
"""simple docstring"""


import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


SCREAMING_SNAKE_CASE			  =  '''\
    Text data.
    Second line of data.'''

SCREAMING_SNAKE_CASE			  =  '''file'''



@pytest.fixture(scope="session"					)
def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       UpperCamelCase       			=		tmp_path_factory.mktemp("data"					) / (FILE_PATH + ".zstd")
       UpperCamelCase       			=		bytes(__A		,   "utf-8"					)
       with zstd.open(__A		,   "wb"					) as f:
              f.write(__A					)
       return path



@pytest.fixture
def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Dict:







       """simple docstring"""
       with open(os.path.join(tmpfs.local_root_dir		,   __A					)		,   "w"					) as f:
              f.write(__A					)
       return FILE_PATH



@pytest.mark.parametrize("compression_format"		,   ["gzip", "xz", "zstd"]					)
def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Union[str, Any]:







       """simple docstring"""
       UpperCamelCase       			=		{"gzip": gz_file, "xz": xz_file, "zstd": zstd_path}
       UpperCamelCase       			=		input_paths[compression_format]
       UpperCamelCase       			=		tmp_path / "cache"
       UpperCamelCase       			=		DownloadConfig(cache_dir=__A		,   extract_compressed_file=__A					)
       UpperCamelCase       			=		cached_path(__A		,   download_config=__A					)
       with open(__A					) as f:
              UpperCamelCase       			=		f.read()
       with open(__A					) as f:
              UpperCamelCase       			=		f.read()
       assert extracted_file_content == expected_file_content



@pytest.mark.parametrize("default_extracted"		,   [True, False]					)
@pytest.mark.parametrize("default_cache_dir"		,   [True, False]					)
def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				int:







       """simple docstring"""
       UpperCamelCase       			=		"custom_cache"
       UpperCamelCase       			=		"custom_extracted_dir"
       UpperCamelCase       			=		tmp_path / "custom_extracted_path"
       if default_extracted:
              UpperCamelCase       			=		("downloads" if default_cache_dir else custom_cache_dir, "extracted")
       else:
              monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_DIR"		,   __A					)
              monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH"		,   str(__A					)					)
              UpperCamelCase       			=		custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

       UpperCamelCase       			=		xz_file
       UpperCamelCase       			=		(
           DownloadConfig(extract_compressed_file=__A					)
           if default_cache_dir
           else DownloadConfig(cache_dir=tmp_path / custom_cache_dir		,   extract_compressed_file=__A					)
       )
       UpperCamelCase       			=		cached_path(__A		,   download_config=__A					)
       assert Path(__A					).parent.parts[-2:] == expected



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       # absolute path
       UpperCamelCase       			=		str(Path(__A					).resolve()					)
       assert cached_path(__A					) == text_file
       # relative path
       UpperCamelCase       			=		str(Path(__A					).resolve().relative_to(Path(os.getcwd()					)					)					)
       assert cached_path(__A					) == text_file



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Any:







       """simple docstring"""
       # absolute path
       UpperCamelCase       			=		str(tmp_path.resolve() / "__missing_file__.txt"					)
       with pytest.raises(__A					):
              cached_path(__A					)
       # relative path
       UpperCamelCase       			=		"./__missing_file__.txt"
       with pytest.raises(__A					):
              cached_path(__A					)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		get_from_cache(F"tmp://{tmpfs_file}"					)
       with open(__A					) as f:
              UpperCamelCase       			=		f.read()
       assert output_file_content == FILE_CONTENT



@patch("datasets.config.HF_DATASETS_OFFLINE"		,   __A					)
def 						lowerCamelCase__      (				)->				str:







       """simple docstring"""
       with pytest.raises(__A					):
              cached_path("https://huggingface.co"					)



@patch("datasets.config.HF_DATASETS_OFFLINE"		,   __A					)
def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Optional[Any]:







       """simple docstring"""
       UpperCamelCase       			=		tmp_path_factory.mktemp("data"					) / "file.html"
       with pytest.raises(__A					):
              http_get("https://huggingface.co"		,   temp_file=__A					)
       with pytest.raises(__A					):
              http_head("https://huggingface.co"					)



@patch("datasets.config.HF_DATASETS_OFFLINE"		,   __A					)
def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       UpperCamelCase       			=		tmp_path_factory.mktemp("data"					) / "file.html"
       with pytest.raises(__A					):
              ftp_get("ftp://huggingface.co"		,   temp_file=__A					)
       with pytest.raises(__A					):
              ftp_head("ftp://huggingface.co"					)



@patch("datasets.config.HF_DATASETS_OFFLINE"		,   __A					)
def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       UpperCamelCase       			=		tmp_path_factory.mktemp("data"					) / "file.html"
       with pytest.raises(__A					):
              fsspec_get("s3://huggingface.co"		,   temp_file=__A					)
       with pytest.raises(__A					):
              fsspec_head("s3://huggingface.co"					)








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      A__					(			__A  :						List[str]     ,				__A  :						Optional[Any]     ,				__A  :						Union[str, Any]     ,				__A  :						int="attention"			)					->str:
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v





def      A__					(			__A  :						Optional[int]     ,				__A  :						List[str]     ,				__A  :						Any     ,				__A  :						Tuple=False			)					->Any:
      if split_mlp_wi:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            __A    =(wi_a, wi_a)
      else:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

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





def      A__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						Any     ,				__A  :						Optional[Any]			)					->str:
      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']





def      A__					(			__A  :						dict     ,				*, __A  :						int     ,				__A  :						bool			)					->Optional[Any]:
      __A    =traverse_util.flatten_dict(variables['''target''']			)
      __A    ={'''/'''.join(__A			): v for k, v in old.items()}

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

      __A    =collections.OrderedDict()

      # Shared embeddings.
      __A    =old['''token_embedder/embedding''']

      # Encoder.
      for i in range(__A			):
            # Block i, layer 0 (Self Attention).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_attention_layer_norm'''			)
            __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''attention'''			)
            __A    =layer_norm
            __A    =k.T
            __A    =o.T
            __A    =q.T
            __A    =v.T

            # Block i, layer 1 (MLP).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_mlp_layer_norm'''			)
            __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''encoder'''     ,				__A			)
            __A    =layer_norm
            if split_mlp_wi:
                  __A    =wi[0].T
                  __A    =wi[1].T
            else:
                  __A    =wi.T
            __A    =wo.T

      __A    =old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      __A    =old['''encoder/encoder_norm/scale''']

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

                  # Block i, layer 1 (Cross Attention).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_cross_attention_layer_norm'''			)
                  __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''encoder_decoder_attention'''			)
                  __A    =layer_norm
                  __A    =k.T
                  __A    =o.T
                  __A    =q.T
                  __A    =v.T

                  # Block i, layer 2 (MLP).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_mlp_layer_norm'''			)
                  __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''decoder'''     ,				__A			)
                  __A    =layer_norm
                  if split_mlp_wi:
                        __A    =wi[0].T
                        __A    =wi[1].T
                  else:
                        __A    =wi.T
                  __A    =wo.T

            __A    =old['''decoder/decoder_norm/scale''']
            __A    =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:
                  __A    =old['''decoder/logits_dense/kernel'''].T

      return new





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						bool			)					->Any:
      __A    =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:
            __A    =state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  __A    =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.'''			)
                  __A    =state_dict['''shared.weight''']

      return state_dict





def      A__					(			__A  :						str     ,				__A  :						Optional[int]     ,				__A  :						int     ,				__A  :						Optional[Any]			)					->Tuple:
      __A    =checkpoints.load_tax_checkpoint(__A			)
      __A    =convert_tax_to_pytorch(__A     ,				num_layers=config.num_layers     ,				is_encoder_only=__A			)
      __A    =make_state_dict(__A     ,				__A			)
      model.load_state_dict(__A     ,				strict=__A			)





def      A__					(			__A  :						List[str]     ,				__A  :						str     ,				__A  :						str     ,				__A  :						bool = False			)					->List[str]:
      __A    =TaConfig.from_json_file(__A			)
      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:
            __A    =TaEncoderModel(__A			)
      else:
            __A    =TaForConditionalGeneration(__A			)

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

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

      # Verify that we can load the checkpoint.
      model.from_pretrained(__A			)
      print('''Done'''			)


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      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      :					int					    =      parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
   )


