




'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A							:      Union[str, Any] 	=       logging.get_logger(__name__)

A							:      int 	=       {
    """facebook/convnext-tiny-224""": """https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json""",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}






class        SCREAMING_SNAKE_CASE(				__A   ,      __A     ):
       snake_case_ :     Union[str, Any]    =					"""convnext"""

       def __init__(    self					,		lowerCamelCase__=3					,		lowerCamelCase__=4					,		lowerCamelCase__=4					,		lowerCamelCase__=None					,		lowerCamelCase__=None					,		lowerCamelCase__="gelu"					,		lowerCamelCase__=0.02					,		lowerCamelCase__=1E-1_2					,		lowerCamelCase__=1E-6					,		lowerCamelCase__=0.0					,		lowerCamelCase__=224					,		lowerCamelCase__=None					,		lowerCamelCase__=None					,		**lowerCamelCase__					,		)    ->					Union[str, Any]:

           """simple docstring"""



           super().__init__(**lowerCamelCase__     )

           __lowercase							=						num_channels
           __lowercase							=						patch_size
           __lowercase							=						num_stages
           __lowercase							=						[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
           __lowercase							=						[3, 3, 9, 3] if depths is None else depths
           __lowercase							=						hidden_act
           __lowercase							=						initializer_range
           __lowercase							=						layer_norm_eps
           __lowercase							=						layer_scale_init_value
           __lowercase							=						drop_path_rate
           __lowercase							=						image_size
           __lowercase							=						["""stem"""] + [F'stage{idx}' for idx in range(1					,		len(self.depths     ) + 1     )]
           __lowercase				,__lowercase							=						get_aligned_output_features_output_indices(
               out_features=lowerCamelCase__					,		out_indices=lowerCamelCase__					,		stage_names=self.stage_names     )






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Any    =					version.parse("""1.11"""     )

       @property
       def     snake_case__			(    self     )    ->					Mapping[str, Mapping[int, str]]:

           """simple docstring"""



           return OrderedDict(
               [
                   ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ]     )




       @property
       def     snake_case__			(    self     )    ->					float:

           """simple docstring"""



           return 1E-5










'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def 				snake_case_  (   a__		:	Optional[int]    ,a__		:	str    ,a__		:	int			):



    """simple docstring"""




    __lowercase							=						TaConfig.from_pretrained(a__			)
    __lowercase							=						FlaxTaForConditionalGeneration(config=a__			)
    __lowercase							=						checkpoints.load_tax_checkpoint(a__			)

    __lowercase							=						"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

    # Encoder
    for layer_index in range(config.num_layers			):
        __lowercase							=						f'layers_{str(a__			)}'

        # Self-Attention
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

        if split_mlp_wi:
            __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
            __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
        else:
            __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

        # Assigning
        __lowercase							=						tax_attention_key
        __lowercase							=						tax_attention_out
        __lowercase							=						tax_attention_query
        __lowercase							=						tax_attention_value

        __lowercase							=						tax_attention_layer_norm

        if split_mlp_wi:
            __lowercase							=						tax_mlp_wi_a
            __lowercase							=						tax_mlp_wi_a
        else:
            __lowercase							=						tax_mlp_wi

        __lowercase							=						tax_mlp_wo
        __lowercase							=						tax_mlp_layer_norm

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

    # Assigning
    __lowercase							=						tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
    __lowercase							=						tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_decoder_layers			):
        __lowercase							=						f'layers_{str(a__			)}'

        # Self-Attention
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
            """scale"""
        ]

        # Encoder-Decoder-Attention
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
            """kernel"""
        ]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
            """kernel"""
        ]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
            """kernel"""
        ]
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
            """kernel"""
        ]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

        # MLP
        if split_mlp_wi:
            __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
            __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
        else:
            __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

        # Layer Normalization
        __lowercase							=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

        # Assigning
        __lowercase							=						tax_attention_key
        __lowercase							=						tax_attention_out
        __lowercase							=						tax_attention_query
        __lowercase							=						tax_attention_value

        __lowercase							=						tax_pre_attention_layer_norm

        __lowercase							=						tax_enc_dec_attention_key
        __lowercase							=						tax_enc_dec_attention_out
        __lowercase							=						tax_enc_dec_attention_query
        __lowercase							=						tax_enc_dec_attention_value

        __lowercase							=						tax_cross_layer_norm

        if split_mlp_wi:
            __lowercase							=						tax_mlp_wi_a
            __lowercase							=						tax_mlp_wi_a
        else:
            __lowercase							=						tax_mlp_wi

        __lowercase							=						tax_mlp_wo

        __lowercase							=						txa_mlp_layer_norm

    # Decoder Normalization
    __lowercase							=						tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
    __lowercase							=						txa_decoder_norm

    # Only for layer 0:
    __lowercase							=						tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
    __lowercase							=						tax_decoder_rel_embedding

    # Token Embeddings
    __lowercase							=						tax_model["""target"""]["""token_embedder"""]["""embedding"""]
    __lowercase							=						txa_token_embeddings

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

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


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




