


'''simple docstring'''




import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class 					a			:
 '''simple docstring'''




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_=1_3		,      lowerCamelCase_=7		,      lowerCamelCase_=True		,      lowerCamelCase_=True		,      lowerCamelCase_=False		,      lowerCamelCase_=True		,      lowerCamelCase_=9_9		,      lowerCamelCase_=3_2		,      lowerCamelCase_=5		,      lowerCamelCase_=4		,      lowerCamelCase_=3_7		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=5_1_2		,      lowerCamelCase_=1_6		,      lowerCamelCase_=2		,      lowerCamelCase_=0.02		,      lowerCamelCase_=3		,      lowerCamelCase_=4		,      lowerCamelCase_=None		,      )      ->    str:
    _a					:  int						=  parent
    _a					:  str						=  batch_size
    _a					:  Any						=  seq_length
    _a					:  Tuple						=  is_training
    _a					:  int						=  use_input_mask
    _a					:  Any						=  use_token_type_ids
    _a					:  List[Any]						=  use_labels
    _a					:  Optional[int]						=  vocab_size
    _a					:  str						=  hidden_size
    _a					:  Any						=  num_hidden_layers
    _a					:  List[Any]						=  num_attention_heads
    _a					:  Tuple						=  intermediate_size
    _a					:  Optional[int]						=  hidden_act
    _a					:  str						=  hidden_dropout_prob
    _a					:  Tuple						=  attention_probs_dropout_prob
    _a					:  List[Any]						=  max_position_embeddings
    _a					:  Union[str, Any]						=  type_vocab_size
    _a					:  Dict						=  type_sequence_label_size
    _a					:  List[Any]						=  initializer_range
    _a					:  Optional[Any]						=  num_labels
    _a					:  Dict						=  num_choices
    _a					:  Dict						=  scope




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Any						=  ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size      )

    _a					:  str						=  None
    if self.use_input_mask:
       _a					:  str						=  random_attention_mask([self.batch_size, self.seq_length]      )

    _a					:  int						=  None
    if self.use_token_type_ids:
       _a					:  Tuple						=  ids_tensor([self.batch_size, self.seq_length]		,      self.type_vocab_size      )

    _a					:  Union[str, Any]						=  None
    _a					:  Any						=  None
    _a					:  Optional[Any]						=  None
    if self.use_labels:
       _a					:  Dict						=  ids_tensor([self.batch_size]		,      self.type_sequence_label_size      )
       _a					:  str						=  ids_tensor([self.batch_size, self.seq_length]		,      self.num_labels      )
       _a					:  List[Any]						=  ids_tensor([self.batch_size]		,      self.num_choices      )

    _a					:  List[str]						=  self.get_config()

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    return LlamaConfig(
        vocab_size=self.vocab_size		,      hidden_size=self.hidden_size		,      num_hidden_layers=self.num_hidden_layers		,      num_attention_heads=self.num_attention_heads		,      intermediate_size=self.intermediate_size		,      hidden_act=self.hidden_act		,      hidden_dropout_prob=self.hidden_dropout_prob		,      attention_probs_dropout_prob=self.attention_probs_dropout_prob		,      max_position_embeddings=self.max_position_embeddings		,      type_vocab_size=self.type_vocab_size		,      is_decoder=lowerCamelCase_		,      initializer_range=self.initializer_range		,      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    int:
    _a					:  List[Any]						=  LlamaModel(config=lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  Any						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_      )
    _a					:  List[Any]						=  model(lowerCamelCase_      )
    self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      )      ->    Any:
    _a					:  Dict						=  True
    _a					:  Tuple						=  LlamaModel(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  List[Any]						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      encoder_attention_mask=lowerCamelCase_		,      )
    _a					:  Optional[Any]						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      )
    _a					:  str						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_      )
    self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      )      ->    Dict:
    _a					:  Dict						=  LlamaForCausalLM(config=lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  int						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      labels=lowerCamelCase_      )
    self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.vocab_size)      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      )      ->    str:
    _a					:  Optional[Any]						=  True
    _a					:  Tuple						=  True
    _a					:  Optional[int]						=  LlamaForCausalLM(config=lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()

    # first forward pass
    _a					:  str						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      encoder_attention_mask=lowerCamelCase_		,      use_cache=lowerCamelCase_		,      )
    _a					:  Union[str, Any]						=  outputs.past_key_values

    # create hypothetical multiple next token and extent to next_input_ids
    _a					:  Optional[Any]						=  ids_tensor((self.batch_size, 3)		,      config.vocab_size      )
    _a					:  str						=  ids_tensor((self.batch_size, 3)		,      vocab_size=2      )

    # append to next input_ids and
    _a					:  Optional[Any]						=  torch.cat([input_ids, next_tokens]		,      dim=-1      )
    _a					:  List[Any]						=  torch.cat([input_mask, next_mask]		,      dim=-1      )

    _a					:  Any						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      encoder_attention_mask=lowerCamelCase_		,      output_hidden_states=lowerCamelCase_		,      )['hidden_states'][0]
    _a					:  List[Any]						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      encoder_attention_mask=lowerCamelCase_		,      past_key_values=lowerCamelCase_		,      output_hidden_states=lowerCamelCase_		,      )['hidden_states'][0]

    # select random slice
    _a					:  List[Any]						=  ids_tensor((1,)		,      output_from_past.shape[-1]      ).item()
    _a					:  int						=  output_from_no_past[:, -3:, random_slice_idx].detach()
    _a					:  Optional[Any]						=  output_from_past[:, :, random_slice_idx].detach()

    self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

    # test that outputs are equal for slice
    self.parent.assertTrue(torch.allclose(lowerCamelCase_		,      lowerCamelCase_		,      atol=1e-3      )      )




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  Optional[int]						=  self.prepare_config_and_inputs()
    (
        (
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						
    )					:  Dict						=  config_and_inputs
    _a					:  Tuple						=  {'input_ids': input_ids, 'attention_mask': input_mask}
    return config, inputs_dict







@require_torch
class 					a			(				snake_case__			,							snake_case__			,							snake_case__			,							unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[int]							  =		(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
 __lowerCAmelCase						: int							  =		(LlamaForCausalLM,) if is_torch_available() else ()
 __lowerCAmelCase						: int							  =		(
     {
         """feature-extraction""": LlamaModel,
         """text-classification""": LlamaForSequenceClassification,
         """text-generation""": LlamaForCausalLM,
         """zero-shot""": LlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowerCAmelCase						: str							  =		False
 __lowerCAmelCase						: List[Any]							  =		False




 def   __UpperCamelCase   (    self      )      ->    str:
    _a					:  Optional[int]						=  LlamaModelTester(self      )
    _a					:  Tuple						=  ConfigTester(self		,      config_class=lowerCamelCase_		,      hidden_size=3_7      )




 def   __UpperCamelCase   (    self      )      ->    Any:
    self.config_tester.run_common_tests()




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a					:  Dict						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  Tuple						=  self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
       _a					:  List[str]						=  type
       self.model_tester.create_and_check_model(*lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a		,						_a					:  int						=  self.model_tester.prepare_config_and_inputs_for_common()
    _a					:  Optional[Any]						=  3
    _a					:  Union[str, Any]						=  input_dict['input_ids']
    _a					:  List[Any]						=  input_ids.ne(1      ).to(lowerCamelCase_      )
    _a					:  int						=  ids_tensor([self.model_tester.batch_size]		,      self.model_tester.type_sequence_label_size      )
    _a					:  Any						=  LlamaForSequenceClassification(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  List[Any]						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      labels=lowerCamelCase_      )
    self.assertEqual(result.logits.shape		,      (self.model_tester.batch_size, self.model_tester.num_labels)      )




 def   __UpperCamelCase   (    self      )      ->    List[str]:
    _a		,						_a					:  Tuple						=  self.model_tester.prepare_config_and_inputs_for_common()
    _a					:  List[str]						=  3
    _a					:  List[str]						=  'single_label_classification'
    _a					:  Union[str, Any]						=  input_dict['input_ids']
    _a					:  str						=  input_ids.ne(1      ).to(lowerCamelCase_      )
    _a					:  Any						=  ids_tensor([self.model_tester.batch_size]		,      self.model_tester.type_sequence_label_size      )
    _a					:  str						=  LlamaForSequenceClassification(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  List[Any]						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      labels=lowerCamelCase_      )
    self.assertEqual(result.logits.shape		,      (self.model_tester.batch_size, self.model_tester.num_labels)      )




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a		,						_a					:  Union[str, Any]						=  self.model_tester.prepare_config_and_inputs_for_common()
    _a					:  Union[str, Any]						=  3
    _a					:  str						=  'multi_label_classification'
    _a					:  Union[str, Any]						=  input_dict['input_ids']
    _a					:  Union[str, Any]						=  input_ids.ne(1      ).to(lowerCamelCase_      )
    _a					:  str						=  ids_tensor(
        [self.model_tester.batch_size, config.num_labels]		,      self.model_tester.type_sequence_label_size      ).to(torch.float      )
    _a					:  str						=  LlamaForSequenceClassification(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  int						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      labels=lowerCamelCase_      )
    self.assertEqual(result.logits.shape		,      (self.model_tester.batch_size, self.model_tester.num_labels)      )




 @unittest.skip('LLaMA buffers include complex numbers, which breaks this test'      )
 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    pass




 @parameterized.expand([('linear',), ('dynamic',)]      )
 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    List[str]:
    _a		,						_a					:  Dict						=  self.model_tester.prepare_config_and_inputs_for_common()
    _a					:  Union[str, Any]						=  ids_tensor([1, 1_0]		,      config.vocab_size      )
    _a					:  Optional[Any]						=  ids_tensor([1, int(config.max_position_embeddings * 1.5      )]		,      config.vocab_size      )

    set_seed(4_2      )  # Fixed seed at init time so the two models get the same random weights
    _a					:  int						=  LlamaModel(lowerCamelCase_      )
    original_model.to(lowerCamelCase_      )
    original_model.eval()
    _a					:  str						=  original_model(lowerCamelCase_      ).last_hidden_state
    _a					:  Union[str, Any]						=  original_model(lowerCamelCase_      ).last_hidden_state

    set_seed(4_2      )  # Fixed seed at init time so the two models get the same random weights
    _a					:  Optional[Any]						=  {'type': scaling_type, 'factor': 10.0}
    _a					:  Any						=  LlamaModel(lowerCamelCase_      )
    scaled_model.to(lowerCamelCase_      )
    scaled_model.eval()
    _a					:  Optional[int]						=  scaled_model(lowerCamelCase_      ).last_hidden_state
    _a					:  int						=  scaled_model(lowerCamelCase_      ).last_hidden_state

    # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
    # maximum sequence length, so the outputs for the short input should match.
    if scaling_type == "dynamic":
       self.assertTrue(torch.allclose(lowerCamelCase_		,      lowerCamelCase_		,      atol=1e-5      )      )
    else:
       self.assertFalse(torch.allclose(lowerCamelCase_		,      lowerCamelCase_		,      atol=1e-5      )      )

    # The output should be different for long inputs
    self.assertFalse(torch.allclose(lowerCamelCase_		,      lowerCamelCase_		,      atol=1e-5      )      )







@require_torch
class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    int:
    _a					:  Optional[Any]						=  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
    _a					:  Optional[int]						=  LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'		,      device_map='auto'      )
    _a					:  Tuple						=  model(torch.tensor([input_ids]      )      )
    # Expected mean on dim = -1
    _a					:  List[Any]						=  torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]      )
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )
    # slicing logits[0, 0, 0:30]
    # fmt: off
    _a					:  str						=  torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]      )
    # fmt: on
    torch.testing.assert_close(out[0, 0, :3_0]		,      lowerCamelCase_		,      atol=1e-5		,      rtol=1e-5      )




 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    _a					:  Any						=  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
    _a					:  Any						=  LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'		,      device_map='auto'      )
    _a					:  List[Any]						=  model(torch.tensor(lowerCamelCase_      )      )
    # Expected mean on dim = -1
    _a					:  Optional[int]						=  torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]      )
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )
    # slicing logits[0, 0, 0:30]
    # fmt: off
    _a					:  List[Any]						=  torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]      )
    # fmt: on
    torch.testing.assert_close(out[0, 0, :3_0]		,      lowerCamelCase_		,      atol=1e-5		,      rtol=1e-5      )




 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  Optional[int]						=  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
    _a					:  Union[str, Any]						=  LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'		,      device_map='auto'      )
    _a					:  str						=  model(torch.tensor(lowerCamelCase_      )      )
    # Expected mean on dim = -1
    _a					:  int						=  torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]      )
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )
    # slicing logits[0, 0, 0:30]
    # fmt: off
    _a					:  List[Any]						=  torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]      )
    # fmt: on
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )




 @unittest.skip(
     'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Dict						=  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
    _a					:  str						=  LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'		,      device_map='auto'      )
    _a					:  Dict						=  model(torch.tensor(lowerCamelCase_      )      )

    _a					:  str						=  torch.tensor(
        [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]		,      dtype=torch.floataa      )
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )
    # fmt: off
    _a					:  Any						=  torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]      )
    # fmt: on
    torch.testing.assert_close(out[0, 0, :3_0]		,      lowerCamelCase_		,      atol=1e-5		,      rtol=1e-5      )




 @unittest.skip('Model is curently gated'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  List[Any]						=  'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
    _a					:  List[Any]						=  'Simply put, the theory of relativity states that '
    _a					:  List[Any]						=  LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'      )
    _a					:  Any						=  tokenizer.encode(lowerCamelCase_		,      return_tensors='pt'      )
    _a					:  Union[str, Any]						=  LlamaForCausalLM.from_pretrained(
        'meta-llama/Llama-2-13b-chat-hf'		,      device_map='sequential'		,      use_safetensors=lowerCamelCase_      )

    # greedy generation outputs
    _a					:  List[Any]						=  model.generate(lowerCamelCase_		,      max_new_tokens=6_4		,      top_p=lowerCamelCase_		,      temperature=1		,      do_sample=lowerCamelCase_      )
    _a					:  Dict						=  tokenizer.decode(generated_ids[0]		,      skip_special_tokens=lowerCamelCase_      )
    self.assertEqual(lowerCamelCase_		,      lowerCamelCase_      )






'''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      )


