





import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Value
from datasets.packaged_modules.json.json import Json




@pytest.fixture
def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		List[Any]:
       UpperCamelCase_ 							=	tmp_path / "file.jsonl"
       UpperCamelCase_ 							=	textwrap.dedent(
           "\\n        {\"col_1\": -1}\n        {\"col_1\": 1, \"col_2\": 2}\n        {\"col_1\": 10, \"col_2\": 20}\n        "	)
       with open(UpperCamelCase_       ,		"w"	) as f:
              f.write(UpperCamelCase_	)
       return str(UpperCamelCase_	)




@pytest.fixture
def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		str:
       UpperCamelCase_ 							=	tmp_path / "file_utf16_encoded.jsonl"
       UpperCamelCase_ 							=	textwrap.dedent(
           "\\n        {\"col_1\": -1}\n        {\"col_1\": 1, \"col_2\": 2}\n        {\"col_1\": 10, \"col_2\": 20}\n        "	)
       with open(UpperCamelCase_       ,		"w"       ,		encoding="utf-16"	) as f:
              f.write(UpperCamelCase_	)
       return str(UpperCamelCase_	)




@pytest.fixture
def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		List[str]:
       UpperCamelCase_ 							=	tmp_path / "file_with_list_of_dicts.json"
       UpperCamelCase_ 							=	textwrap.dedent(
           "\\n        [\n            {\"col_1\": -1},\n            {\"col_1\": 1, \"col_2\": 2},\n            {\"col_1\": 10, \"col_2\": 20}\n        ]\n        "	)
       with open(UpperCamelCase_       ,		"w"	) as f:
              f.write(UpperCamelCase_	)
       return str(UpperCamelCase_	)




@pytest.fixture
def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Union[str, Any]:
       UpperCamelCase_ 							=	tmp_path / "file_with_list_of_dicts_field.json"
       UpperCamelCase_ 							=	textwrap.dedent(
           "\\n        {\n            \"field1\": 1,\n            \"field2\": \"aabb\",\n            \"field3\": [\n                {\"col_1\": -1},\n                {\"col_1\": 1, \"col_2\": 2},\n                {\"col_1\": 10, \"col_2\": 20}\n            ]\n        }\n        "	)
       with open(UpperCamelCase_       ,		"w"	) as f:
              f.write(UpperCamelCase_	)
       return str(UpperCamelCase_	)




@pytest.mark.parametrize(
    "file_fixture, config_kwargs"       ,		[
        ("jsonl_file", {}),
        ("jsonl_file_utf16_encoded", {"encoding": "utf-16"}),
        ("json_file_with_list_of_dicts", {}),
        ("json_file_with_list_of_dicts_field", {"field": "field3"}),
    ]       ,		)
def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		List[str]:
       UpperCamelCase_ 							=	Json(**UpperCamelCase_	)
       UpperCamelCase_ 							=	json._generate_tables([[request.getfixturevalue(UpperCamelCase_	)]]	)
       UpperCamelCase_ 							=	pa.concat_tables([table for _, table in generator]	)
       assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20]}







@pytest.mark.parametrize(
    "file_fixture, config_kwargs"       ,		[
        (
            "jsonl_file",
            {"features": Features({"col_1": Value("int64"	), "col_2": Value("int64"	), "missing_col": Value("string"	)}	)},
        ),
        (
            "json_file_with_list_of_dicts",
            {"features": Features({"col_1": Value("int64"	), "col_2": Value("int64"	), "missing_col": Value("string"	)}	)},
        ),
        (
            "json_file_with_list_of_dicts_field",
            {
                "field": "field3",
                "features": Features(
                    {"col_1": Value("int64"	), "col_2": Value("int64"	), "missing_col": Value("string"	)}	),
            },
        ),
    ]       ,		)
def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Dict:
       UpperCamelCase_ 							=	Json(**UpperCamelCase_	)
       UpperCamelCase_ 							=	json._generate_tables([[request.getfixturevalue(UpperCamelCase_	)]]	)
       UpperCamelCase_ 							=	pa.concat_tables([table for _, table in generator]	)
       assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20], "missing_col": [None, None, None]}


"""simple docstring"""


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def        __a (		A     ,					A     ,					A   ):






     '''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__":
 lowerCAmelCase_:       List[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."
 )
 lowerCAmelCase_:       Dict			     =       parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




