




'''simple docstring'''





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     (       _UpperCamelCase:   Dict ,						_UpperCamelCase:   int ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   List[str]="attention"   )						->			List[str]:

     """simple docstring"""




     _snake_case							=				params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
     _snake_case							=				params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
     _snake_case							=				params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
     _snake_case							=				params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
     return k, o, q, v





def      __a     (       _UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   List[str] ,						_UpperCamelCase:   Tuple ,						_UpperCamelCase:   Tuple=False   )						->			int:

     """simple docstring"""




     if split_mlp_wi:
          _snake_case							=				params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
          _snake_case							=				params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
          _snake_case							=				(wi_a, wi_a)
     else:
          _snake_case							=				params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

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





def      __a     (       _UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Any ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Any   )						->			Tuple:

     """simple docstring"""




     return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]





def      __a     (       _UpperCamelCase:   dict ,						*, _UpperCamelCase:   int ,						_UpperCamelCase:   bool   )						->			List[str]:

     """simple docstring"""




     _snake_case							=				traverse_util.flatten_dict(variables["target"]   )
     _snake_case							=				{"/".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							=				"encoder/layers_0/mlp/wi_0/kernel" in old
     print("Split MLP:" ,						_UpperCamelCase   )

     _snake_case							=				collections.OrderedDict()

     # Shared embeddings.
     _snake_case							=				old["token_embedder/embedding"]

     # Encoder.
     for i in range(_UpperCamelCase   ):
          # Block i, layer 0 (Self Attention).
          _snake_case							=				tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"encoder" ,						"pre_attention_layer_norm"   )
          _snake_case							, _snake_case							, _snake_case							, _snake_case							=				tax_attention_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"encoder" ,						"attention"   )
          _snake_case							=				layer_norm
          _snake_case							=				k.T
          _snake_case							=				o.T
          _snake_case							=				q.T
          _snake_case							=				v.T

          # Block i, layer 1 (MLP).
          _snake_case							=				tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"encoder" ,						"pre_mlp_layer_norm"   )
          _snake_case							, _snake_case							=				tax_mlp_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"encoder" ,						_UpperCamelCase   )
          _snake_case							=				layer_norm
          if split_mlp_wi:
               _snake_case							=				wi[0].T
               _snake_case							=				wi[1].T
          else:
               _snake_case							=				wi.T
          _snake_case							=				wo.T

     _snake_case							=				old[
         "encoder/relpos_bias/rel_embedding"
     ].T
     _snake_case							=				old["encoder/encoder_norm/scale"]

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

               # Block i, layer 1 (Cross Attention).
               _snake_case							=				tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"decoder" ,						"pre_cross_attention_layer_norm"   )
               _snake_case							, _snake_case							, _snake_case							, _snake_case							=				tax_attention_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"decoder" ,						"encoder_decoder_attention"   )
               _snake_case							=				layer_norm
               _snake_case							=				k.T
               _snake_case							=				o.T
               _snake_case							=				q.T
               _snake_case							=				v.T

               # Block i, layer 2 (MLP).
               _snake_case							=				tax_layer_norm_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"decoder" ,						"pre_mlp_layer_norm"   )
               _snake_case							, _snake_case							=				tax_mlp_lookup(_UpperCamelCase ,						_UpperCamelCase ,						"decoder" ,						_UpperCamelCase   )
               _snake_case							=				layer_norm
               if split_mlp_wi:
                    _snake_case							=				wi[0].T
                    _snake_case							=				wi[1].T
               else:
                    _snake_case							=				wi.T
               _snake_case							=				wo.T

          _snake_case							=				old["decoder/decoder_norm/scale"]
          _snake_case							=				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							=				old["decoder/logits_dense/kernel"].T

     return new





def      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   bool   )						->			List[str]:

     """simple docstring"""




     _snake_case							=				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							=				state_dict["shared.weight"]

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               _snake_case							=				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							=				state_dict["shared.weight"]

     return state_dict





def      __a     (       _UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Optional[Any] ,						_UpperCamelCase:   Optional[Any] ,						_UpperCamelCase:   Tuple   )						->			Tuple:

     """simple docstring"""




     _snake_case							=				checkpoints.load_tax_checkpoint(_UpperCamelCase   )
     _snake_case							=				convert_tax_to_pytorch(_UpperCamelCase ,						num_layers=config.num_layers ,						is_encoder_only=_UpperCamelCase   )
     _snake_case							=				make_state_dict(_UpperCamelCase ,						_UpperCamelCase   )
     model.load_state_dict(_UpperCamelCase ,						strict=_UpperCamelCase   )





def      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   Dict ,						_UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   bool = False   )						->			Any:

     """simple docstring"""




     _snake_case							=				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							=				TaEncoderModel(_UpperCamelCase   )
     else:
          _snake_case							=				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__":
   UpperCamelCase_						:      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
   )
   UpperCamelCase_						:      int       =						parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
   )








'''simple docstring'''





# 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,
)


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

UpperCamelCase_						:      List[Any]       =						'''▁'''
UpperCamelCase_						:      Tuple       =						SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

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





def      __a     (       _UpperCamelCase:   Optional[Any]   )						->			int:

     """simple docstring"""




     if version.parse(_UpperCamelCase   ) < version.parse(_UpperCamelCase   ):
          if "dev" in min_version:
               _snake_case							=				(
                   "This example requires a source install from HuggingFace Transformers (see "
                   "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
               )
          else:
               _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."   )


