



import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 	lowerCamelCase__	(       __snake_case							,  unittest.TestCase):



 """simple docstring"""

 a__					: Optional[int] =  MgpstrTokenizer
 a__					: Union[str, Any] =  False
 a__					: Optional[int] =  {}
 a__					: Optional[int] =  False

 def     snake_case_      (    self					:					Optional[Any]      )   ->	Tuple:
    super().setUp()

    # fmt: off
    _A      =       ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
    # fmt: on
    _A      =       dict(zip(__lowerCAmelCase			,						range(len(__lowerCAmelCase      )      )      )      )

    _A      =       os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['''vocab_file''']      )
    with open(self.vocab_file			,						'''w'''			,						encoding='''utf-8'''      ) as fp:
       fp.write(json.dumps(__lowerCAmelCase      ) + '''\n'''      )

 def     snake_case_      (    self					:					str			,						**__lowerCAmelCase					:					List[Any]      )   ->	Any:
    return MgpstrTokenizer.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					str      )   ->	Tuple:
    _A      =       '''tester'''
    _A      =       '''tester'''
    return input_text, output_text

 @unittest.skip('''MGP-STR always lower cases letters.'''      )
 def     snake_case_      (    self					:					Tuple      )   ->	Union[str, Any]:
    pass

 def     snake_case_      (    self					:					str      )   ->	Union[str, Any]:
    _A      =       self.get_tokenizers(do_lower_case=__lowerCAmelCase      )
    for tokenizer in tokenizers:
       with self.subTest(f'''{tokenizer.__class__.__name__}'''      ):
          _A      =       '''[SPECIAL_TOKEN]'''

          tokenizer.add_special_tokens({'''cls_token''': special_token}      )
          _A      =       tokenizer.encode([special_token]			,						add_special_tokens=__lowerCAmelCase      )
          self.assertEqual(len(__lowerCAmelCase      )			,						1      )

          _A      =       tokenizer.decode(__lowerCAmelCase			,						skip_special_tokens=__lowerCAmelCase      )
          self.assertTrue(special_token not in decoded      )

 def     snake_case_      (    self					:					List[Any]      )   ->	List[str]:
    _A      =       self.get_tokenizers()
    for tokenizer in tokenizers:
       with self.subTest(f'''{tokenizer.__class__.__name__}'''      ):
          _A      =       self.get_input_output_texts(__lowerCAmelCase      )

          _A      =       tokenizer.tokenize(__lowerCAmelCase      )
          _A      =       tokenizer.convert_tokens_to_ids(__lowerCAmelCase      )
          _A      =       tokenizer.encode(__lowerCAmelCase			,						add_special_tokens=__lowerCAmelCase      )
          self.assertListEqual(__lowerCAmelCase			,						__lowerCAmelCase      )

          _A      =       tokenizer.convert_ids_to_tokens(__lowerCAmelCase      )
          self.assertNotEqual(len(__lowerCAmelCase      )			,						0      )
          _A      =       tokenizer.decode(__lowerCAmelCase      )
          self.assertIsInstance(__lowerCAmelCase			,						__lowerCAmelCase      )

          self.assertEqual(text_a.replace(''' '''			,						''''''      )			,						__lowerCAmelCase      )

 @unittest.skip('''MGP-STR tokenizer only handles one sequence.'''      )
 def     snake_case_      (    self					:					str      )   ->	int:
    pass

 @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''      )
 def     snake_case_      (    self					:					Optional[Any]      )   ->	Optional[int]:
    pass





'''simple docstring'''




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


lowercase     :							List[str]       						=     logging.get_logger(__name__)

lowercase     :							Optional[int]       						=     {
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''switch_transformers'''
     __magic_name__          =						['''past_key_values''']
     __magic_name__          =						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





     def __init__(				self		,     SCREAMING_SNAKE_CASE=32128		,     SCREAMING_SNAKE_CASE=768		,     SCREAMING_SNAKE_CASE=64		,     SCREAMING_SNAKE_CASE=2048		,     SCREAMING_SNAKE_CASE=64		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=0.01		,     SCREAMING_SNAKE_CASE="float32"		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=128		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=1e-6		,     SCREAMING_SNAKE_CASE=0.001		,     SCREAMING_SNAKE_CASE=0.001		,     SCREAMING_SNAKE_CASE=1.0		,     SCREAMING_SNAKE_CASE="relu"		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=0		,     SCREAMING_SNAKE_CASE=1		,     **SCREAMING_SNAKE_CASE		,     )					->							List[Any]:






         """simple docstring"""






         A			:  Union[str, Any]   =		vocab_size
         A			:  Any   =		d_model
         A			:  Dict   =		d_kv
         A			:  Optional[Any]   =		d_ff

         A			:  List[Any]   =		num_sparse_encoder_layers

         A			:  List[Any]   =		num_layers
         A			:  Optional[int]   =		(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         A			:  List[str]   =		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:
             A			:  int   =		self.num_layers // self.num_sparse_encoder_layers
         else:
             A			:  int   =		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:
             A			:  str   =		self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
             A			:  Dict   =		self.num_decoder_layers  # HACK: this will create 0 sparse layers

         A			:  List[Any]   =		num_heads
         A			:  str   =		num_experts
         A			:  Dict   =		expert_capacity
         A			:  Optional[int]   =		router_bias
         A			:  Any   =		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}'      )
         A			:  Dict   =		router_dtype

         A			:  int   =		router_ignore_padding_tokens
         A			:  List[Any]   =		relative_attention_num_buckets
         A			:  Dict   =		relative_attention_max_distance

         A			:  Optional[int]   =		dropout_rate
         A			:  Dict   =		layer_norm_epsilon
         A			:  Optional[int]   =		initializer_factor
         A			:  Union[str, Any]   =		feed_forward_proj
         A			:  Any   =		use_cache
         A			:  Tuple   =		add_router_probs

         A			:  List[str]   =		router_z_loss_coef
         A			:  str   =		router_aux_loss_coef

         A			:  Union[str, Any]   =		self.feed_forward_proj.split('''-'''      )
         A			:  Optional[Any]   =		act_info[-1]
         A			:  Union[str, Any]   =		act_info[0] == '''gated'''

         if len(SCREAMING_SNAKE_CASE      ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE      ) > 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":
             A			:  str   =		'''gelu_new'''

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




