
"""simple docstring"""

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


UpperCamelCase			:       Union[str, Any]									=				logging.get_logger(__name__)

UpperCamelCase			:       str									=				{
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"switch_transformers"
     lowercase		     =		["past_key_values"]
     lowercase		     =		{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}





     def __init__(			self			,  __UpperCAmelCase=3_2128			,  __UpperCAmelCase=768			,  __UpperCAmelCase=64			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=64			,  __UpperCAmelCase=12			,  __UpperCAmelCase=3			,  __UpperCAmelCase=12			,  __UpperCAmelCase=3			,  __UpperCAmelCase=12			,  __UpperCAmelCase=8			,  __UpperCAmelCase=False			,  __UpperCAmelCase=0.0_1			,  __UpperCAmelCase="float32"			,  __UpperCAmelCase=False			,  __UpperCAmelCase=32			,  __UpperCAmelCase=128			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=1E-6			,  __UpperCAmelCase=0.0_0_1			,  __UpperCAmelCase=0.0_0_1			,  __UpperCAmelCase=1.0			,  __UpperCAmelCase="relu"			,  __UpperCAmelCase=True			,  __UpperCAmelCase=False			,  __UpperCAmelCase=True			,  __UpperCAmelCase=0			,  __UpperCAmelCase=1			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= vocab_size
      __UpperCamelCase       						= d_model
      __UpperCamelCase       						= d_kv
      __UpperCamelCase       						= d_ff

      __UpperCamelCase       						= num_sparse_encoder_layers

      __UpperCamelCase       						= num_layers
      __UpperCamelCase       						= (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      __UpperCamelCase       						= num_sparse_decoder_layers

      # This tells us, each how many encoder layer we'll have to set a sparse layer.
      if self.num_sparse_encoder_layers > 0:
       __UpperCamelCase       						= self.num_layers // self.num_sparse_encoder_layers
      else:
       __UpperCamelCase       						= self.num_layers  # HACK: this will create 0 sparse layers

      # This tells us, each how many encoder layer we'll have to set a sparse layer.
      if self.num_sparse_decoder_layers > 0:
       __UpperCamelCase       						= self.num_decoder_layers // self.num_sparse_decoder_layers
      else:
       __UpperCamelCase       						= self.num_decoder_layers  # HACK: this will create 0 sparse layers

      __UpperCamelCase       						= num_heads
      __UpperCamelCase       						= num_experts
      __UpperCamelCase       						= expert_capacity
      __UpperCamelCase       						= router_bias
      __UpperCamelCase       						= router_jitter_noise
      if router_dtype not in ["float32", "float16", "bfloat16"]:
       raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'   )
      __UpperCamelCase       						= router_dtype

      __UpperCamelCase       						= router_ignore_padding_tokens
      __UpperCamelCase       						= relative_attention_num_buckets
      __UpperCamelCase       						= relative_attention_max_distance

      __UpperCamelCase       						= dropout_rate
      __UpperCamelCase       						= layer_norm_epsilon
      __UpperCamelCase       						= initializer_factor
      __UpperCamelCase       						= feed_forward_proj
      __UpperCamelCase       						= use_cache
      __UpperCamelCase       						= add_router_probs

      __UpperCamelCase       						= router_z_loss_coef
      __UpperCamelCase       						= router_aux_loss_coef

      __UpperCamelCase       						= self.feed_forward_proj.split('-'   )
      __UpperCamelCase       						= act_info[-1]
      __UpperCamelCase       						= act_info[0] == 'gated'

      if len(__UpperCAmelCase   ) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase   ) > 2:
       raise ValueError(
           F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
           'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
           '\'gated-gelu\' or \'relu\''   )

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
       __UpperCamelCase       						= 'gelu_new'

      super().__init__(
          pad_token_id=__UpperCAmelCase			,  eos_token_id=__UpperCAmelCase			,  is_encoder_decoder=__UpperCAmelCase			,  **__UpperCAmelCase			,  )







"""simple docstring"""

import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


UpperCamelCase			:       Union[str, Any]									=				"__DUMMY_TRANSFORMERS_USER__"
UpperCamelCase			:       List[Any]									=				"Dummy User"
UpperCamelCase			:       List[Any]									=				"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"

UpperCamelCase			:       Any									=				"https://hub-ci.huggingface.co"
UpperCamelCase			:       str									=				CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
UpperCamelCase			:       Optional[Any]									=				CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
UpperCamelCase			:       Optional[int]									=				Path("~/.huggingface/hub_ci_token").expanduser()






@pytest.fixture
def 							A		(       snake_case   :Optional[Any]	)   ->			Union[str, Any]:
 monkeypatch.setattr(
     'huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :str	)   ->			List[Any]:
 monkeypatch.setattr('datasets.config.HF_ENDPOINT'       ,      snake_case	)
 monkeypatch.setattr('datasets.config.HUB_DATASETS_URL'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :Optional[int]	)   ->			Dict:
 monkeypatch.setattr('huggingface_hub.hf_api.HfFolder.path_token'       ,      snake_case	)






@pytest.fixture
def 							A		(       snake_case   :List[Any]       ,      snake_case   :Tuple	)   ->			Tuple:
 HfFolder.save_token(snake_case	)
 yield
 HfFolder.delete_token()






@pytest.fixture(scope='session'	)
def 							A		(       )   ->			List[Any]:
 return HfApi(endpoint=snake_case	)






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi	)   ->			List[Any]:
 __UpperCamelCase       						= HfFolder.get_token()
 HfFolder.save_token(snake_case	)
 yield CI_HUB_USER_TOKEN
 if previous_token is not None:
  HfFolder.save_token(snake_case	)






@pytest.fixture
def 							A		(       snake_case   :str	)   ->			str:
 def _cleanup_repo(snake_case   :Union[str, Any]	):
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)

 return _cleanup_repo






@pytest.fixture
def 							A		(       snake_case   :List[str]	)   ->			Any:
 @contextmanager
 def _temporary_repo(snake_case   :Tuple	):
  try:
   yield repo_id
  finally:
   cleanup_repo(snake_case	)

 return _temporary_repo






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :Dict       ,      snake_case   :Dict	)   ->			List[str]:
 __UpperCamelCase       						= f'repo_txt_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data/text_data.txt'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :Optional[int]       ,      snake_case   :str       ,      snake_case   :Tuple	)   ->			Any:
 return hf_private_dataset_repo_txt_data_






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :List[Any]       ,      snake_case   :str	)   ->			Optional[int]:
 __UpperCamelCase       						= f'repo_zipped_txt_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data.zip'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :int       ,      snake_case   :Optional[int]       ,      snake_case   :Optional[int]	)   ->			str:
 return hf_private_dataset_repo_zipped_txt_data_






@pytest.fixture(scope='session'	)
def 							A		(       snake_case   :HfApi       ,      snake_case   :List[str]       ,      snake_case   :Any	)   ->			List[Any]:
 __UpperCamelCase       						= f'repo_zipped_img_data-{int(time.time() * 10e3	)}'
 __UpperCamelCase       						= f'{CI_HUB_USER}/{repo_name}'
 hf_api.create_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'       ,      private=snake_case	)
 hf_api.upload_file(
     token=snake_case       ,      path_or_fileobj=str(snake_case	)       ,      path_in_repo='data.zip'       ,      repo_id=snake_case       ,      repo_type='dataset'       ,      )
 yield repo_id
 try:
  hf_api.delete_repo(snake_case       ,      token=snake_case       ,      repo_type='dataset'	)
 except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
  pass






@pytest.fixture()
def 							A		(       snake_case   :List[str]       ,      snake_case   :Optional[Any]       ,      snake_case   :Optional[int]	)   ->			Optional[int]:
 return hf_private_dataset_repo_zipped_img_data_



