
'''simple docstring'''





from typing import Mapping

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


_A						      =				logging.get_logger(__name__)

_A						      =				{
    '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	(						UpperCamelCase_						):
     UpperCAmelCase__       :							List[Any]     =	"umt5"
     UpperCAmelCase__       :							Union[str, Any]     =	["past_key_values"]





     def __init__(self   :					Optional[Any]       ,					_A   :					Dict=2_5_0_1_1_2       ,					_A   :					List[str]=5_1_2       ,					_A   :					Tuple=6_4       ,					_A   :					Any=1_0_2_4       ,					_A   :					Optional[Any]=8       ,					_A   :					Dict=None       ,					_A   :					List[Any]=6       ,					_A   :					Dict=3_2       ,					_A   :					Union[str, Any]=1_2_8       ,					_A   :					Tuple=0.1       ,					_A   :					Optional[int]=1E-6       ,					_A   :					List[str]=1.0       ,					_A   :					Dict="gated-gelu"       ,					_A   :					Dict=True       ,					_A   :					Tuple=True       ,					_A   :					Any="T5Tokenizer"       ,					_A   :					List[Any]=True       ,					_A   :					Union[str, Any]=0       ,					_A   :					str=1       ,					_A   :					List[Any]=0       ,					**_A   :					List[str]       ,					)		->		str:
         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__       ,					)
         snake_case				  =      vocab_size
         snake_case				  =      d_model
         snake_case				  =      d_kv
         snake_case				  =      d_ff
         snake_case				  =      num_layers
         snake_case				  =      (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         snake_case				  =      num_heads
         snake_case				  =      relative_attention_num_buckets
         snake_case				  =      relative_attention_max_distance
         snake_case				  =      dropout_rate
         snake_case				  =      layer_norm_epsilon
         snake_case				  =      initializer_factor
         snake_case				  =      feed_forward_proj
         snake_case				  =      use_cache

         snake_case				  =      self.feed_forward_proj.split("-"     )
         snake_case				  =      act_info[-1]
         snake_case				  =      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\'"     )

         if feed_forward_proj == "gated-gelu":
             snake_case				  =      '''gelu_new'''





     @property
     def 	UpperCAmelCase(self   :					Dict     )		->		int:
         return self.d_model





     @property
     def 	UpperCAmelCase(self   :					str     )		->		Tuple:
         return self.num_heads





     @property
     def 	UpperCAmelCase(self   :					Optional[int]     )		->		str:
         return self.num_layers







class   lowerCamelCase	(						UpperCamelCase_						):





     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def 	UpperCAmelCase(self   :					str     )		->		Union[str, Any]:
         snake_case				  =      {
             '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
             '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
         }
         if self.use_past:
             snake_case				  =      '''past_encoder_sequence + sequence'''
             snake_case				  =      {0: '''batch'''}
             snake_case				  =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
             snake_case				  =      {0: '''batch''', 1: '''decoder_sequence'''}
             snake_case				  =      {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 	UpperCAmelCase(self   :					Dict     )		->		Any:
         return 1_3





     @property
     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Optional[int]:
         return 5E-4



import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment



def 		lowercase_    (     A__				,	A__=()				,	A__=None				,	A__="no"				,	A__="29500"	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      False
    snake_case				  =      False
    if any(key.startswith("KAGGLE"	) for key in os.environ.keys()	):
        snake_case				  =      True
    elif "IPython" in sys.modules:
        snake_case				  =      "google.colab" in str(sys.modules["IPython"].get_ipython()	)

    try:
        snake_case				  =      PrecisionType(mixed_precision.lower()	)
    except ValueError:
        raise ValueError(
            F'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'	)

    if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"				,	A__	) is not None):
        # TPU launch
        import torch_xla.distributed.xla_multiprocessing as xmp

        if len(AcceleratorState._shared_state	) > 0:
            raise ValueError(
                "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
                "your training function. Restart your notebook and make sure no cells initializes an "
                "`Accelerator`."	)
        if num_processes is None:
            snake_case				  =      8

        snake_case				  =      PrepareForLaunch(A__				,	distributed_type="TPU"	)
        print(F'Launching a training on {num_processes} TPU cores.'	)
        xmp.spawn(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)
    elif in_colab:
        # No need for a distributed launch otherwise as it's either CPU or one GPU.
        if torch.cuda.is_available():
            print("Launching training on one GPU."	)
        else:
            print("Launching training on one CPU."	)
        function(*A__	)
    else:
        if num_processes is None:
            raise ValueError(
                "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."	)

        if num_processes > 1:
            # Multi-GPU launch
            from torch.multiprocessing import start_processes
            from torch.multiprocessing.spawn import ProcessRaisedException

            if len(AcceleratorState._shared_state	) > 0:
                raise ValueError(
                    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
                    "inside your training function. Restart your notebook and make sure no cells initializes an "
                    "`Accelerator`."	)

            if torch.cuda.is_initialized():
                raise ValueError(
                    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
                    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
                    "function."	)

            # torch.distributed will expect a few environment variable to be here. We set the ones common to each
            # process here (the other ones will be set be the launcher).
            with patch_environment(
                world_size=A__				,	master_addr="127.0.01"				,	master_port=A__				,	mixed_precision=A__	):
                snake_case				  =      PrepareForLaunch(A__				,	distributed_type="MULTI_GPU"	)
                print(F'Launching training on {num_processes} GPUs.'	)
                try:
                    start_processes(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)
                except ProcessRaisedException as e:
                    if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                        raise RuntimeError(
                            "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
                            "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
                            "Please review your imports and test them when running the `notebook_launcher()` to identify "
                            "which one is problematic."	) from e

        else:
            # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
            if is_mps_available():
                snake_case				  =      "1"
                print("Launching training on MPS."	)
            elif torch.cuda.is_available():
                print("Launching training on one GPU."	)
            else:
                print("Launching training on CPU."	)
            function(*A__	)





def 		lowercase_    (     A__				,	A__=()				,	A__=2	)							->							Dict:
    """simple docstring"""

    from torch.multiprocessing import start_processes

    with tempfile.NamedTemporaryFile() as tmp_file:
        # torch.distributed will expect a few environment variable to be here. We set the ones common to each
        # process here (the other ones will be set be the launcher).
        with patch_environment(
            world_size=A__				,	master_addr="127.0.01"				,	master_port="29500"				,	accelerate_mixed_precision="no"				,	accelerate_debug_rdv_file=tmp_file.name				,	accelerate_use_cpu="yes"				,	):
            snake_case				  =      PrepareForLaunch(A__				,	debug=A__	)
            start_processes(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)

