


"""simple docstring"""



import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard







@pytest.mark.parametrize(
    "compression_format, is_archive"						,							[
        ("7z", True),
        ("bz2", False),
        ("gzip", False),
        ("lz4", False),
        ("tar", True),
        ("xz", False),
        ("zip", True),
        ("zstd", False),
    ]						,							)
def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  Union[str, Any]						,							lowercase  :  List[Any]						,							lowercase  :  Optional[int]						,							lowercase  :  List[Any]						,							lowercase  :  Dict						,							lowercase  :  str						,							lowercase  :  Any						,							lowercase  :  Optional[Any]						,							lowercase  :  int						,							lowercase  :  str						,							lowercase  :  Union[str, Any]						,							)     ->						List[str]:


				"""simple docstring"""





				snake_case   :				List[Any]				 =   {
				    "7z": (seven_zip_file, SevenZipExtractor),
				    "bz2": (bza_file, BzipaExtractor),
				    "gzip": (gz_file, GzipExtractor),
				    "lz4": (lza_file, LzaExtractor),
				    "tar": (tar_file, TarExtractor),
				    "xz": (xz_file, XzExtractor),
				    "zip": (zip_file, ZipExtractor),
				    "zstd": (zstd_file, ZstdExtractor),
				}
				snake_case   :				List[str]				 =   input_paths_and_base_extractors[compression_format]
				if input_path is None:
								snake_case   :				Union[str, Any]				 =   F'for \'{compression_format}\' compression_format, '
								if compression_format == "7z":
												reason += require_pyazr.kwargs["reason"]
								elif compression_format == "lz4":
												reason += require_lza.kwargs["reason"]
								elif compression_format == "zstd":
												reason += require_zstandard.kwargs["reason"]
								pytest.skip(lowercase			)
				assert base_extractor.is_extractable(lowercase			)
				snake_case   :				Tuple				 =   tmp_path / ("extracted" if is_archive else "extracted.txt")
				base_extractor.extract(lowercase						,							lowercase			)
				if is_archive:
								assert output_path.is_dir()
								for file_path in output_path.iterdir():
												assert file_path.name == text_file.name
												snake_case   :				Dict				 =   file_path.read_text(encoding="utf-8"			)
				else:
								snake_case   :				Optional[Any]				 =   output_path.read_text(encoding="utf-8"			)
				snake_case   :				Optional[Any]				 =   text_file.read_text(encoding="utf-8"			)
				assert extracted_file_content == expected_file_content







@pytest.mark.parametrize(
    "compression_format, is_archive"						,							[
        ("7z", True),
        ("bz2", False),
        ("gzip", False),
        ("lz4", False),
        ("tar", True),
        ("xz", False),
        ("zip", True),
        ("zstd", False),
    ]						,							)
def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int]						,							lowercase  :  Dict						,							lowercase  :  str						,							lowercase  :  int						,							lowercase  :  List[str]						,							lowercase  :  List[Any]						,							lowercase  :  List[str]						,							lowercase  :  List[Any]						,							lowercase  :  int						,							lowercase  :  Optional[Any]						,							lowercase  :  List[str]						,							)     ->						List[Any]:


				"""simple docstring"""





				snake_case   :				List[Any]				 =   {
				    "7z": seven_zip_file,
				    "bz2": bza_file,
				    "gzip": gz_file,
				    "lz4": lza_file,
				    "tar": tar_file,
				    "xz": xz_file,
				    "zip": zip_file,
				    "zstd": zstd_file,
				}
				snake_case   :				str				 =   input_paths[compression_format]
				if input_path is None:
								snake_case   :				Optional[Any]				 =   F'for \'{compression_format}\' compression_format, '
								if compression_format == "7z":
												reason += require_pyazr.kwargs["reason"]
								elif compression_format == "lz4":
												reason += require_lza.kwargs["reason"]
								elif compression_format == "zstd":
												reason += require_zstandard.kwargs["reason"]
								pytest.skip(lowercase			)
				snake_case   :				Optional[int]				 =   Extractor.infer_extractor_format(lowercase			)
				assert extractor_format is not None
				snake_case   :				List[str]				 =   tmp_path / ("extracted" if is_archive else "extracted.txt")
				Extractor.extract(lowercase						,							lowercase						,							lowercase			)
				if is_archive:
								assert output_path.is_dir()
								for file_path in output_path.iterdir():
												assert file_path.name == text_file.name
												snake_case   :				Union[str, Any]				 =   file_path.read_text(encoding="utf-8"			)
				else:
								snake_case   :				Tuple				 =   output_path.read_text(encoding="utf-8"			)
				snake_case   :				Any				 =   text_file.read_text(encoding="utf-8"			)
				assert extracted_file_content == expected_file_content







@pytest.fixture
def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  Optional[Any]			)     ->						int:


				"""simple docstring"""





				import tarfile

				snake_case   :				Optional[int]				 =   tmp_path / "data_dot_dot"
				directory.mkdir()
				snake_case   :				List[str]				 =   directory / "tar_file_with_dot_dot.tar"
				with tarfile.TarFile(lowercase						,							"w"			) as f:
								f.add(lowercase						,							arcname=os.path.join(".."						,							text_file.name			)			)
				return path







@pytest.fixture
def   __lowerCAmelCase						(						lowercase  :  Any			)     ->						List[str]:


				"""simple docstring"""





				import tarfile

				snake_case   :				Tuple				 =   tmp_path / "data_sym_link"
				directory.mkdir()
				snake_case   :				Dict				 =   directory / "tar_file_with_sym_link.tar"
				os.symlink(".."						,							directory / "subdir"						,							target_is_directory=lowercase			)
				with tarfile.TarFile(lowercase						,							"w"			) as f:
								f.add(str(directory / "subdir"			)						,							arcname="subdir"			)  # str required by os.readlink on Windows and Python < 3.8
				return path







@pytest.mark.parametrize(
    "insecure_tar_file, error_log"						,							[("tar_file_with_dot_dot", "illegal path"), ("tar_file_with_sym_link", "Symlink")]						,							)
def   __lowerCAmelCase						(						lowercase  :  List[Any]						,							lowercase  :  Tuple						,							lowercase  :  int						,							lowercase  :  Optional[int]						,							lowercase  :  int						,							lowercase  :  int			)     ->						List[str]:


				"""simple docstring"""





				snake_case   :				Optional[Any]				 =   {
				    "tar_file_with_dot_dot": tar_file_with_dot_dot,
				    "tar_file_with_sym_link": tar_file_with_sym_link,
				}
				snake_case   :				Any				 =   insecure_tar_files[insecure_tar_file]
				snake_case   :				Optional[Any]				 =   tmp_path / "extracted"
				TarExtractor.extract(lowercase						,							lowercase			)
				assert caplog.text
				for record in caplog.records:
								assert record.levelname == "ERROR"
								assert error_log in record.msg







def   __lowerCAmelCase						(						lowercase  :  Tuple			)     ->						int:


				"""simple docstring"""





				snake_case   :				Tuple				 =   tmpdir / "not_a_zip_file"
				# From: https://github.com/python/cpython/pull/5053
				snake_case   :				List[str]				 =   (
				    b"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"
				    b"\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"
				    b"DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"
				    b"\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"
				)
				with not_a_zip_file.open("wb"			) as f:
								f.write(lowercase			)
				assert zipfile.is_zipfile(str(lowercase			)			)  # is a false positive for `zipfile`
				assert not ZipExtractor.is_extractable(lowercase			)  # but we're right









"""simple docstring"""



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
__snake_case             =   {
    """/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						(						lowercase  :  Optional[int]			)     ->						List[str]:


    """simple docstring"""





    snake_case   :				Optional[Any]				 =   list(s_dict.keys()			)
    for key in keys:
        snake_case   :				Any				 =   R".*/layers_(\d+)"
        snake_case   :				Tuple				 =   key
        if re.match(lowercase						,							lowercase			):
            snake_case   :				List[str]				 =   re.sub(R"layers_(\d+)"						,							R"block/\1/layer"						,							lowercase			)

        snake_case   :				Union[str, Any]				 =   R"(encoder|decoder)\/"

        if re.match(lowercase						,							lowercase			):
            snake_case   :				Any				 =   re.match(lowercase						,							lowercase			).groups()
            if groups[0] == "encoder":
                snake_case   :				Union[str, Any]				 =   re.sub(R"/mlp/"						,							R"/1/mlp/"						,							lowercase			)
                snake_case   :				int				 =   re.sub(R"/pre_mlp_layer_norm/"						,							R"/1/layer_norm/"						,							lowercase			)

            elif groups[0] == "decoder":
                snake_case   :				str				 =   re.sub(R"/mlp/"						,							R"/2/mlp/"						,							lowercase			)
                snake_case   :				List[str]				 =   re.sub(R"/pre_mlp_layer_norm/"						,							R"/2/layer_norm/"						,							lowercase			)

        # 2. Convert other classic mappings
        for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
            if old_key in new_key:
                snake_case   :				int				 =   new_key.replace(lowercase						,							lowercase			)

        print(F'{key} -> {new_key}'			)
        snake_case   :				Optional[Any]				 =   s_dict.pop(lowercase			)

    if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        snake_case   :				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   :				Optional[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   :				Tuple				 =   s_dict[key].shape[0]
            snake_case   :				int				 =   s_dict[key]
            for idx in range(lowercase			):
                snake_case   :				List[str]				 =   expert_weihts[idx]
                print(F'{key} -> {key.replace("expert/"						,							"nested fstring"			)}'			)

            s_dict.pop(lowercase			)

    return s_dict


__snake_case             =   {
    """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						(						lowercase  :  Dict						,							lowercase  :  Optional[Any]			)     ->						int:


    """simple docstring"""





    import regex as re

    with open(lowercase						,							"r"			) as f:
        snake_case   :				List[str]				 =   f.read()

    snake_case   :				Tuple				 =   re.findall(R"(.*) = ([0-9.]*)"						,							lowercase			)
    snake_case   :				Any				 =   {}
    for param, value in regex_match:
        if param in GIN_TO_CONFIG_MAPPING and value != "":
            snake_case   :				Tuple				 =   float(lowercase			) if "." in value else int(lowercase			)

    snake_case   :				List[str]				 =   re.findall(R"(.*activations) = \(\'(.*)\',\)"						,							lowercase			)[0]
    snake_case   :				List[Any]				 =   str(activation[1]			)

    snake_case   :				Optional[Any]				 =   num_experts
    snake_case   :				List[Any]				 =   SwitchTransformersConfig(**lowercase			)
    return config







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  Tuple						,							lowercase  :  Union[str, Any]=None						,							lowercase  :  Any="./"						,							lowercase  :  int=8			)     ->						Dict:


    """simple docstring"""





    print(F'Loading flax weights from : {flax_checkpoint_path}'			)
    snake_case   :				Union[str, Any]				 =   checkpoints.load_tax_checkpoint(lowercase			)

    if gin_file is not None:
        snake_case   :				List[str]				 =   convert_gin_to_config(lowercase						,							lowercase			)
    else:
        snake_case   :				str				 =   SwitchTransformersConfig.from_pretrained(lowercase			)

    snake_case   :				Any				 =   SwitchTransformersForConditionalGeneration(lowercase			)

    snake_case   :				Optional[Any]				 =   flax_params["target"]
    snake_case   :				Optional[int]				 =   flatten_dict(lowercase						,							sep="/"			)
    snake_case   :				Optional[Any]				 =   rename_keys(lowercase			)
    snake_case   :				List[str]				 =   unflatten_dict(lowercase						,							sep="/"			)

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

    print(F'Save PyTorch model to {pytorch_dump_path}'			)
    pt_model.save_pretrained(lowercase			)


if __name__ == "__main__":
  __snake_case             =   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""")
  __snake_case             =   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,
  )





