"""simple docstring"""


import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin




class 					UpperCamelCase   (							lowerCAmelCase__       ,						unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_						      = SpeechaTextaTokenizer
       SCREAMING_SNAKE_CASE_						      = False







       def 						a_		(					self) -> List[str]:
            super().setUp()

            snake_case_   			=   '<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' ')
            snake_case_   			=   ['he re</w> 123', 'here a 1456']
            snake_case_   			=   dict(zip(lowercase__,				range(len(lowercase__))))

            snake_case_   			=   {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

            snake_case_   			=   tempfile.mkdtemp()
            snake_case_   			=   os.path.join(self.tmpdirname,				VOCAB_FILES_NAMES['vocab_file'])
            snake_case_   			=   os.path.join(self.tmpdirname,				VOCAB_FILES_NAMES['merges_file'])

            with open(self.vocab_file,				'w',				encoding='utf-8') as fp:
                 fp.write(json.dumps(lowercase__) + '\n')

            with open(self.merges_file,				'w') as fp:
                 fp.write('\n'.join(lowercase__))







       def 						a_		(					self) -> List[str]:
            snake_case_   			=   list(self.get_tokenizer().get_vocab().keys())

            self.assertEqual(vocab_keys[0],				'<s>')
            self.assertEqual(vocab_keys[1],				'<pad>')
            self.assertEqual(vocab_keys[-1],				'vocab')
            self.assertEqual(len(lowercase__),				14)







       def 						a_		(					self) -> Any:
            self.assertEqual(self.get_tokenizer().vocab_size,				14)







       def 						a_		(					self) -> Optional[int]:
            snake_case_   			=   SpeechaTextaTokenizer.from_pretrained(self.tmpdirname)

            # make sure @@ is correctly concatenated
            snake_case_   			=   [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
            snake_case_   			=   tokenizer.decode(lowercase__)

            self.assertTrue(output_string == 'herecouple words ofthe')







       def 						a_		(					self) -> Dict:
            snake_case_   			=   SpeechaTextaTokenizer.from_pretrained(self.tmpdirname)

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 tokenizer.save_pretrained(lowercase__)
                 os.remove(os.path.join(lowercase__,				'merges.txt'))

                 # load tokenizer without merges file should not throw an error
                 snake_case_   			=   SpeechaTextaTokenizer.from_pretrained(lowercase__)

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 # save tokenizer and load again
                 tokenizer.save_pretrained(lowercase__)
                 snake_case_   			=   SpeechaTextaTokenizer.from_pretrained(lowercase__)

            self.assertIsNotNone(lowercase__)







       def 						a_		(					self) -> Dict:







            if not self.test_slow_tokenizer:
                 return

            snake_case_   			=   inspect.signature(self.tokenizer_class.__init__)
            snake_case_   			=   self.get_tokenizer()

            for parameter_name, parameter in signature.parameters.items():
                 if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                      self.assertIn(lowercase__,				tokenizer.init_kwargs)



"""simple docstring"""




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


snake_case					  =						logging.get_logger(__name__)

snake_case					  =						{
    # pcqm4mv1 now deprecated
    'graphormer-base': 'https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}




class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				UpperCAmelCase_							: Dict		    =							"graphormer"
				UpperCAmelCase_							: Optional[int]		    =							["past_key_values"]


				def __init__(							self						,      lowercase__ = 1						,      lowercase__ = 512 * 9						,      lowercase__ = 512 * 3						,      lowercase__ = 512						,      lowercase__ = 512						,      lowercase__ = 512						,      lowercase__ = 128						,      lowercase__ = 5						,      lowercase__ = 1024						,      lowercase__ = "multi_hop"						,      lowercase__ = 512						,      lowercase__ = False						,      lowercase__ = 12						,      lowercase__ = 768						,      lowercase__ = 768						,      lowercase__ = 32						,      lowercase__ = 0.1						,      lowercase__ = 0.1						,      lowercase__ = 0.0						,      lowercase__ = False						,      lowercase__ = False						,      lowercase__ = False						,      lowercase__ = "gelu"						,      lowercase__ = None						,      lowercase__ = False						,      lowercase__ = 0						,      lowercase__ = False						,      lowercase__ = 0.0						,      lowercase__ = 8						,      lowercase__ = None						,      lowercase__ = None						,      lowercase__ = True						,      lowercase__ = True						,      lowercase__=0						,      lowercase__=1						,      lowercase__=2						,      **lowercase__						,      )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      num_classes
									SCREAMING_SNAKE_CASE           =      num_atoms
									SCREAMING_SNAKE_CASE           =      num_in_degree
									SCREAMING_SNAKE_CASE           =      num_out_degree
									SCREAMING_SNAKE_CASE           =      num_edges
									SCREAMING_SNAKE_CASE           =      num_spatial
									SCREAMING_SNAKE_CASE           =      num_edge_dis
									SCREAMING_SNAKE_CASE           =      edge_type
									SCREAMING_SNAKE_CASE           =      multi_hop_max_dist
									SCREAMING_SNAKE_CASE           =      spatial_pos_max
									SCREAMING_SNAKE_CASE           =      max_nodes
									SCREAMING_SNAKE_CASE           =      num_hidden_layers
									SCREAMING_SNAKE_CASE           =      embedding_dim
									SCREAMING_SNAKE_CASE           =      embedding_dim
									SCREAMING_SNAKE_CASE           =      ffn_embedding_dim
									SCREAMING_SNAKE_CASE           =      num_attention_heads
									SCREAMING_SNAKE_CASE           =      dropout
									SCREAMING_SNAKE_CASE           =      attention_dropout
									SCREAMING_SNAKE_CASE           =      layerdrop
									SCREAMING_SNAKE_CASE           =      encoder_normalize_before
									SCREAMING_SNAKE_CASE           =      pre_layernorm
									SCREAMING_SNAKE_CASE           =      apply_graphormer_init
									SCREAMING_SNAKE_CASE           =      activation_fn
									SCREAMING_SNAKE_CASE           =      embed_scale
									SCREAMING_SNAKE_CASE           =      freeze_embeddings
									SCREAMING_SNAKE_CASE           =      num_trans_layers_to_freeze
									SCREAMING_SNAKE_CASE           =      share_input_output_embed
									SCREAMING_SNAKE_CASE           =      traceable
									SCREAMING_SNAKE_CASE           =      q_noise
									SCREAMING_SNAKE_CASE           =      qn_block_size

									# These parameters are here for future extensions
									# atm, the model only supports self attention
									SCREAMING_SNAKE_CASE           =      kdim
									SCREAMING_SNAKE_CASE           =      vdim
									SCREAMING_SNAKE_CASE           =      self_attention
									SCREAMING_SNAKE_CASE           =      bias

									super().__init__(
									    pad_token_id=lowercase__						,      bos_token_id=lowercase__						,      eos_token_id=lowercase__						,      **lowercase__						,      )
