
'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			str   	=  TaConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)
    snake_case_       :			Any   	=  FlaxTaForConditionalGeneration(config=__SCREAMING_SNAKE_CASE				)
    snake_case_       :			Any   	=  checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE				)

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

    # Encoder
    for layer_index in range(config.num_layers				):
        snake_case_       :			Union[str, Any]   	=  f'layers_{str(__SCREAMING_SNAKE_CASE				)}'

        # Self-Attention
        snake_case_       :			str   	=  tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
        snake_case_       :			Optional[int]   	=  tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
        snake_case_       :			Optional[Any]   	=  tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
        snake_case_       :			Tuple   	=  tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

        # Layer Normalization
        snake_case_       :			str   	=  tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

        if split_mlp_wi:
            snake_case_       :			Tuple   	=  tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
            snake_case_       :			Union[str, Any]   	=  tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
        else:
            snake_case_       :			List[str]   	=  tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

        snake_case_       :			str   	=  tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

        # Layer Normalization
        snake_case_       :			Dict   	=  tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

        # Assigning
        snake_case_       :			List[Any]   	=  tax_attention_key
        snake_case_       :			int   	=  tax_attention_out
        snake_case_       :			int   	=  tax_attention_query
        snake_case_       :			Union[str, Any]   	=  tax_attention_value

        snake_case_       :			List[str]   	=  tax_attention_layer_norm

        if split_mlp_wi:
            snake_case_       :			Dict   	=  tax_mlp_wi_a
            snake_case_       :			int   	=  tax_mlp_wi_a
        else:
            snake_case_       :			Any   	=  tax_mlp_wi

        snake_case_       :			Any   	=  tax_mlp_wo
        snake_case_       :			Union[str, Any]   	=  tax_mlp_layer_norm

    # Only for layer 0:
    snake_case_       :			Any   	=  tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
    snake_case_       :			Tuple   	=  tax_encoder_rel_embedding

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

    # Decoder
    for layer_index in range(config.num_decoder_layers				):
        snake_case_       :			Optional[int]   	=  f'layers_{str(__SCREAMING_SNAKE_CASE				)}'

        # Self-Attention
        snake_case_       :			Optional[Any]   	=  tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
        snake_case_       :			Union[str, Any]   	=  tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
        snake_case_       :			Optional[Any]   	=  tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
        snake_case_       :			List[Any]   	=  tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

        # Layer Normalization
        snake_case_       :			Dict   	=  tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
            "scale"
        ]

        # Encoder-Decoder-Attention
        snake_case_       :			Optional[int]   	=  tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
            "kernel"
        ]
        snake_case_       :			Dict   	=  tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
            "kernel"
        ]
        snake_case_       :			Union[str, Any]   	=  tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
            "kernel"
        ]
        snake_case_       :			List[Any]   	=  tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
            "kernel"
        ]

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

        # MLP
        if split_mlp_wi:
            snake_case_       :			Union[str, Any]   	=  tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
            snake_case_       :			Union[str, Any]   	=  tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
        else:
            snake_case_       :			int   	=  tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

        snake_case_       :			Union[str, Any]   	=  tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

        # Layer Normalization
        snake_case_       :			Dict   	=  tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

        # Assigning
        snake_case_       :			str   	=  tax_attention_key
        snake_case_       :			int   	=  tax_attention_out
        snake_case_       :			List[Any]   	=  tax_attention_query
        snake_case_       :			Optional[Any]   	=  tax_attention_value

        snake_case_       :			str   	=  tax_pre_attention_layer_norm

        snake_case_       :			Union[str, Any]   	=  tax_enc_dec_attention_key
        snake_case_       :			Union[str, Any]   	=  tax_enc_dec_attention_out
        snake_case_       :			Dict   	=  tax_enc_dec_attention_query
        snake_case_       :			int   	=  tax_enc_dec_attention_value

        snake_case_       :			str   	=  tax_cross_layer_norm

        if split_mlp_wi:
            snake_case_       :			Tuple   	=  tax_mlp_wi_a
            snake_case_       :			Tuple   	=  tax_mlp_wi_a
        else:
            snake_case_       :			int   	=  tax_mlp_wi

        snake_case_       :			str   	=  tax_mlp_wo

        snake_case_       :			Union[str, Any]   	=  txa_mlp_layer_norm

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

    # Only for layer 0:
    snake_case_       :			Optional[int]   	=  tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
    snake_case_       :			Tuple   	=  tax_decoder_rel_embedding

    # Token Embeddings
    snake_case_       :			Any   	=  tax_model["target"]["token_embedder"]["embedding"]
    snake_case_       :			str   	=  txa_token_embeddings

    # LM Head (only in v1.1 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        snake_case_       :			List[str]   	=  tax_model["target"]["decoder"]["logits_dense"]["kernel"]

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


if __name__ == "__main__":
  a_      =  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
  )
  parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
  parser.add_argument(
      "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
  )
  a_      =  parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
   from .testing_utils import (
       floats_tensor,
       load_hf_numpy,
       load_image,
       load_numpy,
       load_pt,
       nightly,
       parse_flag_from_env,
       print_tensor_test,
       require_torch_a,
       require_torch_gpu,
       skip_mps,
       slow,
       torch_all_close,
       torch_device,
   )
   from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


UpperCAmelCase			=    get_logger(__name__)






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  Tuple:




      """simple docstring"""

      if version.parse(SCREAMING_SNAKE_CASE					) < version.parse(SCREAMING_SNAKE_CASE					):
            if "dev" in min_version:
                  snake_case_       =      (
                      '''This example requires a source install from HuggingFace diffusers (see '''
                      '''`https://huggingface.co/docs/diffusers/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(SCREAMING_SNAKE_CASE					)