"""simple docstring"""




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


_a				:		Any	   =       'http://www.mocksite.com/file1.txt'
_a				:		str	   =       '"text": ["foo", "foo"]'
_a				:		Union[str, Any]	   =       '6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'
class 						__A							:
     _UpperCamelCase							:      List[str]		 =		200
     _UpperCamelCase							:      Dict		 =		{"Content-Length": "100"}
     _UpperCamelCase							:      Optional[int]		 =		{}






     def    __A  (		self    ,					**a__  ):
       return [bytes(a__    ,					"""utf-8"""  )]







def 							SCREAMING_SNAKE_CASE					(      *_lowerCamelCase						:	Optional[int]      ,**_lowerCamelCase						:	Tuple     )	->				List[Any]:
  return MockResponse()





@pytest.mark.parametrize("""urls_type"""      ,[str, list, dict]     )
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Dict      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	List[Any]     )	->				List[Any]:
  import requests

  monkeypatch.setattr(_lowerCamelCase      ,"""request"""      ,_lowerCamelCase     )

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





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





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	List[str]     )	->				List[Any]:
  assert path.endswith(""".jsonl"""     )
  for num_items, line in enumerate(_lowerCamelCase      ,start=1     ):
    _lowerCAmelCase						:	List[Any]        =					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 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Dict     )	->				Optional[int]:
  _lowerCAmelCase						:	Optional[int]        =					request.getfixturevalue(_lowerCamelCase     )
  _lowerCAmelCase						:	List[str]        =					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 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	str     )	->				List[str]:
  _lowerCAmelCase						:	Any        =					request.getfixturevalue(_lowerCamelCase     )
  _lowerCAmelCase						:	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 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Dict     )	->				List[Any]:
  _lowerCAmelCase						:	List[Any]        =					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





"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	Optional[Any]     )	->				str:
  _lowerCAmelCase						:	str        =					AutoConfig.from_pretrained(_lowerCamelCase     )
  _lowerCAmelCase						:	int        =					FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCamelCase     )
  _lowerCAmelCase						:	Any        =					checkpoints.load_tax_checkpoint(_lowerCamelCase     )

  _lowerCAmelCase						:	Tuple        =					"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

  if config.model_type == "t5":
    _lowerCAmelCase						:	Tuple        =					"""SelfAttention"""
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    _lowerCAmelCase						:	Optional[Any]        =					"""LocalSelfAttention"""
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _lowerCAmelCase						:	Union[str, Any]        =					"""TransientGlobalSelfAttention"""
  else:
    raise ValueError(
        """Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"""
        """ attribute with a value from ['local', 'transient-global]."""     )

  # Encoder
  for layer_index in range(config.num_layers     ):
    _lowerCAmelCase						:	Tuple        =					f"layers_{str(_lowerCamelCase     )}"

    # Self-Attention
    _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
    _lowerCAmelCase						:	str        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
    _lowerCAmelCase						:	str        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
    _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _lowerCAmelCase						:	Dict        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]

    # Layer Normalization
    _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

    if split_mlp_wi:
      _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
      _lowerCAmelCase						:	int        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
    else:
      _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

    _lowerCAmelCase						:	Tuple        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Tuple        =					tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    _lowerCAmelCase						:	Any        =					flax_model.params["""encoder"""]["""block"""][str(_lowerCamelCase     )]["""layer"""]
    _lowerCAmelCase						:	Any        =					tax_attention_key
    _lowerCAmelCase						:	str        =					tax_attention_out
    _lowerCAmelCase						:	Union[str, Any]        =					tax_attention_query
    _lowerCAmelCase						:	Optional[Any]        =					tax_attention_value

    _lowerCAmelCase						:	List[str]        =					tax_attention_layer_norm

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _lowerCAmelCase						:	Any        =					tax_global_layer_norm

    if split_mlp_wi:
      _lowerCAmelCase						:	Dict        =					tax_mlp_wi_a
      _lowerCAmelCase						:	List[Any]        =					tax_mlp_wi_a
    else:
      _lowerCAmelCase						:	List[str]        =					tax_mlp_wi

    _lowerCAmelCase						:	str        =					tax_mlp_wo
    _lowerCAmelCase						:	Optional[Any]        =					tax_mlp_layer_norm

    _lowerCAmelCase						:	Any        =					flax_model_encoder_layer_block

  # Only for layer 0:
  _lowerCAmelCase						:	Union[str, Any]        =					tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  _lowerCAmelCase						:	Optional[Any]        =					tax_encoder_rel_embedding

  # Side/global relative position_bias + layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
    _lowerCAmelCase						:	Optional[int]        =					tax_encoder_global_rel_embedding

  # Assigning
  _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  _lowerCAmelCase						:	Any        =					tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers     ):
    _lowerCAmelCase						:	Optional[int]        =					f"layers_{str(_lowerCamelCase     )}"

    # Self-Attention
    _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
    _lowerCAmelCase						:	int        =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
    _lowerCAmelCase						:	List[Any]        =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
    _lowerCAmelCase						:	str        =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Optional[Any]        =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
        """scale"""
    ]

    # Encoder-Decoder-Attention
    _lowerCAmelCase						:	List[Any]        =					tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]
    _lowerCAmelCase						:	List[str]        =					tax_enc_dec_attention_module["""key"""]["""kernel"""]
    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_module["""out"""]["""kernel"""]
    _lowerCAmelCase						:	List[str]        =					tax_enc_dec_attention_module["""query"""]["""kernel"""]
    _lowerCAmelCase						:	Dict        =					tax_enc_dec_attention_module["""value"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

    # MLP
    if split_mlp_wi:
      _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
      _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
    else:
      _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

    _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    _lowerCAmelCase						:	str        =					flax_model.params["""decoder"""]["""block"""][str(_lowerCamelCase     )]["""layer"""]
    _lowerCAmelCase						:	int        =					tax_attention_key
    _lowerCAmelCase						:	List[str]        =					tax_attention_out
    _lowerCAmelCase						:	Optional[Any]        =					tax_attention_query
    _lowerCAmelCase						:	Dict        =					tax_attention_value

    _lowerCAmelCase						:	str        =					tax_pre_attention_layer_norm

    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_key
    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_out
    _lowerCAmelCase						:	Tuple        =					tax_enc_dec_attention_query
    _lowerCAmelCase						:	Any        =					tax_enc_dec_attention_value

    _lowerCAmelCase						:	Dict        =					tax_cross_layer_norm

    if split_mlp_wi:
      _lowerCAmelCase						:	Dict        =					tax_mlp_wi_a
      _lowerCAmelCase						:	int        =					tax_mlp_wi_a
    else:
      _lowerCAmelCase						:	Optional[int]        =					tax_mlp_wi

    _lowerCAmelCase						:	Dict        =					tax_mlp_wo

    _lowerCAmelCase						:	List[Any]        =					txa_mlp_layer_norm

    _lowerCAmelCase						:	Optional[Any]        =					flax_model_decoder_layer_block

  # Decoder Normalization
  _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
  _lowerCAmelCase						:	List[str]        =					txa_decoder_norm

  # Only for layer 0:
  _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  _lowerCAmelCase						:	Union[str, Any]        =					tax_decoder_rel_embedding

  # Token Embeddings
  _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""token_embedder"""]["""embedding"""]
  _lowerCAmelCase						:	Optional[int]        =					txa_token_embeddings

  # LM Head (only in v1.1 and LongT5 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    _lowerCAmelCase						:	Tuple        =					tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

  flax_model.save_pretrained(_lowerCamelCase     )
  print("""T5X Model was sucessfully converted!"""     )


if __name__ == "__main__":
     _a				:		Any	   =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
     )
     parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
     parser.add_argument(
         '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
     )
     _a				:		List[str]	   =       parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




