from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


snake_case__				= logging.get_logger(__name__)

snake_case__				= {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  'marian'
     A_										=  ['past_key_values']
     A_										=  {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



     def __init__(					self				,				A_=5_81_01				,				A_=None				,				A_=10_24				,				A_=12				,				A_=40_96				,				A_=16				,				A_=12				,				A_=40_96				,				A_=16				,				A_=0.0				,				A_=0.0				,				A_=True				,				A_=True				,				A_="gelu"				,				A_=10_24				,				A_=0.1				,				A_=0.0				,				A_=0.0				,				A_=0.02				,				A_=5_81_00				,				A_=False				,				A_=5_81_00				,				A_=0				,				A_=0				,				A_=True				,				**A_				,				) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				vocab_size
           _lowerCamelCase			    =				decoder_vocab_size or vocab_size
           _lowerCamelCase			    =				max_position_embeddings
           _lowerCamelCase			    =				d_model
           _lowerCamelCase			    =				encoder_ffn_dim
           _lowerCamelCase			    =				encoder_layers
           _lowerCamelCase			    =				encoder_attention_heads
           _lowerCamelCase			    =				decoder_ffn_dim
           _lowerCamelCase			    =				decoder_layers
           _lowerCamelCase			    =				decoder_attention_heads
           _lowerCamelCase			    =				dropout
           _lowerCamelCase			    =				attention_dropout
           _lowerCamelCase			    =				activation_dropout
           _lowerCamelCase			    =				activation_function
           _lowerCamelCase			    =				init_std
           _lowerCamelCase			    =				encoder_layerdrop
           _lowerCamelCase			    =				decoder_layerdrop
           _lowerCamelCase			    =				use_cache
           _lowerCamelCase			    =				encoder_layers
           _lowerCamelCase			    =				scale_embedding  # scale factor will be sqrt(d_model) if True
           _lowerCamelCase			    =				share_encoder_decoder_embeddings
           super().__init__(
               pad_token_id=A_				,				eos_token_id=A_				,				is_encoder_decoder=A_				,				decoder_start_token_id=A_				,				forced_eos_token_id=A_				,				**A_				,				)





class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
     def 		UpperCamelCase_						(					self						) ->				Mapping[str, Mapping[int, str]]:






           """simple docstring"""
           if self.task in ["default", "seq2seq-lm"]:
                 _lowerCamelCase			    =				OrderedDict(
                     [
                         ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                         ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ]						)

                 if self.use_past:
                       _lowerCamelCase			    =				{0: '''batch'''}
                       _lowerCamelCase			    =				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
                 else:
                       _lowerCamelCase			    =				{0: '''batch''', 1: '''decoder_sequence'''}
                       _lowerCamelCase			    =				{0: '''batch''', 1: '''decoder_sequence'''}

                 if self.use_past:
                       self.fill_with_past_key_values_(A_				,				direction='''inputs'''						)
           elif self.task == "causal-lm":
                 # TODO: figure this case out.
                 _lowerCamelCase			    =				OrderedDict(
                     [
                         ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                         ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ]						)
                 if self.use_past:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				self.num_layers
                       for i in range(A_						):
                             _lowerCamelCase			    =				{0: '''batch''', 2: '''past_sequence + sequence'''}
                             _lowerCamelCase			    =				{0: '''batch''', 2: '''past_sequence + sequence'''}
           else:
                 _lowerCamelCase			    =				OrderedDict(
                     [
                         ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                         ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                         ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                         ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                     ]						)

           return common_inputs



     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def 		UpperCamelCase_						(					self						) ->				Mapping[str, Mapping[int, str]]:






           """simple docstring"""
           if self.task in ["default", "seq2seq-lm"]:
                 _lowerCamelCase			    =				super().outputs
           else:
                 _lowerCamelCase			    =				super(A_				,				self						).outputs
                 if self.use_past:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				self.num_layers
                       for i in range(A_						):
                             _lowerCamelCase			    =				{0: '''batch''', 2: '''past_sequence + sequence'''}
                             _lowerCamelCase			    =				{0: '''batch''', 2: '''past_sequence + sequence'''}
           return common_outputs



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = -1				,				A_ = -1				,				A_ = False				,				A_ = None				,				) ->				Mapping[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self._generate_dummy_inputs_for_encoder_and_decoder(
               A_				,				A_				,				A_				,				A_				,				A_						)

           # Generate decoder inputs
           _lowerCamelCase			    =				seq_length if not self.use_past else 1
           _lowerCamelCase			    =				self._generate_dummy_inputs_for_encoder_and_decoder(
               A_				,				A_				,				A_				,				A_				,				A_						)
           _lowerCamelCase			    =				{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
           _lowerCamelCase			    =				dict(**A_				,				**A_						)

           if self.use_past:
                 if not is_torch_available():
                       raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
                 else:
                       import torch
                 _lowerCamelCase    ,					_lowerCamelCase			    =				common_inputs['''input_ids'''].shape
                 _lowerCamelCase			    =				common_inputs['''decoder_input_ids'''].shape[1]
                 _lowerCamelCase    ,					_lowerCamelCase			    =				self.num_attention_heads
                 _lowerCamelCase			    =				(
                     batch,
                     num_encoder_attention_heads,
                     encoder_seq_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )
                 _lowerCamelCase			    =				decoder_seq_length + 3
                 _lowerCamelCase			    =				(
                     batch,
                     num_decoder_attention_heads,
                     decoder_past_length,
                     self._config.hidden_size // num_decoder_attention_heads,
                 )

                 _lowerCamelCase			    =				torch.cat(
                     [common_inputs['''decoder_attention_mask'''], torch.ones(A_				,				A_						)]				,				dim=1						)

                 _lowerCamelCase			    =				[]
                 # If the number of encoder and decoder layers are present in the model configuration, both are considered
                 _lowerCamelCase    ,					_lowerCamelCase			    =				self.num_layers
                 _lowerCamelCase			    =				min(A_				,				A_						)
                 _lowerCamelCase			    =				max(A_				,				A_						) - min_num_layers
                 _lowerCamelCase			    =				'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

                 for _ in range(A_						):
                       common_inputs["past_key_values"].append(
                           (
                               torch.zeros(A_						),
                               torch.zeros(A_						),
                               torch.zeros(A_						),
                               torch.zeros(A_						),
                           )						)
                 # TODO: test this.
                 _lowerCamelCase			    =				encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
                 for _ in range(A_				,				A_						):
                       common_inputs["past_key_values"].append((torch.zeros(A_						), torch.zeros(A_						))						)
           return common_inputs



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = -1				,				A_ = -1				,				A_ = False				,				A_ = None				,				) ->				Mapping[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self._generate_dummy_inputs_for_encoder_and_decoder(
               A_				,				A_				,				A_				,				A_				,				A_						)

           if self.use_past:
                 if not is_torch_available():
                       raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
                 else:
                       import torch
                 _lowerCamelCase    ,					_lowerCamelCase			    =				common_inputs['''input_ids'''].shape
                 # Not using the same length for past_key_values
                 _lowerCamelCase			    =				seqlen + 2
                 _lowerCamelCase    ,					_lowerCamelCase			    =				self.num_layers
                 _lowerCamelCase    ,					_lowerCamelCase			    =				self.num_attention_heads
                 _lowerCamelCase			    =				(
                     batch,
                     num_encoder_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )

                 _lowerCamelCase			    =				common_inputs['''attention_mask'''].dtype
                 _lowerCamelCase			    =				torch.cat(
                     [common_inputs['''attention_mask'''], torch.ones(A_				,				A_				,				dtype=A_						)]				,				dim=1						)
                 _lowerCamelCase			    =				[
                     (torch.zeros(A_						), torch.zeros(A_						)) for _ in range(A_						)
                 ]
           return common_inputs



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = -1				,				A_ = -1				,				A_ = False				,				A_ = None				,				) ->				Mapping[str, Any]:






           """simple docstring"""
           # Copied from OnnxConfig.generate_dummy_inputs
           # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _lowerCamelCase			    =				compute_effective_axis_dimension(
               A_				,				fixed_dimension=OnnxConfig.default_fixed_batch				,				num_token_to_add=0						)

           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           _lowerCamelCase			    =				tokenizer.num_special_tokens_to_add(A_						)
           _lowerCamelCase			    =				compute_effective_axis_dimension(
               A_				,				fixed_dimension=OnnxConfig.default_fixed_sequence				,				num_token_to_add=A_						)

           # Generate dummy inputs according to compute batch and sequence
           _lowerCamelCase			    =				[''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
           _lowerCamelCase			    =				dict(tokenizer(A_				,				return_tensors=A_						)						)
           return common_inputs



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = -1				,				A_ = -1				,				A_ = False				,				A_ = None				,				) ->				Mapping[str, Any]:






           """simple docstring"""
           if self.task in ["default", "seq2seq-lm"]:
                 _lowerCamelCase			    =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                     A_				,				batch_size=A_				,				seq_length=A_				,				is_pair=A_				,				framework=A_						)

           else:
                 _lowerCamelCase			    =				self._generate_dummy_inputs_for_causal_lm(
                     A_				,				batch_size=A_				,				seq_length=A_				,				is_pair=A_				,				framework=A_						)

           return common_inputs



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_						) ->				Tuple:






           """simple docstring"""
           if self.task in ["default", "seq2seq-lm"]:
                 _lowerCamelCase			    =				super()._flatten_past_key_values_(A_				,				A_				,				A_				,				A_						)
           else:
                 _lowerCamelCase			    =				super(A_				,				self						)._flatten_past_key_values_(
                     A_				,				A_				,				A_				,				A_						)





     @property
     def 		UpperCamelCase_						(					self						) ->				float:






           """simple docstring"""
           return 1E-4
import argparse
import json
import subprocess
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				[]

      _lowerCamelCase			    =				(
          F'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
          ''' https://api.github.com/repos/huggingface/transformers/actions/runners'''
      )
      _lowerCamelCase			    =				subprocess.run(__UpperCAmelCase ,						shell=__UpperCAmelCase ,						stdout=subprocess.PIPE		)
      _lowerCamelCase			    =				output.stdout.decode('''utf-8'''		)
      _lowerCamelCase			    =				json.loads(__UpperCAmelCase		)

      _lowerCamelCase			    =				status['''runners''']
      for runner in runners:
            if runner["name"] in target_runners:
                  if runner["status"] == "offline":
                        offline_runners.append(__UpperCAmelCase		)

    # save the result so we can report them on Slack
      with open('''offline_runners.txt''' ,						'''w'''		) as fp:
            fp.write(json.dumps(__UpperCAmelCase		)		)

      if len(__UpperCAmelCase		) > 0:
            _lowerCamelCase			    =				'''\n'''.join([x['''name'''] for x in offline_runners]		)
            raise ValueError(F'The following runners are offline:\n{failed}'		)


if __name__ == "__main__":
     def       __magic_name__(  __UpperCAmelCase		)						->    str:



           '''simple docstring'''

           return values.split(''','''		)

     snake_case__				= argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--target_runners',
         default=None,
         type=list_str,
         required=True,
         help='Comma-separated list of runners to check status.',
     )

     parser.add_argument(
         '--token', default=None, type=str, required=True, help='A token that has actions:read permission.'
     )
     snake_case__				= parser.parse_args()




     get_runner_status(args.target_runners, args.token)