





import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
lowerCAmelCase_						   =    {
    """/attention/""": """/0/SelfAttention/""",
    """/self_attention/""": """/0/SelfAttention/""",
    """/encoder_decoder_attention/""": """/1/EncDecAttention/""",
    """value""": """v""",
    """query""": """q""",
    """key""": """k""",
    """out""": """o""",
    """pre_self_attention_layer_norm""": """0/layer_norm""",
    """pre_cross_attention_layer_norm""": """1/layer_norm""",
    """pre_attention_layer_norm""": """0/layer_norm""",  # previously 1, but seems wrong
    """token_embedder""": """shared""",
    """encoder_norm""": """final_layer_norm""",
    """decoder_norm""": """final_layer_norm""",
    """relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
    """router/router_weights/w/""": """router/classifier/""",
    """roer/roer_weights/w/""": """router/classifier/""",
    """logits_dense""": """lm_head""",
}


def     lowerCamelCase_	(      lowerCAmelCase:	str				)->		Dict:
  # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
  # the original model
  _snake_case   :				Optional[int]														=	list(s_dict.keys()				)
  for key in keys:
    _snake_case   :				Union[str, Any]														=	R'.*/layers_(\d+)'
    _snake_case   :				Union[str, Any]														=	key
    if re.match(lowerCAmelCase    ,							lowerCAmelCase				):
      _snake_case   :				Optional[Any]														=	re.sub(R'layers_(\d+)'    ,							R'block/\1/layer'    ,							lowerCAmelCase				)

    _snake_case   :				Optional[Any]														=	R'(encoder|decoder)\/'

    if re.match(lowerCAmelCase    ,							lowerCAmelCase				):
      _snake_case   :				int														=	re.match(lowerCAmelCase    ,							lowerCAmelCase				).groups()
      if groups[0] == "encoder":
        _snake_case   :				Optional[Any]														=	re.sub(R'/mlp/'    ,							R'/1/mlp/'    ,							lowerCAmelCase				)
        _snake_case   :				Optional[Any]														=	re.sub(R'/pre_mlp_layer_norm/'    ,							R'/1/layer_norm/'    ,							lowerCAmelCase				)

      elif groups[0] == "decoder":
        _snake_case   :				Tuple														=	re.sub(R'/mlp/'    ,							R'/2/mlp/'    ,							lowerCAmelCase				)
        _snake_case   :				Union[str, Any]														=	re.sub(R'/pre_mlp_layer_norm/'    ,							R'/2/layer_norm/'    ,							lowerCAmelCase				)

        # 2. Convert other classic mappings
    for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
      if old_key in new_key:
        _snake_case   :				Optional[int]														=	new_key.replace(lowerCAmelCase    ,							lowerCAmelCase				)

    print(F"""{key} -> {new_key}"""				)
    _snake_case   :				List[str]														=	s_dict.pop(lowerCAmelCase				)

  if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
    _snake_case   :				Optional[int]														=	s_dict[
        'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
    ].T
  if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
    _snake_case   :				Any														=	s_dict[
        'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
    ].T

  # 3. Take extra care of the EXPERTS layer
  for key in list(s_dict.keys()				):
    if "expert" in key:
      _snake_case   :				str														=	s_dict[key].shape[0]
      _snake_case   :				Any														=	s_dict[key]
      for idx in range(lowerCAmelCase				):
        _snake_case   :				Tuple														=	expert_weihts[idx]
        print(F"""{key} -> {key.replace('expert/'    ,							'nested fstring'				)}"""				)

      s_dict.pop(lowerCAmelCase				)

  return s_dict


lowerCAmelCase_						   =    {
    """NUM_ENCODER_LAYERS""": """num_layers""",
    """NUM_DECODER_LAYERS""": """num_decoder_layers""",
    """NUM_HEADS""": """num_heads""",
    """HEAD_DIM""": """d_kv""",
    """EMBED_DIM""": """d_model""",
    """MLP_DIM""": """d_ff""",
    """NUM_SELECTED_EXPERTS""": """num_selected_experts""",
    """NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
    """NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
    """dense.MlpBlock.activations""": """feed_forward_proj""",
}


def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	Tuple				)->		int:
  # Convert a google style config to the hugging face fromat
  import regex as re

  with open(lowerCAmelCase    ,							'r'				) as f:
    _snake_case   :				Any														=	f.read()

  _snake_case   :				Union[str, Any]														=	re.findall(R'(.*) = ([0-9.]*)'    ,							lowerCAmelCase				)
  _snake_case   :				List[str]														=	{}
  for param, value in regex_match:
    if param in GIN_TO_CONFIG_MAPPING and value != "":
      _snake_case   :				Optional[Any]														=	float(lowerCAmelCase				) if '.' in value else int(lowerCAmelCase				)

  _snake_case   :				Any														=	re.findall(R'(.*activations) = \(\'(.*)\',\)'    ,							lowerCAmelCase				)[0]
  _snake_case   :				Any														=	str(activation[1]				)

  _snake_case   :				int														=	num_experts
  _snake_case   :				int														=	SwitchTransformersConfig(**lowerCAmelCase				)
  return config


def     lowerCamelCase_	(      lowerCAmelCase:	Union[str, Any]    ,							lowerCAmelCase:	Optional[int]    ,							lowerCAmelCase:	Optional[Any]=None    ,							lowerCAmelCase:	Optional[Any]="./"    ,							lowerCAmelCase:	int=8				)->		List[Any]:
  # Initialise PyTorch model

  print(F"""Loading flax weights from : {flax_checkpoint_path}"""				)
  _snake_case   :				int														=	checkpoints.load_tax_checkpoint(lowerCAmelCase				)

  if gin_file is not None:
    _snake_case   :				Optional[int]														=	convert_gin_to_config(lowerCAmelCase    ,							lowerCAmelCase				)
  else:
    _snake_case   :				Optional[Any]														=	SwitchTransformersConfig.from_pretrained(lowerCAmelCase				)

  _snake_case   :				Dict														=	SwitchTransformersForConditionalGeneration(lowerCAmelCase				)

  _snake_case   :				Optional[Any]														=	flax_params['target']
  _snake_case   :				Any														=	flatten_dict(lowerCAmelCase    ,							sep='/'				)
  _snake_case   :				Union[str, Any]														=	rename_keys(lowerCAmelCase				)
  _snake_case   :				Optional[Any]														=	unflatten_dict(lowerCAmelCase    ,							sep='/'				)

  # Load the flax params in the PT model
  load_flax_weights_in_pytorch_model(lowerCAmelCase    ,							lowerCAmelCase				)

  print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
  pt_model.save_pretrained(lowerCAmelCase				)


if __name__ == "__main__":
      lowerCAmelCase_						   =    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--switch_t5x_checkpoint_path""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"""
              """ model architecture. If not provided, a `gin_file` has to be provided."""
          ),
      )
      parser.add_argument(
          """--gin_file""",
          default=None,
          type=str,
          required=False,
          help="""Path to the gin config file. If not provided, a `config_file` has to be passed   """,
      )
      parser.add_argument(
          """--config_name""", default=None, type=str, required=False, help="""Config name of SwitchTransformers model."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output pytorch model."""
      )
      parser.add_argument("""--num_experts""", default=8, type=int, required=False, help="""Number of experts""")
      lowerCAmelCase_						   =    parser.parse_args()
      convert_flax_checkpoint_to_pytorch(
          args.switch_tax_checkpoint_path,
          args.config_name,
          args.gin_file,
          args.pytorch_dump_folder_path,
          args.num_experts,
      )











import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


lowerCAmelCase_						   =    """http://www.mocksite.com/file1.txt"""
lowerCAmelCase_						   =    """\"text\": [\"foo\", \"foo\"]"""
lowerCAmelCase_						   =    """6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"""



class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       int      					=200
  a_							:       List[str]      					={"""Content-Length""": """100"""}
  a_							:       Tuple      					={}







  def UpperCamelCase_ (      self      :					Any      ,				**UpperCamelCase      :					Any							):


    '''simple docstring'''





    return [bytes(UpperCamelCase      ,				'utf-8'							)]




def     lowerCamelCase_	(      *lowerCAmelCase:	Tuple    ,							**lowerCAmelCase:	Tuple				)->		str:
  return MockResponse()


@pytest.mark.parametrize('urls_type'    ,							[str, list, dict]				)
def     lowerCamelCase_	(      lowerCAmelCase:	Union[str, Any]    ,							lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	Dict				)->		Optional[Any]:
  import requests

  monkeypatch.setattr(lowerCAmelCase    ,							'request'    ,							lowerCAmelCase				)

  _snake_case   :				List[str]														=	URL
  if issubclass(lowerCAmelCase    ,							lowerCAmelCase				):
    _snake_case   :				Optional[int]														=	url
  elif issubclass(lowerCAmelCase    ,							lowerCAmelCase				):
    _snake_case   :				Any														=	[url]
  elif issubclass(lowerCAmelCase    ,							lowerCAmelCase				):
    _snake_case   :				Optional[Any]														=	{'train': url}
  _snake_case   :				int														=	'dummy'
  _snake_case   :				Optional[Any]														=	'downloads'
  _snake_case   :				Union[str, Any]														=	tmp_path
  _snake_case   :				Dict														=	DownloadConfig(
      cache_dir=os.path.join(lowerCAmelCase    ,							lowerCAmelCase				)    ,							use_etag=lowerCAmelCase    ,							)
  _snake_case   :				str														=	DownloadManager(dataset_name=lowerCAmelCase    ,							download_config=lowerCAmelCase				)
  _snake_case   :				Optional[int]														=	dl_manager.download(lowerCAmelCase				)
  _snake_case   :				Tuple														=	urls
  for downloaded_paths in [downloaded_paths]:
    if isinstance(lowerCAmelCase    ,							lowerCAmelCase				):
      _snake_case   :				Optional[Any]														=	[downloaded_paths]
      _snake_case   :				List[str]														=	[urls]
    elif isinstance(lowerCAmelCase    ,							lowerCAmelCase				):
      assert "train" in downloaded_paths.keys()
      _snake_case   :				Any														=	downloaded_paths.values()
      _snake_case   :				List[str]														=	urls.values()
    assert downloaded_paths
    for downloaded_path, input_url in zip(lowerCAmelCase    ,							lowerCAmelCase				):
      assert downloaded_path == dl_manager.downloaded_paths[input_url]
      _snake_case   :				str														=	Path(lowerCAmelCase				)
      _snake_case   :				int														=	downloaded_path.parts
      assert parts[-1] == HASH
      assert parts[-2] == cache_subdir
      assert downloaded_path.exists()
      _snake_case   :				List[str]														=	downloaded_path.read_text()
      assert content == CONTENT
      _snake_case   :				Any														=	downloaded_path.with_suffix('.json'				)
      assert metadata_downloaded_path.exists()
      _snake_case   :				Tuple														=	json.loads(metadata_downloaded_path.read_text()				)
      assert metadata_content == {"url": URL, "etag": None}


@pytest.mark.parametrize('paths_type'    ,							[str, list, dict]				)
def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	Optional[int]    ,							lowerCAmelCase:	Any				)->		str:
  _snake_case   :				str														=	str(lowerCAmelCase				)
  if issubclass(lowerCAmelCase    ,							lowerCAmelCase				):
    _snake_case   :				str														=	filename
  elif issubclass(lowerCAmelCase    ,							lowerCAmelCase				):
    _snake_case   :				List[Any]														=	[filename]
  elif issubclass(lowerCAmelCase    ,							lowerCAmelCase				):
    _snake_case   :				Optional[Any]														=	{'train': filename}
  _snake_case   :				Any														=	'dummy'
  _snake_case   :				Union[str, Any]														=	xz_file.parent
  _snake_case   :				int														=	'extracted'
  _snake_case   :				Union[str, Any]														=	DownloadConfig(
      cache_dir=lowerCAmelCase    ,							use_etag=lowerCAmelCase    ,							)
  _snake_case   :				List[str]														=	DownloadManager(dataset_name=lowerCAmelCase    ,							download_config=lowerCAmelCase				)
  _snake_case   :				Dict														=	dl_manager.extract(lowerCAmelCase				)
  _snake_case   :				Optional[int]														=	paths
  for extracted_paths in [extracted_paths]:
    if isinstance(lowerCAmelCase    ,							lowerCAmelCase				):
      _snake_case   :				List[str]														=	[extracted_paths]
      _snake_case   :				int														=	[paths]
    elif isinstance(lowerCAmelCase    ,							lowerCAmelCase				):
      assert "train" in extracted_paths.keys()
      _snake_case   :				Optional[int]														=	extracted_paths.values()
      _snake_case   :				str														=	paths.values()
    assert extracted_paths
    for extracted_path, input_path in zip(lowerCAmelCase    ,							lowerCAmelCase				):
      assert extracted_path == dl_manager.extracted_paths[input_path]
      _snake_case   :				List[str]														=	Path(lowerCAmelCase				)
      _snake_case   :				Optional[Any]														=	extracted_path.parts
      assert parts[-1] == hash_url_to_filename(lowerCAmelCase    ,							etag=lowerCAmelCase				)
      assert parts[-2] == extracted_subdir
      assert extracted_path.exists()
      _snake_case   :				Optional[int]														=	extracted_path.read_text()
      _snake_case   :				int														=	text_file.read_text()
      assert extracted_file_content == expected_file_content


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	List[Any]				)->		Dict:
  assert path.endswith('.jsonl'				)
  for num_items, line in enumerate(lowerCAmelCase    ,							start=1				):
    _snake_case   :				Dict														=	json.loads(line.decode('utf-8'				)				)
    assert item.keys() == {"col_1", "col_2", "col_3"}
  assert num_items == 4


@pytest.mark.parametrize('archive_jsonl'    ,							['tar_jsonl_path', 'zip_jsonl_path']				)
def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	List[str]				)->		Dict:
  _snake_case   :				List[str]														=	request.getfixturevalue(lowerCAmelCase				)
  _snake_case   :				Optional[Any]														=	DownloadManager()
  for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(lowerCAmelCase				)    ,							start=1				):
    _test_jsonl(lowerCAmelCase    ,							lowerCAmelCase				)
  assert num_jsonl == 2


@pytest.mark.parametrize('archive_nested_jsonl'    ,							['tar_nested_jsonl_path', 'zip_nested_jsonl_path']				)
def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	int				)->		str:
  _snake_case   :				List[Any]														=	request.getfixturevalue(lowerCAmelCase				)
  _snake_case   :				Optional[int]														=	DownloadManager()
  for num_tar, (path, file) in enumerate(dl_manager.iter_archive(lowerCAmelCase				)    ,							start=1				):
    for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(lowerCAmelCase				)    ,							start=1				):
      _test_jsonl(lowerCAmelCase    ,							lowerCAmelCase				)
  assert num_tar == 1
  assert num_jsonl == 2







def     lowerCamelCase_	(      lowerCAmelCase:	Any				)->		int:
  _snake_case   :				Tuple														=	DownloadManager()
  for num_file, file in enumerate(dl_manager.iter_files(lowerCAmelCase				)    ,							start=1				):
    assert os.path.basename(lowerCAmelCase				) == ("test.txt" if num_file == 1 else "train.txt")
  assert num_file == 2




