


from typing import Mapping

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


UpperCamelCase						:				List[Any]             =      logging.get_logger(__name__)

UpperCamelCase						:				Dict             =      {
    "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__						(   _A			):
						lowerCAmelCase       =     """umt5"""
						lowerCAmelCase       =     ["""past_key_values"""]







						def __init__(							self   :    int			,			_lowercase   :    Any=250_112			,			_lowercase   :    List[str]=512			,			_lowercase   :    Any=64			,			_lowercase   :    Any=1_024			,			_lowercase   :    Tuple=8			,			_lowercase   :    Dict=None			,			_lowercase   :    List[Any]=6			,			_lowercase   :    Any=32			,			_lowercase   :    List[Any]=128			,			_lowercase   :    Any=0.1			,			_lowercase   :    List[str]=1e-6			,			_lowercase   :    Dict=1.0			,			_lowercase   :    Union[str, Any]="gated-gelu"			,			_lowercase   :    List[Any]=True			,			_lowercase   :    Tuple=True			,			_lowercase   :    Optional[Any]="T5Tokenizer"			,			_lowercase   :    Union[str, Any]=True			,			_lowercase   :    Union[str, Any]=0			,			_lowercase   :    Union[str, Any]=1			,			_lowercase   :    str=0			,			**_lowercase   :    List[str]			,			):
								super().__init__(
								    is_encoder_decoder=_lowercase			,			tokenizer_class=_lowercase			,			tie_word_embeddings=_lowercase			,			pad_token_id=_lowercase			,			eos_token_id=_lowercase			,			decoder_start_token_id=_lowercase			,			**_lowercase			,			)
								A					       = vocab_size
								A					       = d_model
								A					       = d_kv
								A					       = d_ff
								A					       = num_layers
								A					       = (
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								A					       = num_heads
								A					       = relative_attention_num_buckets
								A					       = relative_attention_max_distance
								A					       = dropout_rate
								A					       = layer_norm_epsilon
								A					       = initializer_factor
								A					       = feed_forward_proj
								A					       = use_cache

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

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







						@property
						def __a						(							self   :    Optional[Any]		):
								return self.d_model







						@property
						def __a						(							self   :    Union[str, Any]		):
								return self.num_heads







						@property
						def __a						(							self   :    Optional[Any]		):
								return self.num_layers



class 			lowerCamelCase__						(   _A			):







						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def __a						(							self   :    Any		):
								A					       = {
								    "input_ids": {0: "batch", 1: "encoder_sequence"},
								    "attention_mask": {0: "batch", 1: "encoder_sequence"},
								}
								if self.use_past:
										A					       = "past_encoder_sequence + sequence"
										A					       = {0: "batch"}
										A					       = {0: "batch", 1: "past_decoder_sequence + sequence"}
								else:
										A					       = {0: "batch", 1: "decoder_sequence"}
										A					       = {0: "batch", 1: "decoder_sequence"}

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

								return common_inputs







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







						@property
						def __a						(							self   :    Optional[Any]		):
								return 5e-4









"""simple docstring"""


from unittest.mock import Mock, patch

from file_transfer.send_file import send_file







@patch('socket.socket'	)
@patch('builtins.open'	)
def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Dict:







		"""simple docstring"""





		A					       = Mock()
		A					       = conn, Mock()
		A					       = iter([1, None]	)
		A					       = lambda UpperCamelCase__	: next(UpperCamelCase__	)

		# ===== invoke =====
		send_file(filename='mytext.txt' ,			testing=UpperCamelCase__	)

		# ===== ensurance =====
		sock.assert_called_once()
		sock.return_value.bind.assert_called_once()
		sock.return_value.listen.assert_called_once()
		sock.return_value.accept.assert_called_once()
		conn.recv.assert_called_once()

		file.return_value.__enter__.assert_called_once()
		file.return_value.__enter__.return_value.read.assert_called()

		conn.send.assert_called_once()
		conn.close.assert_called_once()
		sock.return_value.shutdown.assert_called_once()
		sock.return_value.close.assert_called_once()





