



'''simple docstring'''






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						str  )      ->   List[Any]:
							UpperCAmelCase :       List[str] 							=		AutoConfig.from_pretrained(_lowerCAmelCase  )
							UpperCAmelCase :       Tuple 							=		FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCAmelCase  )
							UpperCAmelCase :       Union[str, Any] 							=		checkpoints.load_tax_checkpoint(_lowerCAmelCase  )

							UpperCAmelCase :       Dict 							=		'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

							if config.model_type == "t5":
														UpperCAmelCase :       str 							=		'''SelfAttention'''
							if config.model_type == "longt5" and config.encoder_attention_type == "local":
														UpperCAmelCase :       List[Any] 							=		'''LocalSelfAttention'''
							elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														UpperCAmelCase :       Optional[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  ):
														UpperCAmelCase :       Dict 							=		f"""layers_{str(_lowerCAmelCase  )}"""

														# Self-Attention
														UpperCAmelCase :       Any 							=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
														UpperCAmelCase :       Optional[Any] 							=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
														UpperCAmelCase :       List[str] 							=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
														UpperCAmelCase :       Dict 							=		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":
																					UpperCAmelCase :       List[Any] 							=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

														# Layer Normalization
														UpperCAmelCase :       Optional[int] 							=		tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

														if split_mlp_wi:
																					UpperCAmelCase :       Optional[Any] 							=		tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																					UpperCAmelCase :       int 							=		tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
														else:
																					UpperCAmelCase :       List[Any] 							=		tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

														UpperCAmelCase :       List[str] 							=		tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

														# Layer Normalization
														UpperCAmelCase :       Tuple 							=		tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

														# Assigning
														UpperCAmelCase :       List[Any] 							=		flax_model.params['''encoder''']['''block'''][str(_lowerCAmelCase  )]['''layer''']
														UpperCAmelCase :       Union[str, Any] 							=		tax_attention_key
														UpperCAmelCase :       int 							=		tax_attention_out
														UpperCAmelCase :       Union[str, Any] 							=		tax_attention_query
														UpperCAmelCase :       Any 							=		tax_attention_value

														UpperCAmelCase :       str 							=		tax_attention_layer_norm

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

														if split_mlp_wi:
																					UpperCAmelCase :       str 							=		tax_mlp_wi_a
																					UpperCAmelCase :       int 							=		tax_mlp_wi_a
														else:
																					UpperCAmelCase :       List[str] 							=		tax_mlp_wi

														UpperCAmelCase :       Any 							=		tax_mlp_wo
														UpperCAmelCase :       int 							=		tax_mlp_layer_norm

														UpperCAmelCase :       List[Any] 							=		flax_model_encoder_layer_block

							# Only for layer 0:
							UpperCAmelCase :       Tuple 							=		tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
							UpperCAmelCase :       Any 							=		tax_encoder_rel_embedding

							# Side/global relative position_bias + layer norm
							if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														UpperCAmelCase :       Tuple 							=		tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
														UpperCAmelCase :       int 							=		tax_encoder_global_rel_embedding

							# Assigning
							UpperCAmelCase :       int 							=		tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
							UpperCAmelCase :       List[Any] 							=		tax_encoder_norm

							# Decoder
							for layer_index in range(config.num_layers  ):
														UpperCAmelCase :       str 							=		f"""layers_{str(_lowerCAmelCase  )}"""

														# Self-Attention
														UpperCAmelCase :       List[Any] 							=		tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
														UpperCAmelCase :       int 							=		tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
														UpperCAmelCase :       Dict 							=		tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
														UpperCAmelCase :       Dict 							=		tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

														# Layer Normalization
														UpperCAmelCase :       Dict 							=		tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
														    '''scale'''
														]

														# Encoder-Decoder-Attention
														UpperCAmelCase :       Optional[int] 							=		tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
														UpperCAmelCase :       List[Any] 							=		tax_enc_dec_attention_module['''key''']['''kernel''']
														UpperCAmelCase :       Any 							=		tax_enc_dec_attention_module['''out''']['''kernel''']
														UpperCAmelCase :       Optional[int] 							=		tax_enc_dec_attention_module['''query''']['''kernel''']
														UpperCAmelCase :       str 							=		tax_enc_dec_attention_module['''value''']['''kernel''']

														# Layer Normalization
														UpperCAmelCase :       Dict 							=		tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

														# MLP
														if split_mlp_wi:
																					UpperCAmelCase :       Dict 							=		tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																					UpperCAmelCase :       Optional[int] 							=		tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
														else:
																					UpperCAmelCase :       Tuple 							=		tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

														UpperCAmelCase :       List[str] 							=		tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

														# Layer Normalization
														UpperCAmelCase :       List[Any] 							=		tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

														# Assigning
														UpperCAmelCase :       List[Any] 							=		flax_model.params['''decoder''']['''block'''][str(_lowerCAmelCase  )]['''layer''']
														UpperCAmelCase :       Optional[int] 							=		tax_attention_key
														UpperCAmelCase :       Optional[int] 							=		tax_attention_out
														UpperCAmelCase :       Union[str, Any] 							=		tax_attention_query
														UpperCAmelCase :       List[str] 							=		tax_attention_value

														UpperCAmelCase :       List[str] 							=		tax_pre_attention_layer_norm

														UpperCAmelCase :       List[str] 							=		tax_enc_dec_attention_key
														UpperCAmelCase :       int 							=		tax_enc_dec_attention_out
														UpperCAmelCase :       Dict 							=		tax_enc_dec_attention_query
														UpperCAmelCase :       List[str] 							=		tax_enc_dec_attention_value

														UpperCAmelCase :       str 							=		tax_cross_layer_norm

														if split_mlp_wi:
																					UpperCAmelCase :       Tuple 							=		tax_mlp_wi_a
																					UpperCAmelCase :       int 							=		tax_mlp_wi_a
														else:
																					UpperCAmelCase :       List[str] 							=		tax_mlp_wi

														UpperCAmelCase :       Union[str, Any] 							=		tax_mlp_wo

														UpperCAmelCase :       str 							=		txa_mlp_layer_norm

														UpperCAmelCase :       str 							=		flax_model_decoder_layer_block

							# Decoder Normalization
							UpperCAmelCase :       List[Any] 							=		tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
							UpperCAmelCase :       Any 							=		txa_decoder_norm

							# Only for layer 0:
							UpperCAmelCase :       str 							=		tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
							UpperCAmelCase :       Optional[int] 							=		tax_decoder_rel_embedding

							# Token Embeddings
							UpperCAmelCase :       Union[str, Any] 							=		tax_model['''target''']['''token_embedder''']['''embedding''']
							UpperCAmelCase :       str 							=		txa_token_embeddings

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

							flax_model.save_pretrained(_lowerCAmelCase  )
							print('''T5X Model was sucessfully converted!'''  )


if __name__ == "__main__":
						UpperCamelCase__:   List[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."
						)
						UpperCamelCase__:   List[Any]								=			parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)











# Copyright 2021 The HuggingFace Inc. 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.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


lowerCamelCase__ :   str					=							'pytorch_model.bin'
lowerCamelCase__ :   int					=							'pytorch_model.bin.index.json'
lowerCamelCase__ :   List[Any]					=							'adapter_config.json'
lowerCamelCase__ :   Optional[int]					=							'adapter_model.bin'
lowerCamelCase__ :   Dict					=							'adapter_model.safetensors'
lowerCamelCase__ :   List[str]					=							'tf_model.h5'
lowerCamelCase__ :   Tuple					=							'tf_model.h5.index.json'
lowerCamelCase__ :   Optional[int]					=							'model.ckpt'
lowerCamelCase__ :   Any					=							'flax_model.msgpack'
lowerCamelCase__ :   List[Any]					=							'flax_model.msgpack.index.json'
lowerCamelCase__ :   int					=							'model.safetensors'
lowerCamelCase__ :   str					=							'model.safetensors.index.json'
lowerCamelCase__ :   Union[str, Any]					=							'config.json'
lowerCamelCase__ :   List[Any]					=							'preprocessor_config.json'
lowerCamelCase__ :   Optional[int]					=							FEATURE_EXTRACTOR_NAME
lowerCamelCase__ :   Any					=							'generation_config.json'
lowerCamelCase__ :   Any					=							'modelcard.json'

lowerCamelCase__ :   List[str]					=							'▁'
lowerCamelCase__ :   Union[str, Any]					=							SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

lowerCamelCase__ :   Optional[int]					=							[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
lowerCamelCase__ :   str					=							[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
lowerCamelCase__ :   Dict					=							[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[int]							)					-> int:
      if version.parse(__UpperCAmelCase							) < version.parse(__UpperCAmelCase							):
            if "dev" in min_version:
                  SCREAMING_SNAKE_CASE_							=			(
                      'This example requires a source install from HuggingFace Transformers (see '
                      '`https://huggingface.co/docs/transformers/installation#install-from-source`),'
                  )
            else:
                  SCREAMING_SNAKE_CASE_							=			f"This example requires a minimum version of {min_version},"
            error_message += f" but the version found is {__version__}.\n"
            raise ImportError(
                error_message
                + 'Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '
                'versions of HuggingFace Transformers.'							)