






"""simple docstring"""






import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int



def 					_A			(__a    ,				__a							) ->       List[Any]:
    """simple docstring"""






    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
        # expert layer
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		flax_key_tuple[:-1] + ('''weight''',)
        SCREAMING_SNAKE_CASE_      :  Dict    						=		torch.permute(__a    ,				(0, 2, 1)							)
    elif flax_key_tuple[-1] == "kernel" and ".".join(__a							):
        # linear layer
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		flax_key_tuple[:-1] + ('''weight''',)
        SCREAMING_SNAKE_CASE_      :  int    						=		flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		flax_key_tuple[:-1] + ('''weight''',)

    return flax_key_tuple, flax_tensor



def 					_A			(__a    ,				__a    ,				__a							) ->       Union[str, Any]:
    """simple docstring"""






    if "metadata" in layer:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		layer.split('''metadata'''							)
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		''''''.join(split_layer[0]							)[:-1]
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[tuple(('''metadata''' + split_layer[1]).split('''/'''							)							)]
    elif "kvstore" in layer:
        SCREAMING_SNAKE_CASE_      :  Any    						=		layer.split('''kvstore'''							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		''''''.join(split_layer[0]							)[:-1]
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[tuple(('''kvstore''' + split_layer[1]).split('''/'''							)							)]

    else:
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		layer.split('''/'''							)
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		'''/'''.join(split_layer[:-1]							)
        SCREAMING_SNAKE_CASE_      :  str    						=		(split_layer[-1],)

    if "kvstore/path" in layer:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
    elif "kvstore/driver" in layer:
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		'''file'''
    else:
        SCREAMING_SNAKE_CASE_      :  int    						=		checkpoint_info[layer]

    return curr_real_layer_name, split_layer, content



def 					_A			(__a    ,				__a							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Dict    						=		rename_keys(__a							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		{}
    for k, v in current_block.items():
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		v
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		new_current_block
    torch.save(__a    ,				__a							)



def 					_A			(__a    ,				__a    ,				__a    ,				__a    ,				__a = WEIGHTS_NAME							) ->       Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		convert_file_size_to_int(__a							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[]
    SCREAMING_SNAKE_CASE_      :  Any    						=		{}
    SCREAMING_SNAKE_CASE_      :  Any    						=		0
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0

    os.makedirs(__a    ,				exist_ok=__a							)
    with gfile.GFile(switch_checkpoint_path + '''/checkpoint'''    ,				'''rb'''							) as fp:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		serialization.msgpack_restore(fp.read()							)['''optimizer''']['''target''']
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		flatten_dict(__a    ,				sep='''/'''							)

    SCREAMING_SNAKE_CASE_      :  Tuple    						=		{}
    for layer in checkpoint_info.keys():
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		get_key_and_tensorstore_dict(
            __a    ,				__a    ,				__a							)
        if curr_real_layer_name in all_layers:
            SCREAMING_SNAKE_CASE_      :  Any    						=		content
        else:
            SCREAMING_SNAKE_CASE_      :  int    						=		{split_layer[-1]: content}

    for key in all_layers.keys():
        # open tensorstore file
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		ts.open(unflatten_dict(all_layers[key]							)							).result().read().result()
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		torch.tensor(__a							)
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		raw_weights.numel() * dtype_byte_size(raw_weights.dtype							)

        # use the renaming pattern from the small conversion scripts
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		rename_base_flax_keys(tuple(key.split('''/'''							)							)    ,				__a							)
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		'''/'''.join(__a							)

        # If this weight is going to tip up over the maximal size, we split.
        if current_block_size + weight_size > max_shard_size:
            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		os.path.join(
                __a    ,				weights_name.replace('''.bin'''    ,				f'-{len(__a							)+1:05d}-of-???.bin'							)							)
            rename_and_save_block(__a    ,				__a							)
            sharded_state_dicts.append(current_block.keys()							)
            del current_block
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{}
            SCREAMING_SNAKE_CASE_      :  str    						=		0

        SCREAMING_SNAKE_CASE_      :  str    						=		raw_weights.to(getattr(__a    ,				__a							)							)
        current_block_size += weight_size
        total_size += weight_size

    # Add the last block
    SCREAMING_SNAKE_CASE_      :  Dict    						=		os.path.join(__a    ,				weights_name.replace('''.bin'''    ,				f'-{len(__a							)+1:05d}-of-???.bin'							)							)
    rename_and_save_block(__a    ,				__a							)
    sharded_state_dicts.append(current_block.keys()							)

    # If we only have one shard, we return it
    if len(__a							) == 1:
        return {weights_name: sharded_state_dicts[0]}, None

    # Otherwise, let's build the index
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		{}
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{}
    for idx, shard in enumerate(__a							):
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		weights_name.replace(
            '''.bin'''    ,				f'-{idx+1:05d}-of-{len(__a							):05d}.bin'							)  # len(sharded_state_dicts):05d}
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		os.path.join(__a    ,				weights_name.replace('''.bin'''    ,				f'-{idx+1:05d}-of-???.bin'							)							)
        os.rename(__a    ,				os.path.join(__a    ,				__a							)							)
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		shard
        for key in shard:
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		shard_file

    # Add the metadata
    SCREAMING_SNAKE_CASE_      :  Dict    						=		{'''total_size''': total_size}
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{'''metadata''': metadata, '''weight_map''': weight_map}

    with open(os.path.join(__a    ,				__a							)    ,				'''w'''    ,				encoding='''utf-8'''							) as f:
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		json.dumps(__a    ,				indent=2    ,				sort_keys=__a							) + '''\n'''
        f.write(__a							)

    return metadata, index


if __name__ == "__main__":
  UpperCAmelCase_   : Tuple   			= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--switch_t5x_checkpoint_path""",
      default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
      type=str,
      required=False,
      help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
  )
  parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
  parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
      type=str,
      required=False,
      help="""Path to the output pytorch model.""",
  )
  UpperCAmelCase_   : Any   			= parser.parse_args()
  shard_on_the_fly(
      args.switch_tax_checkpoint_path,
      args.pytorch_dump_folder_path,
      args.max_shard_size,
      args.dtype,
  )





def 					_A			() ->       Tuple:
    """simple docstring"""






    from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''							)
    config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		SwitchTransformersForConditionalGeneration.from_pretrained(
        '''/home/arthur_huggingface_co/transformers/switch_converted'''    ,				device_map='''auto'''							)

    SCREAMING_SNAKE_CASE_      :  str    						=		TaTokenizer.from_pretrained('''t5-small'''							)
    SCREAMING_SNAKE_CASE_      :  str    						=		'''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

    SCREAMING_SNAKE_CASE_      :  List[str]    						=		tokenizer(__a    ,				return_tensors='''pt'''							).input_ids
    SCREAMING_SNAKE_CASE_      :  int    						=		model.generate(__a    ,				decoder_start_token_id=0							)
    print(tokenizer.decode(out[0]							)							)








"""simple docstring"""


import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]		,   snake_case__							:					Any=7    ):
  """simple docstring"""

  _snake_case							:		Any								=  None
  if token is not None:
    _snake_case							:		Any								=  {"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

  # The id of a workflow (not of a workflow run)
  _snake_case							:		List[str]								=  """636036"""

  _snake_case							:		Union[str, Any]								=  F"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"
  # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
  url += F"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"

  _snake_case							:		str								=  requests.get(snake_case__		,   headers=snake_case__    ).json()

  return result["workflow_runs"]


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]    ):
  """simple docstring"""

  _snake_case							:		str								=  get_daily_ci_runs(snake_case__    )
  _snake_case							:		str								=  None
  for workflow_run in workflow_runs:
    if workflow_run["status"] == "completed":
      _snake_case							:		List[str]								=  workflow_run["""id"""]
      break

  return workflow_run_id


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					Union[str, Any]		,   snake_case__							:					Optional[int]    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  get_last_daily_ci_runs(snake_case__    )
  if workflow_run_id is not None:
    _snake_case							:		Optional[Any]								=  get_artifacts_links(worflow_run_id=snake_case__		,   token=snake_case__    )
    for artifact_name in artifact_names:
      if artifact_name in artifacts_links:
        _snake_case							:		Optional[int]								=  artifacts_links[artifact_name]
        download_artifact(
            artifact_name=snake_case__		,   artifact_url=snake_case__		,   output_dir=snake_case__		,   token=snake_case__    )






def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]		,   snake_case__							:					List[str]		,   snake_case__							:					int    ):
  """simple docstring"""

  get_last_daily_ci_artifacts(snake_case__		,   snake_case__		,   snake_case__    )

  _snake_case							:		int								=  {}
  for artifact_name in artifact_names:
    _snake_case							:		int								=  os.path.join(snake_case__		,   F"{artifact_name}.zip"    )
    if os.path.isfile(snake_case__    ):
      _snake_case							:		Tuple								=  {}
      with zipfile.ZipFile(snake_case__    ) as z:
        for filename in z.namelist():
          if not os.path.isdir(snake_case__    ):
            # read the file
            with z.open(snake_case__    ) as f:
              _snake_case							:		Any								=  f.read().decode("""UTF-8"""    )

  return results


