



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        lowercase_							(A : List[str]		,					A : Optional[Any]		,					A : str		,					A : Tuple="attention"      ):
       snake_case__							:	int   	=	params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
       snake_case__							:	int   	=	params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
       snake_case__							:	List[Any]   	=	params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
       snake_case__							:	str   	=	params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
       return k, o, q, v
def        lowercase_							(A : Any		,					A : Dict		,					A : Any		,					A : List[str]=False      ):
       if split_mlp_wi:
              snake_case__							:	Optional[Any]   	=	params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
              snake_case__							:	Optional[int]   	=	params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
              snake_case__							:	Union[str, Any]   	=	(wi_a, wi_a)
       else:
              snake_case__							:	Tuple   	=	params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

       snake_case__							:	str   	=	params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
       return wi, wo
def        lowercase_							(A : List[Any]		,					A : Tuple		,					A : Union[str, Any]		,					A : Any      ):
       return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def        lowercase_							(A : List[Any]		,					*, A : Union[str, Any]		,					A : Union[str, Any]      ):
       snake_case__							:	List[str]   	=	traverse_util.flatten_dict(variables['target']      )
       snake_case__							:	Optional[int]   	=	{'/'.join(_UpperCAmelCase      ): v for k, v in old.items()}

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

       snake_case__							:	Optional[Any]   	=	collections.OrderedDict()

       # Shared embeddings.
       snake_case__							:	Optional[Any]   	=	old['token_embedder/embedding']

       # Encoder.
       for i in range(_UpperCAmelCase      ):
              # Block i, layer 0 (Self Attention).
              snake_case__							:	str   	=	tax_layer_norm_lookup(_UpperCAmelCase		,					_UpperCAmelCase		,					'encoder'		,					'pre_attention_layer_norm'      )
              snake_case__							:	int   	=	tax_attention_lookup(_UpperCAmelCase		,					_UpperCAmelCase		,					'encoder'		,					'attention'      )
              snake_case__							:	Dict   	=	layer_norm
              snake_case__							:	List[Any]   	=	k.T
              snake_case__							:	Dict   	=	o.T
              snake_case__							:	Optional[int]   	=	q.T
              snake_case__							:	Union[str, Any]   	=	v.T

              # Block i, layer 1 (MLP).
              snake_case__							:	Dict   	=	tax_layer_norm_lookup(_UpperCAmelCase		,					_UpperCAmelCase		,					'encoder'		,					'pre_mlp_layer_norm'      )
              snake_case__							:	Any   	=	tax_mlp_lookup(_UpperCAmelCase		,					_UpperCAmelCase		,					'encoder'		,					_UpperCAmelCase      )
              snake_case__							:	List[Any]   	=	layer_norm
              if split_mlp_wi:
                     snake_case__							:	str   	=	wi[0].T
                     snake_case__							:	Dict   	=	wi[1].T
              else:
                     snake_case__							:	Optional[Any]   	=	wi.T
              snake_case__							:	Tuple   	=	wo.T

       snake_case__							:	int   	=	old[
           'encoder/relpos_bias/rel_embedding'
       ].T
       snake_case__							:	int   	=	old['encoder/encoder_norm/scale']

       if not is_encoder_only:
              # Decoder.
              for i in range(_UpperCAmelCase      ):
                     # Block i, layer 0 (Self Attention).
                     snake_case__							:	Optional[int]   	=	tax_layer_norm_lookup(_UpperCAmelCase		,					_UpperCAmelCase		,					'decoder'		,					'pre_self_attention_layer_norm'      )
                     snake_case__							:	Dict   	=	tax_attention_lookup(_UpperCAmelCase		,					_UpperCAmelCase		,					'decoder'		,					'self_attention'      )
                     snake_case__							:	List[Any]   	=	layer_norm
                     snake_case__							:	List[Any]   	=	k.T
                     snake_case__							:	Union[str, Any]   	=	o.T
                     snake_case__							:	str   	=	q.T
                     snake_case__							:	int   	=	v.T

                     # Block i, layer 1 (Cross Attention).
                     snake_case__							:	str   	=	tax_layer_norm_lookup(_UpperCAmelCase		,					_UpperCAmelCase		,					'decoder'		,					'pre_cross_attention_layer_norm'      )
                     snake_case__							:	Optional[Any]   	=	tax_attention_lookup(_UpperCAmelCase		,					_UpperCAmelCase		,					'decoder'		,					'encoder_decoder_attention'      )
                     snake_case__							:	str   	=	layer_norm
                     snake_case__							:	List[str]   	=	k.T
                     snake_case__							:	Tuple   	=	o.T
                     snake_case__							:	List[Any]   	=	q.T
                     snake_case__							:	int   	=	v.T

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

              snake_case__							:	int   	=	old['decoder/decoder_norm/scale']
              snake_case__							:	Optional[int]   	=	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:
                     snake_case__							:	Dict   	=	old['decoder/logits_dense/kernel'].T

       return new
def        lowercase_							(A : Tuple		,					A : List[Any]      ):
       snake_case__							:	int   	=	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:
              snake_case__							:	Optional[int]   	=	state_dict['shared.weight']

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     snake_case__							:	Union[str, Any]   	=	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.'      )
                     snake_case__							:	Optional[int]   	=	state_dict['shared.weight']

       return state_dict
def        lowercase_							(A : List[Any]		,					A : Tuple		,					A : Any		,					A : Any      ):
       snake_case__							:	Any   	=	checkpoints.load_tax_checkpoint(_UpperCAmelCase      )
       snake_case__							:	Dict   	=	convert_tax_to_pytorch(_UpperCAmelCase		,					num_layers=config.num_layers		,					is_encoder_only=_UpperCAmelCase      )
       snake_case__							:	Tuple   	=	make_state_dict(_UpperCAmelCase		,					_UpperCAmelCase      )
       model.load_state_dict(_UpperCAmelCase		,					strict=_UpperCAmelCase      )
def        lowercase_							(A : List[Any]		,					A : Tuple		,					A : Optional[Any]		,					A : Optional[Any] = False      ):
       snake_case__							:	Optional[int]   	=	TaConfig.from_json_file(_UpperCAmelCase      )
       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:
              snake_case__							:	List[str]   	=	TaEncoderModel(_UpperCAmelCase      )
       else:
              snake_case__							:	Tuple   	=	TaForConditionalGeneration(_UpperCAmelCase      )

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

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

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


if __name__ == "__main__":
 a_   :List[Any]        =      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
 )
 a_   :Tuple        =      parser.parse_args()
 convert_tax_checkpoint_to_pytorch(
     args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
 )













# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase=None )			->     Tuple:
    if subparsers is not None:
        lowerCamelCase__    :      Any					 =		subparsers.add_parser('test' )
    else:
        lowerCamelCase__    :      int					 =		argparse.ArgumentParser('Accelerate test command' )

    parser.add_argument(
        '--config_file'						,	default=_UpperCAmelCase						,	help=(
            'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
            'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
            'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
            'with \'huggingface\'.'
        )						,	)

    if subparsers is not None:
        parser.set_defaults(func=_UpperCAmelCase )
    return parser


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      Tuple					 =		os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['test_utils', 'scripts', 'test_script.py'] )

    if args.config_file is None:
        lowerCamelCase__    :      List[str]					 =		script_name
    else:
        lowerCamelCase__    :      List[Any]					 =		F"""--config_file={args.config_file} {script_name}"""

    lowerCamelCase__    :      str					 =		['accelerate-launch'] + test_args.split()
    lowerCamelCase__    :      Dict					 =		execute_subprocess_async(_UpperCAmelCase						,	env=os.environ.copy() )
    if result.returncode == 0:
        print('Test is a success! You are ready for your distributed training!' )


def        SCREAMING_SNAKE_CASE      (       )			->     Any:
    lowerCamelCase__    :      Any					 =		test_command_parser()
    lowerCamelCase__    :      List[Any]					 =		parser.parse_args()
    test_command(_UpperCAmelCase )


if __name__ == "__main__":
    main()





