"""simple docstring"""






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :Optional[Any]="attention"		) ->    List[Any]:
      '''simple docstring'''


      lowercase														=	params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
      lowercase														=	params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
      lowercase														=	params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
      lowercase														=	params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
      return k, o, q, v


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[int]					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :Dict=False		) ->    Tuple:
      '''simple docstring'''


      if split_mlp_wi:
            lowercase														=	params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
            lowercase														=	params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
            lowercase														=	(wi_a, wi_a)
      else:
            lowercase														=	params[f'{prefix}/layers_{i}/mlp/wi/kernel']

      lowercase														=	params[f'{prefix}/layers_{i}/mlp/wo/kernel']
      return wi, wo


def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :Optional[int]					,		lowerCAmelCase__     :Optional[int]		) ->    Tuple:
      '''simple docstring'''


      return params[f'{prefix}/layers_{i}/{layer_name}/scale']


def 			UpperCAmelCase__       (				lowerCAmelCase__     :dict					,		*, lowerCAmelCase__     :int					,		lowerCAmelCase__     :bool		) ->    List[Any]:
      '''simple docstring'''


      lowercase														=	traverse_util.flatten_dict(variables["""target"""]		)
      lowercase														=	{"""/""".join(lowerCAmelCase__		): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      lowercase														=	"""encoder/layers_0/mlp/wi_0/kernel""" in old
      print("""Split MLP:"""					,		lowerCAmelCase__		)

      lowercase														=	collections.OrderedDict()

      # Shared embeddings.
      lowercase														=	old["""token_embedder/embedding"""]

      # Encoder.
      for i in range(lowerCAmelCase__		):
            # Block i, layer 0 (Self Attention).
            lowercase														=	tax_layer_norm_lookup(lowerCAmelCase__					,		lowerCAmelCase__					,		"""encoder"""					,		"""pre_attention_layer_norm"""		)
            lowercase							,							lowercase							,							lowercase							,							lowercase														=	tax_attention_lookup(lowerCAmelCase__					,		lowerCAmelCase__					,		"""encoder"""					,		"""attention"""		)
            lowercase														=	layer_norm
            lowercase														=	k.T
            lowercase														=	o.T
            lowercase														=	q.T
            lowercase														=	v.T

            # Block i, layer 1 (MLP).
            lowercase														=	tax_layer_norm_lookup(lowerCAmelCase__					,		lowerCAmelCase__					,		"""encoder"""					,		"""pre_mlp_layer_norm"""		)
            lowercase							,							lowercase														=	tax_mlp_lookup(lowerCAmelCase__					,		lowerCAmelCase__					,		"""encoder"""					,		lowerCAmelCase__		)
            lowercase														=	layer_norm
            if split_mlp_wi:
                  lowercase														=	wi[0].T
                  lowercase														=	wi[1].T
            else:
                  lowercase														=	wi.T
            lowercase														=	wo.T

      lowercase														=	old[
          """encoder/relpos_bias/rel_embedding"""
      ].T
      lowercase														=	old["""encoder/encoder_norm/scale"""]

      if not is_encoder_only:
            # Decoder.
            for i in range(lowerCAmelCase__		):
                  # Block i, layer 0 (Self Attention).
                  lowercase														=	tax_layer_norm_lookup(lowerCAmelCase__					,		lowerCAmelCase__					,		"""decoder"""					,		"""pre_self_attention_layer_norm"""		)
                  lowercase							,							lowercase							,							lowercase							,							lowercase														=	tax_attention_lookup(lowerCAmelCase__					,		lowerCAmelCase__					,		"""decoder"""					,		"""self_attention"""		)
                  lowercase														=	layer_norm
                  lowercase														=	k.T
                  lowercase														=	o.T
                  lowercase														=	q.T
                  lowercase														=	v.T

                  # Block i, layer 1 (Cross Attention).
                  lowercase														=	tax_layer_norm_lookup(lowerCAmelCase__					,		lowerCAmelCase__					,		"""decoder"""					,		"""pre_cross_attention_layer_norm"""		)
                  lowercase							,							lowercase							,							lowercase							,							lowercase														=	tax_attention_lookup(lowerCAmelCase__					,		lowerCAmelCase__					,		"""decoder"""					,		"""encoder_decoder_attention"""		)
                  lowercase														=	layer_norm
                  lowercase														=	k.T
                  lowercase														=	o.T
                  lowercase														=	q.T
                  lowercase														=	v.T

                  # Block i, layer 2 (MLP).
                  lowercase														=	tax_layer_norm_lookup(lowerCAmelCase__					,		lowerCAmelCase__					,		"""decoder"""					,		"""pre_mlp_layer_norm"""		)
                  lowercase							,							lowercase														=	tax_mlp_lookup(lowerCAmelCase__					,		lowerCAmelCase__					,		"""decoder"""					,		lowerCAmelCase__		)
                  lowercase														=	layer_norm
                  if split_mlp_wi:
                        lowercase														=	wi[0].T
                        lowercase														=	wi[1].T
                  else:
                        lowercase														=	wi.T
                  lowercase														=	wo.T

            lowercase														=	old["""decoder/decoder_norm/scale"""]
            lowercase														=	old[
                """decoder/relpos_bias/rel_embedding"""
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  lowercase														=	old["""decoder/logits_dense/kernel"""].T

      return new


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :bool		) ->    Tuple:
      '''simple docstring'''


      lowercase														=	collections.OrderedDict([(k, torch.from_numpy(v.copy()		)) for (k, v) in converted_params.items()]		)

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            lowercase														=	state_dict["""shared.weight"""]

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  lowercase														=	state_dict["""shared.weight"""]

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print("""Using shared word embeddings as lm_head."""		)
                  lowercase														=	state_dict["""shared.weight"""]

      return state_dict


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Optional[int]					,		lowerCAmelCase__     :Tuple		) ->    int:
      '''simple docstring'''


      lowercase														=	checkpoints.load_tax_checkpoint(lowerCAmelCase__		)
      lowercase														=	convert_tax_to_pytorch(lowerCAmelCase__					,		num_layers=config.num_layers					,		is_encoder_only=lowerCAmelCase__		)
      lowercase														=	make_state_dict(lowerCAmelCase__					,		lowerCAmelCase__		)
      model.load_state_dict(lowerCAmelCase__					,		strict=lowerCAmelCase__		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :Optional[Any]					,		lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :bool = False		) ->    List[str]:
      '''simple docstring'''


      lowercase														=	TaConfig.from_json_file(lowerCAmelCase__		)
      print(f'Building PyTorch model from configuration: {config}'		)
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            lowercase														=	TaEncoderModel(lowerCAmelCase__		)
      else:
            lowercase														=	TaForConditionalGeneration(lowerCAmelCase__		)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__		)

      # Save pytorch-model
      print(f'Save PyTorch model to {pytorch_dump_path}'		)
      model.save_pretrained(lowerCAmelCase__		)

      # Verify that we can load the checkpoint.
      model.from_pretrained(lowerCAmelCase__		)
      print("""Done"""		)


if __name__ == "__main__":
       __lowerCAmelCase    :       Dict					     =argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
       # Required parameters
       parser.add_argument(
           """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
       )
       __lowerCAmelCase    :       str					     =parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
       )




"""simple docstring"""






import argparse
import json
import subprocess


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :List[Any]		) ->    Optional[Any]:
      '''simple docstring'''


      lowercase														=	[]

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

      lowercase														=	status["""runners"""]
      for runner in runners:
            if runner["name"] in target_runners:
                  if runner["status"] == "offline":
                        offline_runners.append(lowerCAmelCase__		)

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

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


if __name__ == "__main__":


       def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[int]		) ->    Tuple:
             '''simple docstring'''


             return values.split(""","""		)

       __lowerCAmelCase    :       str					     =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."""
       )
       __lowerCAmelCase    :       str					     =parser.parse_args()






       get_runner_status(args.target_runners, args.token)



