
from typing import Mapping

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


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						str         =							"mt5"
							lowerCAmelCase__		:						Optional[int]         =							["past_key_values"]





							def __init__(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       List[str]=25_01_12					,			_UpperCAmelCase  :       Optional[int]=5_12					,			_UpperCAmelCase  :       Any=64					,			_UpperCAmelCase  :       str=10_24					,			_UpperCAmelCase  :       int=8					,			_UpperCAmelCase  :       Optional[Any]=None					,			_UpperCAmelCase  :       Optional[Any]=6					,			_UpperCAmelCase  :       List[Any]=32					,			_UpperCAmelCase  :       int=1_28					,			_UpperCAmelCase  :       List[str]=0.1					,			_UpperCAmelCase  :       Any=1e-6					,			_UpperCAmelCase  :       int=1.0					,			_UpperCAmelCase  :       Dict="gated-gelu"					,			_UpperCAmelCase  :       Any=True					,			_UpperCAmelCase  :       str=True					,			_UpperCAmelCase  :       Any="T5Tokenizer"					,			_UpperCAmelCase  :       Optional[int]=False					,			_UpperCAmelCase  :       Dict=0					,			_UpperCAmelCase  :       Any=1					,			_UpperCAmelCase  :       Optional[int]=0					,			**_UpperCAmelCase  :       Union[str, Any]					,			)						->		List[Any]:



											"""simple docstring"""


											super().__init__(
											    is_encoder_decoder=_UpperCAmelCase					,			tokenizer_class=_UpperCAmelCase					,			tie_word_embeddings=_UpperCAmelCase					,			pad_token_id=_UpperCAmelCase					,			eos_token_id=_UpperCAmelCase					,			decoder_start_token_id=_UpperCAmelCase					,			**_UpperCAmelCase					,			)
											__lowercase								= vocab_size
											__lowercase								= d_model
											__lowercase								= d_kv
											__lowercase								= d_ff
											__lowercase								= num_layers
											__lowercase								= (
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											__lowercase								= num_heads
											__lowercase								= relative_attention_num_buckets
											__lowercase								= relative_attention_max_distance
											__lowercase								= dropout_rate
											__lowercase								= layer_norm_epsilon
											__lowercase								= initializer_factor
											__lowercase								= feed_forward_proj
											__lowercase								= use_cache

											__lowercase								= self.feed_forward_proj.split('-'    )
											__lowercase								= act_info[-1]
											__lowercase								= 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":
															__lowercase								= 'gelu_new'





							@property
							def       a__			(			self  :       Dict    )						->		Any:



											"""simple docstring"""


											return self.d_model





							@property
							def       a__			(			self  :       int    )						->		Union[str, Any]:



											"""simple docstring"""


											return self.num_heads





							@property
							def       a__			(			self  :       Any    )						->		List[Any]:



											"""simple docstring"""


											return self.num_layers






class      A__       (						lowerCAmelCase__    ):





							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
							def       a__			(			self  :       int    )						->		Mapping[str, Mapping[int, str]]:



											"""simple docstring"""


											__lowercase								= {
											    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
											    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
											}
											if self.use_past:
															__lowercase								= 'past_encoder_sequence + sequence'
															__lowercase								= {0: 'batch'}
															__lowercase								= {0: 'batch', 1: 'past_decoder_sequence + sequence'}
											else:
															__lowercase								= {0: 'batch', 1: 'decoder_sequence'}
															__lowercase								= {0: 'batch', 1: 'decoder_sequence'}

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

											return common_inputs





							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
							def       a__			(			self  :       List[str]    )						->		int:



											"""simple docstring"""


											return 13





							@property
							def       a__			(			self  :       List[str]    )						->		float:



											"""simple docstring"""


											return 5e-4







import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types
class      A__       (						unittest.TestCase   ,						lowerCAmelCase__    ):





							def       a__			(			self  :       Any    )						->		Dict:



											"""simple docstring"""


											__lowercase								= load_tool('translation'    )
											self.tool.setup()
											__lowercase								= load_tool('translation'					,			remote=_UpperCAmelCase    )





							def       a__			(			self  :       int    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= self.tool('Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Tuple    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= self.remote_tool('Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Union[str, Any]    )						->		Tuple:



											"""simple docstring"""


											__lowercase								= self.tool(text='Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Tuple    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= self.remote_tool(text='Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       List[Any]    )						->		int:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']
											__lowercase								= self.tool(*_UpperCAmelCase    )

											# There is a single output
											if len(self.tool.outputs    ) == 1:
															__lowercase								= [outputs]

											self.assertListEqual(output_types(_UpperCAmelCase    )					,			self.tool.outputs    )





							def       a__			(			self  :       List[str]    )						->		Any:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']
											__lowercase								= self.tool(*_UpperCAmelCase    )

											if not isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= [outputs]

											self.assertEqual(len(_UpperCAmelCase    )					,			len(self.tool.outputs    )    )

											for output, output_type in zip(_UpperCAmelCase					,			self.tool.outputs    ):
															__lowercase								= AGENT_TYPE_MAPPING[output_type]
															self.assertTrue(isinstance(_UpperCAmelCase					,			_UpperCAmelCase    )    )





							def       a__			(			self  :       Tuple    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']

											__lowercase								= []

											for _input, input_type in zip(_UpperCAmelCase					,			self.tool.inputs    ):
															if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
																			_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input    ) for _input_type in input_type]    )
															else:
																			_inputs.append(AGENT_TYPE_MAPPING[input_type](_input    )    )

        # Should not raise an error
											__lowercase								= self.tool(*_UpperCAmelCase    )

											if not isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= [outputs]

											self.assertEqual(len(_UpperCAmelCase    )					,			len(self.tool.outputs    )    )





