
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  List[Any]:
    __lowercase						:     List[str]		      =    TaConfig.from_pretrained(__lowerCAmelCase		)
    __lowercase						:     Union[str, Any]		      =    FlaxTaForConditionalGeneration(config=__lowerCAmelCase		)
    __lowercase						:     int		      =    checkpoints.load_tax_checkpoint(__lowerCAmelCase		)

    __lowercase						:     List[str]		      =    '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    # Encoder
    for layer_index in range(config.num_layers		):
        __lowercase						:     Optional[int]		      =    F'layers_{str(__lowerCAmelCase		)}'

        # Self-Attention
        __lowercase						:     Union[str, Any]		      =    tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        __lowercase						:     List[Any]		      =    tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        __lowercase						:     Union[str, Any]		      =    tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        __lowercase						:     int		      =    tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

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

        if split_mlp_wi:
            __lowercase						:     Tuple		      =    tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            __lowercase						:     int		      =    tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            __lowercase						:     List[str]		      =    tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        __lowercase						:     int		      =    tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        __lowercase						:     int		      =    tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

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

        __lowercase						:     Dict		      =    tax_attention_layer_norm

        if split_mlp_wi:
            __lowercase						:     str		      =    tax_mlp_wi_a
            __lowercase						:     Union[str, Any]		      =    tax_mlp_wi_a
        else:
            __lowercase						:     Union[str, Any]		      =    tax_mlp_wi

        __lowercase						:     Optional[Any]		      =    tax_mlp_wo
        __lowercase						:     List[str]		      =    tax_mlp_layer_norm

    # Only for layer 0:
    __lowercase						:     List[str]		      =    tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    __lowercase						:     int		      =    tax_encoder_rel_embedding

    # Assigning
    __lowercase						:     Optional[int]		      =    tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    __lowercase						:     Optional[Any]		      =    tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_decoder_layers		):
        __lowercase						:     List[Any]		      =    F'layers_{str(__lowerCAmelCase		)}'

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

        # Layer Normalization
        __lowercase						:     Optional[int]		      =    tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        __lowercase						:     Dict		      =    tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
            '''kernel'''
        ]
        __lowercase						:     Dict		      =    tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
            '''kernel'''
        ]
        __lowercase						:     List[Any]		      =    tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
            '''kernel'''
        ]
        __lowercase						:     Dict		      =    tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
            '''kernel'''
        ]

        # Layer Normalization
        __lowercase						:     Optional[int]		      =    tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

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

        __lowercase						:     Union[str, Any]		      =    tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        __lowercase						:     Optional[int]		      =    tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        __lowercase						:     Tuple		      =    tax_attention_key
        __lowercase						:     Optional[int]		      =    tax_attention_out
        __lowercase						:     Any		      =    tax_attention_query
        __lowercase						:     int		      =    tax_attention_value

        __lowercase						:     Optional[Any]		      =    tax_pre_attention_layer_norm

        __lowercase						:     Tuple		      =    tax_enc_dec_attention_key
        __lowercase						:     Optional[Any]		      =    tax_enc_dec_attention_out
        __lowercase						:     Optional[Any]		      =    tax_enc_dec_attention_query
        __lowercase						:     List[str]		      =    tax_enc_dec_attention_value

        __lowercase						:     Tuple		      =    tax_cross_layer_norm

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

        __lowercase						:     Tuple		      =    tax_mlp_wo

        __lowercase						:     Union[str, Any]		      =    txa_mlp_layer_norm

    # Decoder Normalization
    __lowercase						:     Optional[int]		      =    tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    __lowercase						:     str		      =    txa_decoder_norm

    # Only for layer 0:
    __lowercase						:     Union[str, Any]		      =    tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    __lowercase						:     Tuple		      =    tax_decoder_rel_embedding

    # Token Embeddings
    __lowercase						:     Tuple		      =    tax_model['''target''']['''token_embedder''']['''embedding''']
    __lowercase						:     Optional[Any]		      =    txa_token_embeddings

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

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


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




import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


__lowerCAmelCase   :     Optional[Any]      =						importlib.metadata.version("numpy")
__lowerCAmelCase   :     Any      =						".".join([str(x) for x in sys.version_info[:3]])


class   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      def 						snake_case_     ( self						:    List[Any]			):
          # lt + different version strings
          require_version_core('''numpy<1000.4.5'''			)
          require_version_core('''numpy<1000.4'''			)
          require_version_core('''numpy<1000'''			)

          # le
          require_version_core('''numpy<=1000.4.5'''			)
          require_version_core(F'numpy<={numpy_ver}'			)

          # eq
          require_version_core(F'numpy=={numpy_ver}'			)

          # ne
          require_version_core('''numpy!=1000.4.5'''			)

          # ge
          require_version_core('''numpy>=1.0'''			)
          require_version_core('''numpy>=1.0.0'''			)
          require_version_core(F'numpy>={numpy_ver}'			)

          # gt
          require_version_core('''numpy>1.0.0'''			)

          # mix
          require_version_core('''numpy>1.0.0,<1000'''			)

          # requirement w/o version
          require_version_core('''numpy'''			)

          # unmet requirements due to version conflict
          for req in ["numpy==1.0.0", "numpy>=1000.0.0", F'numpy<{numpy_ver}']:
              try:
                  require_version_core(_snake_case			)
              except ImportError as e:
                  self.assertIn(F'{req} is required'			,    str(_snake_case			)			)
                  self.assertIn('''but found'''			,    str(_snake_case			)			)

        # unmet requirements due to missing module
          for req in ["numpipypie>1", "numpipypie2"]:
              try:
                  require_version_core(_snake_case			)
              except importlib.metadata.PackageNotFoundError as e:
                  self.assertIn(F'The \'{req}\' distribution was not found and is required by this application'			,    str(_snake_case			)			)
                  self.assertIn('''Try: pip install transformers -U'''			,    str(_snake_case			)			)

        # bogus requirements formats:
        # 1. whole thing
          for req in ["numpy??1.0.0", "numpy1.0.0"]:
              try:
                  require_version_core(_snake_case			)
              except ValueError as e:
                  self.assertIn('''requirement needs to be in the pip package format'''			,    str(_snake_case			)			)
        # 2. only operators
          for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
              try:
                  require_version_core(_snake_case			)
              except ValueError as e:
                  self.assertIn('''need one of '''			,    str(_snake_case			)			)




      def 						snake_case_     ( self						:    List[str]			):
          # matching requirement
          require_version('''python>=3.6.0'''			)

          # not matching requirements
          for req in ["python>9.9.9", "python<3.0.0"]:
              try:
                  require_version_core(_snake_case			)
              except ImportError as e:
                  self.assertIn(F'{req} is required'			,    str(_snake_case			)			)
                  self.assertIn(F'but found python=={python_ver}'			,    str(_snake_case			)			)


