


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  Dict:




      """simple docstring"""

      snake_case_       =      TaConfig.from_pretrained(SCREAMING_SNAKE_CASE					)
      snake_case_       =      FlaxTaForConditionalGeneration(config=SCREAMING_SNAKE_CASE					)
      snake_case_       =      checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE					)

      snake_case_       =      '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

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

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

            # Layer Normalization
            snake_case_       =      tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

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

            snake_case_       =      tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            snake_case_       =      tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            snake_case_       =      tax_attention_key
            snake_case_       =      tax_attention_out
            snake_case_       =      tax_attention_query
            snake_case_       =      tax_attention_value

            snake_case_       =      tax_attention_layer_norm

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

            snake_case_       =      tax_mlp_wo
            snake_case_       =      tax_mlp_layer_norm

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

      # Assigning
      snake_case_       =      tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      snake_case_       =      tax_encoder_norm

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

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

            # Layer Normalization
            snake_case_       =      tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                '''scale'''
            ]

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

            # Layer Normalization
            snake_case_       =      tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

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

            snake_case_       =      tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            snake_case_       =      tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            snake_case_       =      tax_attention_key
            snake_case_       =      tax_attention_out
            snake_case_       =      tax_attention_query
            snake_case_       =      tax_attention_value

            snake_case_       =      tax_pre_attention_layer_norm

            snake_case_       =      tax_enc_dec_attention_key
            snake_case_       =      tax_enc_dec_attention_out
            snake_case_       =      tax_enc_dec_attention_query
            snake_case_       =      tax_enc_dec_attention_value

            snake_case_       =      tax_cross_layer_norm

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

            snake_case_       =      tax_mlp_wo

            snake_case_       =      txa_mlp_layer_norm

      # Decoder Normalization
      snake_case_       =      tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      snake_case_       =      txa_decoder_norm

      # Only for layer 0:
      snake_case_       =      tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
      snake_case_       =      tax_decoder_rel_embedding

      # Token Embeddings
      snake_case_       =      tax_model['''target''']['''token_embedder''']['''embedding''']
      snake_case_       =      txa_token_embeddings

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

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


if __name__ == "__main__":
   UpperCAmelCase			=    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."""
   )
   UpperCAmelCase			=    parser.parse_args()
   convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)



import argparse
import os


UpperCAmelCase			=    {
    """pytorch""": {
        """1.13""": {
            """torch""": """1.13.1""",
            """torchvision""": """0.14.1""",
            """torchaudio""": """0.13.1""",
            """python""": 3.9,
            """cuda""": """cu116""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu116"""
            ),
            """base_image""": """nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.12""": {
            """torch""": """1.12.1""",
            """torchvision""": """0.13.1""",
            """torchaudio""": """0.12.1""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.11""": {
            """torch""": """1.11.0""",
            """torchvision""": """0.12.0""",
            """torchaudio""": """0.11.0""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.10""": {
            """torch""": """1.10.2""",
            """torchvision""": """0.11.3""",
            """torchaudio""": """0.10.2""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        # torchaudio < 0.10 has no CUDA-enabled binary distributions
        """1.9""": {
            """torch""": """1.9.1""",
            """torchvision""": """0.10.1""",
            """torchaudio""": """0.9.1""",
            """python""": 3.9,
            """cuda""": """cu111""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu111"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
    },
    """tensorflow""": {
        """2.11""": {
            """tensorflow""": """2.11.1""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.11.1""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.10""": {
            """tensorflow""": """2.10.1""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.10.1""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.9""": {
            """tensorflow""": """2.9.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.9.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.8""": {
            """tensorflow""": """2.8.2""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.8.2""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.7""": {
            """tensorflow""": """2.7.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.7.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.6""": {
            """tensorflow""": """2.6.5""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.6.5""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.5""": {
            """tensorflow""": """2.5.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.5.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
    },
}


if __name__ == "__main__":
   UpperCAmelCase			=    argparse.ArgumentParser("""Choose the framework and version to install""")
   parser.add_argument(
       """--framework""", help="""The framework to install. Should be `torch` or `tensorflow`""", type=str, required=True
   )
   parser.add_argument("""--version""", help="""The version of the framework to install.""", type=str, required=True)
   UpperCAmelCase			=    parser.parse_args()

   UpperCAmelCase			=    past_versions_testing[args.framework][args.version]

   os.system(f'''echo "export INSTALL_CMD=\'{info['install']}\'" >> ~/.profile''')
   print(f'''echo "export INSTALL_CMD=\'{info['install']}\'" >> ~/.profile''')

   UpperCAmelCase			=    """"""
   if args.framework == "pytorch":
      UpperCAmelCase			=    info["""cuda"""]
   os.system(f'''echo \"export CUDA=\'{cuda}\'\" >> ~/.profile''')
   print(f'''echo \"export CUDA=\'{cuda}\'\" >> ~/.profile''')