						str:
          UpperCamelCase       :    Tuple		      =      np.clip(ids_tensor([self.batch_size, self.seq_length - 1],     self.vocab_size			),     3,     self.vocab_size			)
          UpperCamelCase       :    Any		      =      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),     dtype=np.intaa			)),     -1			)

          UpperCamelCase       :    Tuple		      =      shift_tokens_right(SCREAMING_SNAKE_CASE_,     1,     2			)

          UpperCamelCase       :    Union[str, Any]		      =      BlenderbotConfig(
              vocab_size=self.vocab_size,     d_model=self.hidden_size,     encoder_layers=self.num_hidden_layers,     decoder_layers=self.num_hidden_layers,     encoder_attention_heads=self.num_attention_heads,     decoder_attention_heads=self.num_attention_heads,     encoder_ffn_dim=self.intermediate_size,     decoder_ffn_dim=self.intermediate_size,     dropout=self.hidden_dropout_prob,     attention_dropout=self.attention_probs_dropout_prob,     max_position_embeddings=self.max_position_embeddings,     eos_token_id=self.eos_token_id,     bos_token_id=self.bos_token_id,     pad_token_id=self.pad_token_id,     initializer_range=self.initializer_range,     use_cache=SCREAMING_SNAKE_CASE_,     )
          UpperCamelCase       :    Any		      =      prepare_blenderbot_inputs_dict(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          return config, inputs_dict
       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase      ,	UpperCamelCase       :    Optional[int]		      =      self.prepare_config_and_inputs()
          return config, inputs_dict
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Any:
          UpperCamelCase       :    str		      =      20
          UpperCamelCase       :    Union[str, Any]		      =      model_class_name(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[Any]		      =      model.encode(inputs_dict['input_ids']			)

          UpperCamelCase      ,	UpperCamelCase       :    Optional[int]		      =      (
              inputs_dict['decoder_input_ids'],
              inputs_dict['decoder_attention_mask'],
          )

          UpperCamelCase       :    int		      =      model.init_cache(decoder_input_ids.shape[0],     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[Any]		      =      jnp.ones((decoder_input_ids.shape[0], max_decoder_length),     dtype='i4'			)

          UpperCamelCase       :    Optional[Any]		      =      jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :],     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),     )
          UpperCamelCase       :    str		      =      model.decode(
              decoder_input_ids[:, :-1],     SCREAMING_SNAKE_CASE_,     decoder_attention_mask=SCREAMING_SNAKE_CASE_,     past_key_values=SCREAMING_SNAKE_CASE_,     decoder_position_ids=SCREAMING_SNAKE_CASE_,     )

          UpperCamelCase       :    Optional[Any]		      =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],     dtype='i4'			)
          UpperCamelCase       :    Tuple		      =      model.decode(
              decoder_input_ids[:, -1:],     SCREAMING_SNAKE_CASE_,     decoder_attention_mask=SCREAMING_SNAKE_CASE_,     past_key_values=outputs_cache.past_key_values,     decoder_position_ids=SCREAMING_SNAKE_CASE_,     )

          UpperCamelCase       :    List[Any]		      =      model.decode(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Optional[Any]		      =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
          self.parent.assertTrue(diff < 1e-3,     msg=F"""Max diff is {diff}"""			)






       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						str:
          UpperCamelCase       :    str		      =      20
          UpperCamelCase       :    List[str]		      =      model_class_name(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Union[str, Any]		      =      model.encode(inputs_dict['input_ids']			)

          UpperCamelCase      ,	UpperCamelCase       :    Optional[Any]		      =      (
              inputs_dict['decoder_input_ids'],
              inputs_dict['decoder_attention_mask'],
          )

          UpperCamelCase       :    Any		      =      jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
              ],     axis=-1,     )

          UpperCamelCase       :    Dict		      =      model.init_cache(decoder_input_ids.shape[0],     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Union[str, Any]		      =      jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :],     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),     )

          UpperCamelCase       :    Tuple		      =      model.decode(
              decoder_input_ids[:, :-1],     SCREAMING_SNAKE_CASE_,     decoder_attention_mask=SCREAMING_SNAKE_CASE_,     past_key_values=SCREAMING_SNAKE_CASE_,     decoder_position_ids=SCREAMING_SNAKE_CASE_,     )
          UpperCamelCase       :    List[Any]		      =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],     dtype='i4'			)
          UpperCamelCase       :    int		      =      model.decode(
              decoder_input_ids[:, -1:],     SCREAMING_SNAKE_CASE_,     past_key_values=outputs_cache.past_key_values,     decoder_attention_mask=SCREAMING_SNAKE_CASE_,     decoder_position_ids=SCREAMING_SNAKE_CASE_,     )

          UpperCamelCase       :    List[Any]		      =      model.decode(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     decoder_attention_mask=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[Any]		      =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
          self.parent.assertTrue(diff < 1e-3,     msg=F"""Max diff is {diff}"""			)






@require_flax
class 							lowerCAmelCase_						(    unittest.TestCase				):
       UpperCAmelCase__							:   List[str]  	=    99
       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase       :    List[str]		      =      np.array(
              [
                  [71, 82, 18, 33, 46, 91, 2],
                  [68, 34, 26, 58, 30, 82, 2],
                  [5, 97, 17, 39, 94, 40, 2],
                  [76, 83, 94, 25, 70, 78, 2],
                  [87, 59, 41, 35, 48, 66, 2],
                  [55, 13, 16, 58, 5, 2, 1],  # note padding
                  [64, 27, 31, 51, 12, 75, 2],
                  [52, 64, 86, 17, 83, 39, 2],
                  [48, 61, 9, 24, 71, 82, 2],
                  [26, 1, 60, 48, 22, 13, 2],
                  [21, 5, 62, 28, 14, 76, 2],
                  [45, 98, 37, 86, 59, 48, 2],
                  [70, 70, 50, 9, 28, 0, 2],
              ],     dtype=np.intaa,     )

          UpperCamelCase       :    int		      =      input_ids.shape[0]
          UpperCamelCase       :    Optional[int]		      =      BlenderbotConfig(
              vocab_size=self.vocab_size,     d_model=24,     encoder_layers=2,     decoder_layers=2,     encoder_attention_heads=2,     decoder_attention_heads=2,     encoder_ffn_dim=32,     decoder_ffn_dim=32,     max_position_embeddings=48,     eos_token_id=2,     pad_token_id=1,     bos_token_id=0,     )
          return config, input_ids, batch_size
       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase       :    List[Any]		      =      self._get_config_and_data()
          UpperCamelCase       :    Union[str, Any]		      =      FlaxBlenderbotForConditionalGeneration(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[int]		      =      lm_model(input_ids=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Union[str, Any]		      =      (batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs['logits'].shape,     SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase       :    List[str]		      =      BlenderbotConfig(
              vocab_size=self.vocab_size,     d_model=14,     encoder_layers=2,     decoder_layers=2,     encoder_attention_heads=2,     decoder_attention_heads=2,     encoder_ffn_dim=8,     decoder_ffn_dim=8,     max_position_embeddings=48,     )
          UpperCamelCase       :    Dict		      =      FlaxBlenderbotForConditionalGeneration(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    int		      =      np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],     dtype=np.intaa			)
          UpperCamelCase       :    Dict		      =      np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],     dtype=np.intaa			)
          UpperCamelCase       :    Any		      =      lm_model(input_ids=SCREAMING_SNAKE_CASE_,     decoder_input_ids=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[int]		      =      (*summary.shape, config.vocab_size)
          self.assertEqual(outputs['logits'].shape,     SCREAMING_SNAKE_CASE_			)






       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase       :    int		      =      np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],     dtype=np.intaa			)
          UpperCamelCase       :    int		      =      shift_tokens_right(SCREAMING_SNAKE_CASE_,     1,     2			)
          UpperCamelCase       :    Tuple		      =      np.equal(SCREAMING_SNAKE_CASE_,     1			).astype(np.floataa			).sum()
          UpperCamelCase       :    int		      =      np.equal(SCREAMING_SNAKE_CASE_,     1			).astype(np.floataa			).sum()
          self.assertEqual(shifted.shape,     input_ids.shape			)
          self.assertEqual(SCREAMING_SNAKE_CASE_,     n_pad_before - 1			)
          self.assertTrue(np.equal(shifted[:, 0],     2			).all()			)






@require_flax
class 							lowerCAmelCase_						(    a__			,       unittest.TestCase			,       a__				):
       UpperCAmelCase__							:   Any  	=    True
       UpperCAmelCase__							:   List[Any]  	=    (
           (
               FlaxBlenderbotModel,
               FlaxBlenderbotForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       UpperCAmelCase__							:   Optional[Any]  	=    (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
       def    snake_case_			(     self			)	->						Tuple:
          UpperCamelCase       :    Dict		      =      FlaxBlenderbotModelTester(self			)
       def    snake_case_			(     self			)	->						Dict:
          UpperCamelCase      ,	UpperCamelCase       :    Dict		      =      self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase      ,	UpperCamelCase       :    List[str]		      =      self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase      ,	UpperCamelCase       :    Optional[int]		      =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__			):
                UpperCamelCase       :    Tuple		      =      self._prepare_for_class(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
                UpperCamelCase       :    List[str]		      =      model_class(SCREAMING_SNAKE_CASE_			)

                @jax.jit
                def encode_jitted(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=None,     **SCREAMING_SNAKE_CASE_			):
                   return model.encode(input_ids=SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_			)

                with self.subTest('JIT Enabled'			):
                   UpperCamelCase       :    Optional[int]		      =      encode_jitted(**SCREAMING_SNAKE_CASE_			).to_tuple()

                with self.subTest('JIT Disabled'			):
                   with jax.disable_jit():
                      UpperCamelCase       :    int		      =      encode_jitted(**SCREAMING_SNAKE_CASE_			).to_tuple()

                self.assertEqual(len(SCREAMING_SNAKE_CASE_			),     len(SCREAMING_SNAKE_CASE_			)			)
                for jitted_output, output in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
                   self.assertEqual(jitted_output.shape,     output.shape			)
       def    snake_case_			(     self			)	->						Dict:
          UpperCamelCase      ,	UpperCamelCase       :    List[Any]		      =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__			):
                UpperCamelCase       :    List[str]		      =      model_class(SCREAMING_SNAKE_CASE_			)
                UpperCamelCase       :    Union[str, Any]		      =      model.encode(inputs_dict['input_ids'],     inputs_dict['attention_mask']			)

                UpperCamelCase       :    int		      =      {
                    'decoder_input_ids': inputs_dict['decoder_input_ids'],
                    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                    'encoder_outputs': encoder_outputs,
                }

                @jax.jit
                def decode_jitted(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
                   return model.decode(
                       decoder_input_ids=SCREAMING_SNAKE_CASE_,     decoder_attention_mask=SCREAMING_SNAKE_CASE_,     encoder_outputs=SCREAMING_SNAKE_CASE_,     )

                with self.subTest('JIT Enabled'			):
                   UpperCamelCase       :    Dict		      =      decode_jitted(**SCREAMING_SNAKE_CASE_			).to_tuple()

                with self.subTest('JIT Disabled'			):
                   with jax.disable_jit():
                      UpperCamelCase       :    Optional[Any]		      =      decode_jitted(**SCREAMING_SNAKE_CASE_			).to_tuple()

                self.assertEqual(len(SCREAMING_SNAKE_CASE_			),     len(SCREAMING_SNAKE_CASE_			)			)
                for jitted_output, output in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
                   self.assertEqual(jitted_output.shape,     output.shape			)
       @slow
       def    snake_case_			(     self			)	->						Dict:
          for model_class_name in self.all_model_classes:
             UpperCamelCase       :    Optional[Any]		      =      model_class_name.from_pretrained('facebook/blenderbot-400M-distill'			)
             # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
             UpperCamelCase       :    List[str]		      =      np.ones((1, 1)			) * model.config.eos_token_id
             UpperCamelCase       :    str		      =      model(SCREAMING_SNAKE_CASE_			)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)






       @unittest.skipUnless(jax_device != 'cpu',     '3B test too slow on CPU.'			)
       @slow
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Tuple		      =      {'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
          UpperCamelCase       :    Any		      =      {'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

          UpperCamelCase       :    str		      =      FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B',     from_pt=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[Any]		      =      BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'			)

          UpperCamelCase       :    List[str]		      =      ['Sam']
          UpperCamelCase       :    str		      =      tokenizer(SCREAMING_SNAKE_CASE_,     return_tensors='jax'			)

          UpperCamelCase       :    int		      =      model.generate(**SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[Any]		      =      'Sam is a great name. It means "sun" in Gaelic.'

          UpperCamelCase       :    Union[str, Any]		      =      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
          assert generated_txt[0].strip() == tgt_text




"""simple docstring"""



import functools
import operator

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


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "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 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'sew'





			def __init__(			self   ,      snake_case_=32   ,      snake_case_=768   ,      snake_case_=12   ,      snake_case_=12   ,      snake_case_=3_072   ,      snake_case_=2   ,      snake_case_="gelu"   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.0   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.02   ,      snake_case_=1E-5   ,      snake_case_="group"   ,      snake_case_="gelu"   ,      snake_case_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,      snake_case_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,      snake_case_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,      snake_case_=False   ,      snake_case_=128   ,      snake_case_=16   ,      snake_case_=True   ,      snake_case_=0.05   ,      snake_case_=10   ,      snake_case_=2   ,      snake_case_=0.0   ,      snake_case_=10   ,      snake_case_=0   ,      snake_case_="mean"   ,      snake_case_=False   ,      snake_case_=False   ,      snake_case_=256   ,      snake_case_=0   ,      snake_case_=1   ,      snake_case_=2   ,      **snake_case_   ,      ):







										'''simple docstring'''


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

										# ctc loss
										A__       :     str								=			ctc_loss_reduction
										A__       :     List[Any]								=			ctc_zero_infinity

										# sequence classification
										A__       :     Union[str, Any]								=			use_weighted_layer_sum
										A__       :     str								=			classifier_proj_size





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





