




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase__ :   Dict						=							logging.get_logger(__name__)

UpperCamelCase__ :   Any						=							{
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       str						=     "umt5"
				__a     :       str						=     ["past_key_values"]






				def __init__(			self      ,snake_case__=250112      ,snake_case__=512      ,snake_case__=64      ,snake_case__=1024      ,snake_case__=8      ,snake_case__=None      ,snake_case__=6      ,snake_case__=32      ,snake_case__=128      ,snake_case__=0.1      ,snake_case__=1E-6      ,snake_case__=1.0      ,snake_case__="gated-gelu"      ,snake_case__=True      ,snake_case__=True      ,snake_case__="T5Tokenizer"      ,snake_case__=True      ,snake_case__=0      ,snake_case__=1      ,snake_case__=0      ,**snake_case__      ,):
							super().__init__(
							    is_encoder_decoder=snake_case__      ,tokenizer_class=snake_case__      ,tie_word_embeddings=snake_case__      ,pad_token_id=snake_case__      ,eos_token_id=snake_case__      ,decoder_start_token_id=snake_case__      ,**snake_case__      ,)
							SCREAMING_SNAKE_CASE_   : Tuple					  =      vocab_size
							SCREAMING_SNAKE_CASE_   : List[str]					  =      d_model
							SCREAMING_SNAKE_CASE_   : Any					  =      d_kv
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      d_ff
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      num_layers
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      (
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							SCREAMING_SNAKE_CASE_   : str					  =      num_heads
							SCREAMING_SNAKE_CASE_   : Dict					  =      relative_attention_num_buckets
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      relative_attention_max_distance
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      dropout_rate
							SCREAMING_SNAKE_CASE_   : List[str]					  =      layer_norm_epsilon
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      initializer_factor
							SCREAMING_SNAKE_CASE_   : Tuple					  =      feed_forward_proj
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      use_cache

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

							if len(snake_case__ ) > 1 and act_info[0] != "gated" or len(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\'' )

							if feed_forward_proj == "gated-gelu":
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      'gelu_new'






				@property
				def 		snake_case	(			self ):
							return self.d_model






				@property
				def 		snake_case	(			self ):
							return self.num_heads






				@property
				def 		snake_case	(			self ):
							return self.num_layers







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      {
							    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
							    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
							}
							if self.use_past:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      'past_encoder_sequence + sequence'
										SCREAMING_SNAKE_CASE_   : str					  =      {0: 'batch'}
										SCREAMING_SNAKE_CASE_   : Any					  =      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {0: 'batch', 1: 'decoder_sequence'}
										SCREAMING_SNAKE_CASE_   : Any					  =      {0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
										self.fill_with_past_key_values_(snake_case__      ,direction='inputs' )

							return common_inputs






				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
				def 		snake_case	(			self ):
							return 13






				@property
				def 		snake_case	(			self ):
							return 5E-4









import os
import sys
import unittest


UpperCamelCase__ :   Optional[Any]						=							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCamelCase__ :   Tuple						=							os.path.join(git_repo_path, '''src''', '''transformers''')

UpperCamelCase__ :   List[Any]						=							'''
{0} = None
'''

UpperCamelCase__ :   str						=							'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


UpperCamelCase__ :   List[Any]						=							'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''







class 				lowerCAmelCase_       (					unittest.TestCase							):






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")' )
							self.assertIsNone(snake_case__ )

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      find_backend('    if not is_tokenizers_available():' )
							self.assertEqual(snake_case__      ,'tokenizers' )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      find_backend('    if not is_tensorflow_text_available():' )
							self.assertEqual(snake_case__      ,'tensorflow_text' )

							SCREAMING_SNAKE_CASE_   : int					  =      find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tokenizers' )

							SCREAMING_SNAKE_CASE_   : str					  =      find_backend(
							    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tensorflow_text' )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      find_backend(
							    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tokenizers_and_vision' )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn('torch'      ,snake_case__ )
							self.assertIn('tensorflow_text'      ,snake_case__ )
							self.assertIn('sentencepiece_and_tokenizers'      ,snake_case__ )

							# Likewise, we can't assert on the exact content of a key
							self.assertIn('BertModel'      ,objects['torch'] )
							self.assertIn('TFBertModel'      ,objects['tf'] )
							self.assertIn('FlaxBertModel'      ,objects['flax'] )
							self.assertIn('BertModel'      ,objects['torch'] )
							self.assertIn('TFBertTokenizer'      ,objects['tensorflow_text'] )
							self.assertIn('convert_slow_tokenizer'      ,objects['sentencepiece_and_tokenizers'] )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      create_dummy_object('CONSTANT'      ,'\'torch\'' )
							self.assertEqual(snake_case__      ,'\nCONSTANT = None\n' )

							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_object('function'      ,'\'torch\'' )
							self.assertEqual(
							    snake_case__      ,'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n' )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_object('FakeClass'      ,'\'torch\'' )
							self.assertEqual(snake_case__      ,snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
							self.assertEqual(dummy_files['torch']      ,snake_case__ )



