


import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


__UpperCAmelCase			:			Optional[Any]			      =					torch.cuda.is_available()
__UpperCAmelCase			:			Any			      =					[" Sam ate lunch today.", "Sams lunch ingredients."]
__UpperCAmelCase			:			Optional[Any]			      =					["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__UpperCAmelCase			:			int			      =					{
    "max_tokens_per_batch": None,
    "supervise_forward": True,
    "normalize_hidden": True,
    "label_smoothing": 0.2,
    "eval_max_gen_length": None,
    "eval_beams": 1,
    "val_metric": "loss",
    "save_top_k": 1,
    "adafactor": True,
    "early_stopping_patience": 2,
    "logger_name": "default",
    "length_penalty": 0.5,
    "cache_dir": "",
    "task": "summarization",
    "num_workers": 2,
    "alpha_hid": 0,
    "freeze_embeds": True,
    "enc_only": False,
    "tgt_suffix": "",
    "resume_from_checkpoint": None,
    "sortish_sampler": True,
    "student_decoder_layers": 1,
    "val_check_interval": 1.0,
    "output_dir": "",
    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    "no_teacher": False,
    "fp16_opt_level": "O1",
    "gpus": 1 if CUDA_AVAILABLE else 0,
    "n_tpu_cores": 0,
    "max_grad_norm": 1.0,
    "do_train": True,
    "do_predict": True,
    "accumulate_grad_batches": 1,
    "server_ip": "",
    "server_port": "",
    "seed": 42,
    "model_name_or_path": "sshleifer/bart-tiny-random",
    "config_name": "",
    "tokenizer_name": "facebook/bart-large",
    "do_lower_case": False,
    "learning_rate": 0.3,
    "lr_scheduler": "linear",
    "weight_decay": 0.0,
    "adam_epsilon": 1E-08,
    "warmup_steps": 0,
    "max_epochs": 1,
    "train_batch_size": 2,
    "eval_batch_size": 2,
    "max_source_length": 12,
    "max_target_length": 12,
    "val_max_target_length": 12,
    "test_max_target_length": 12,
    "fast_dev_run": False,
    "no_cache": False,
    "n_train": -1,
    "n_val": -1,
    "n_test": -1,
    "student_encoder_layers": 1,
    "freeze_encoder": False,
    "auto_scale_batch_size": False,
    "overwrite_output_dir": False,
    "student": None,
}


def   a						(				SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		Optional[int]							):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		"""\n""".join(__A							)
       Path(__A							).open('''w'''							).writelines(__A							)


def   a						(				SCREAMING_SNAKE_CASE_   :		Any							):






       """simple docstring"""





       for split in ["train", "val", "test"]:
              _dump_articles(os.path.join(__A				,      F"""{split}.source"""							)				,      __A							)
              _dump_articles(os.path.join(__A				,      F"""{split}.target"""							)				,      __A							)
       return tmp_dir



class  UpperCAmelCase_						(				__lowerCAmelCase):
       '''simple docstring'''






       @classmethod
       def 			_lowercase (  cls    ):




              """simple docstring"""




              return cls






       @require_torch_multi_gpu
       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		{
                  """no_teacher""": True,
                  """freeze_encoder""": True,
                  """gpus""": 2,
                  """overwrite_output_dir""": True,
                  """sortish_sampler""": True,
              }
              self._test_distiller_cli_fork(_UpperCamelCase			,       check_contents=_UpperCamelCase    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=True    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		{
                  """label_smoothing""": 0.0,
                  """early_stopping_patience""": -1,
                  """train_batch_size""": 1,
                  """eval_batch_size""": 2,
                  """max_epochs""": 2,
                  """alpha_mlm""": 0.2,
                  """alpha_ce""": 0.8,
                  """do_predict""": True,
                  """model_name_or_path""": """sshleifer/tinier_bart""",
                  """teacher""": CHEAP_ARGS["""model_name_or_path"""],
                  """val_check_interval""": 0.5,
              }
              default_updates.update(_UpperCamelCase    )
              UpperCamelCase     :						dict 						=		CHEAP_ARGS.copy()
              UpperCamelCase     :						Any 						=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
              UpperCamelCase     :						Any 						=		self.get_auto_remove_tmp_dir()
              args_d.update(data_dir=_UpperCamelCase			,       output_dir=_UpperCamelCase			,       **_UpperCamelCase    )

              def convert(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):
                     if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                            return ""
                     if v is False or v is None:
                            return ""
                     if v is True:  # or len(str(v))==0:
                            return f"""--{k}"""
                     return f"""--{k}={v}"""

              UpperCamelCase     :						List[Any] 						=		[x for x in (convert(_UpperCamelCase			,       _UpperCamelCase    ) for k, v in args_d.items()) if len(_UpperCamelCase    )]
              UpperCamelCase     :						List[str] 						=		[sys.executable, f"""{self.test_file_dir}/distillation.py"""] + cli_args
              execute_subprocess_async(_UpperCamelCase			,       env=self.get_env()    )

              UpperCamelCase     :						Optional[int] 						=		os.listdir(_UpperCamelCase    )
              UpperCamelCase     :						str 						=		{os.path.basename(_UpperCamelCase    ) for p in contents}
              UpperCamelCase     :						Optional[Any] 						=		[p for p in contents if p.endswith('''ckpt'''    )]
              assert len(_UpperCamelCase    ) > 0

              self.assertIn('''test_generations.txt'''			,       _UpperCamelCase    )
              self.assertIn('''test_results.txt'''			,       _UpperCamelCase    )

              # get the following from the module, (we don't have access to `model` here)
              UpperCamelCase     :						Dict 						=		os.path.join(_UpperCamelCase			,       '''metrics.json'''    )
              UpperCamelCase     :						List[str] 						=		"""rouge2"""

              UpperCamelCase     :						List[Any] 						=		load_json(_UpperCamelCase    )
              # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
              print(_UpperCamelCase    )
              UpperCamelCase     :						int 						=		metrics["""val"""][-1]
              self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']			,       0.01    )
              self.assertIsInstance(last_step_stats[f"""val_avg_{val_metric}"""]			,       _UpperCamelCase    )
              self.assertEqual(len(metrics['''test''']    )			,       1    )
              UpperCamelCase     :						Optional[Any] 						=		int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1    )
              self.assertEqual(len(metrics['''val''']    )			,       _UpperCamelCase    )


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Tuple			    =    TaConfig.from_pretrained(__A						)
    _lowerCamelCase  :		List[Any]			    =    FlaxTaForConditionalGeneration(config=__A						)
    _lowerCamelCase  :		Tuple			    =    checkpoints.load_tax_checkpoint(__A						)

    _lowerCamelCase  :		Union[str, Any]			    =    """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

    # Encoder
    for layer_index in range(config.num_layers						):
        _lowerCamelCase  :		Optional[int]			    =    F"""layers_{str(__A						)}"""

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

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

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

        _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		Optional[Any]			    =    tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

        # Assigning
        _lowerCamelCase  :		int			    =    tax_attention_key
        _lowerCamelCase  :		Tuple			    =    tax_attention_out
        _lowerCamelCase  :		List[str]			    =    tax_attention_query
        _lowerCamelCase  :		Tuple			    =    tax_attention_value

        _lowerCamelCase  :		Dict			    =    tax_attention_layer_norm

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

        _lowerCamelCase  :		Tuple			    =    tax_mlp_wo
        _lowerCamelCase  :		Optional[Any]			    =    tax_mlp_layer_norm

    # Only for layer 0:
    _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
    _lowerCamelCase  :		List[Any]			    =    tax_encoder_rel_embedding

    # Assigning
    _lowerCamelCase  :		Union[str, Any]			    =    tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
    _lowerCamelCase  :		int			    =    tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_decoder_layers						):
        _lowerCamelCase  :		Union[str, Any]			    =    F"""layers_{str(__A						)}"""

        # Self-Attention
        _lowerCamelCase  :		List[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
        _lowerCamelCase  :		Dict			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
        _lowerCamelCase  :		Any			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
        _lowerCamelCase  :		Dict			    =    tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		List[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
            """scale"""
        ]

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

        # Layer Normalization
        _lowerCamelCase  :		Tuple			    =    tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

        # MLP
        if split_mlp_wi:
            _lowerCamelCase  :		str			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
            _lowerCamelCase  :		List[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
        else:
            _lowerCamelCase  :		Optional[Any]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

        _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

        # Layer Normalization
        _lowerCamelCase  :		Optional[int]			    =    tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

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

        _lowerCamelCase  :		Optional[Any]			    =    tax_pre_attention_layer_norm

        _lowerCamelCase  :		str			    =    tax_enc_dec_attention_key
        _lowerCamelCase  :		str			    =    tax_enc_dec_attention_out
        _lowerCamelCase  :		Tuple			    =    tax_enc_dec_attention_query
        _lowerCamelCase  :		Dict			    =    tax_enc_dec_attention_value

        _lowerCamelCase  :		List[Any]			    =    tax_cross_layer_norm

        if split_mlp_wi:
            _lowerCamelCase  :		Optional[Any]			    =    tax_mlp_wi_a
            _lowerCamelCase  :		Tuple			    =    tax_mlp_wi_a
        else:
            _lowerCamelCase  :		Any			    =    tax_mlp_wi

        _lowerCamelCase  :		Optional[int]			    =    tax_mlp_wo

        _lowerCamelCase  :		List[str]			    =    txa_mlp_layer_norm

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

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

    # Token Embeddings
    _lowerCamelCase  :		List[str]			    =    tax_model["""target"""]["""token_embedder"""]["""embedding"""]
    _lowerCamelCase  :		List[str]			    =    txa_token_embeddings

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

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


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




