


import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase      ):
							return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase="attention"      ):
							__snake_case						:     Tuple									=  np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]      )
							__snake_case						:     Dict									=  k_tmp.reshape(k_tmp.shape[0]				,      k_tmp.shape[1] * k_tmp.shape[2]      )
							__snake_case						:     str									=  np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]      )
							__snake_case						:     List[str]									=  o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]				,      o_tmp.shape[2]      )
							__snake_case						:     Any									=  np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]      )
							__snake_case						:     Optional[int]									=  q_tmp.reshape(q_tmp.shape[0]				,      q_tmp.shape[1] * q_tmp.shape[2]      )
							__snake_case						:     List[str]									=  np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]      )
							__snake_case						:     List[Any]									=  v_tmp.reshape(v_tmp.shape[0]				,      v_tmp.shape[1] * v_tmp.shape[2]      )
							return k, o, q, v







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase=False      ):
							if split_mlp_wi:
														__snake_case						:     Any									=  params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
														__snake_case						:     List[Any]									=  params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
														__snake_case						:     Optional[Any]									=  (wi_a, wi_a)
							else:
														__snake_case						:     Optional[int]									=  params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

							__snake_case						:     List[Any]									=  params[F'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
							return wi, wo







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase      ):
							return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]







def 							lowerCAmelCase_	(   __lowerCamelCase				,      *, __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase = False      ):
							__snake_case						:     Union[str, Any]									=  traverse_util.flatten_dict(variables["target"]      )
							__snake_case						:     str									=  {"/".join(__lowerCamelCase      ): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							__snake_case						:     Any									=  "encoder/encoder/mlp/wi_0/kernel" in old
							print("Split MLP:"				,      __lowerCamelCase      )

							__snake_case						:     Tuple									=  collections.OrderedDict()

							# Shared embeddings.
							__snake_case						:     Optional[int]									=  old["token_embedder/embedding"]

							# Encoder.
							for i in range(__lowerCamelCase      ):
														# Block i, layer 0 (Self Attention).
														__snake_case						:     Union[str, Any]									=  tax_layer_norm_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "encoder"				,      "pre_attention_layer_norm"      )
														__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     List[str]									=  tax_attention_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "encoder"				,      "attention"      )
														__snake_case						:     List[str]									=  layer_norm
														__snake_case						:     Union[str, Any]									=  k.T
														__snake_case						:     List[str]									=  o.T
														__snake_case						:     List[str]									=  q.T
														__snake_case						:     Union[str, Any]									=  v.T

														# Block i, layer 1 (MLP).
														__snake_case						:     str									=  tax_layer_norm_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "encoder"				,      "pre_mlp_layer_norm"      )
														__snake_case					,     __snake_case						:     Any									=  tax_mlp_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "encoder"				,      __lowerCamelCase      )
														__snake_case						:     str									=  layer_norm
														if split_mlp_wi:
																					__snake_case						:     Optional[int]									=  wi[0].T
																					__snake_case						:     List[Any]									=  wi[1].T
														else:
																					__snake_case						:     Any									=  wi.T
														__snake_case						:     Union[str, Any]									=  wo.T
														if scalable_attention:
																					# convert the rel_embedding of each layer
																					__snake_case						:     List[Any]									=  tax_relpos_bias_lookup(
																					    __lowerCamelCase				,      __lowerCamelCase				,      "encoder"      ).T

							__snake_case						:     int									=  old["encoder/encoder_norm/scale"]

							if not scalable_attention:
														__snake_case						:     Optional[int]									=  tax_relpos_bias_lookup(
														    __lowerCamelCase				,      0				,      "encoder"      ).T
														__snake_case						:     Union[str, Any]									=  tax_relpos_bias_lookup(
														    __lowerCamelCase				,      0				,      "decoder"      ).T

							if not is_encoder_only:
														# Decoder.
														for i in range(__lowerCamelCase      ):
																					# Block i, layer 0 (Self Attention).
																					__snake_case						:     Tuple									=  tax_layer_norm_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "decoder"				,      "pre_self_attention_layer_norm"      )
																					__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     Tuple									=  tax_attention_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "decoder"				,      "self_attention"      )
																					__snake_case						:     int									=  layer_norm
																					__snake_case						:     Tuple									=  k.T
																					__snake_case						:     List[Any]									=  o.T
																					__snake_case						:     str									=  q.T
																					__snake_case						:     Optional[Any]									=  v.T

																					# Block i, layer 1 (Cross Attention).
																					__snake_case						:     Optional[Any]									=  tax_layer_norm_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "decoder"				,      "pre_cross_attention_layer_norm"      )
																					__snake_case					,     __snake_case					,     __snake_case					,     __snake_case						:     List[Any]									=  tax_attention_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "decoder"				,      "encoder_decoder_attention"      )
																					__snake_case						:     Optional[int]									=  layer_norm
																					__snake_case						:     int									=  k.T
																					__snake_case						:     Optional[Any]									=  o.T
																					__snake_case						:     Dict									=  q.T
																					__snake_case						:     List[Any]									=  v.T

																					# Block i, layer 2 (MLP).
																					__snake_case						:     Dict									=  tax_layer_norm_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "decoder"				,      "pre_mlp_layer_norm"      )
																					__snake_case					,     __snake_case						:     Dict									=  tax_mlp_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "decoder"				,      __lowerCamelCase      )
																					__snake_case						:     Dict									=  layer_norm
																					if split_mlp_wi:
																												__snake_case						:     List[Any]									=  wi[0].T
																												__snake_case						:     int									=  wi[1].T
																					else:
																												__snake_case						:     Dict									=  wi.T
																					__snake_case						:     Any									=  wo.T

																					if scalable_attention:
																												# convert the rel_embedding of each layer
																												__snake_case						:     Any									=  tax_relpos_bias_lookup(__lowerCamelCase				,      __lowerCamelCase				,      "decoder"      ).T

														__snake_case						:     Dict									=  old["decoder/decoder_norm/scale"]

														# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
														if "decoder/logits_dense/kernel" in old:
																					__snake_case						:     List[Any]									=  old["decoder/logits_dense/kernel"].T

							return new







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase      ):
							__snake_case						:     Optional[Any]									=  collections.OrderedDict([(k, torch.from_numpy(v.copy()      )) for (k, v) in converted_params.items()]      )

							# Add what is missing.
							if "encoder.embed_tokens.weight" not in state_dict:
														__snake_case						:     int									=  state_dict["shared.weight"]

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					__snake_case						:     Optional[int]									=  state_dict["shared.weight"]

														if "lm_head.weight" not in state_dict:  # For old 1.0 models.
																					print("Using shared word embeddings as lm_head."      )
																					__snake_case						:     int									=  state_dict["shared.weight"]

							return state_dict







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase      ):
							__snake_case						:     Union[str, Any]									=  checkpoints.load_tax_checkpoint(__lowerCamelCase      )
							__snake_case						:     int									=  convert_tax_to_pytorch(
							    __lowerCamelCase				,      num_layers=config.num_layers				,      is_encoder_only=__lowerCamelCase				,      scalable_attention=__lowerCamelCase      )
							__snake_case						:     List[str]									=  make_state_dict(__lowerCamelCase				,      __lowerCamelCase      )
							model.load_state_dict(__lowerCamelCase				,      strict=__lowerCamelCase      )







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase = False				,      __lowerCamelCase = False				,      ):
							__snake_case						:     str									=  MTaConfig.from_json_file(__lowerCamelCase      )
							print(F'Building PyTorch model from configuration: {config}'      )
							# Non-v1.1 checkpoints could also use T5Model, but this works for all.
							# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
							if is_encoder_only:
														__snake_case						:     Optional[int]									=  UMTaEncoderModel(__lowerCamelCase      )
							else:
														__snake_case						:     int									=  UMTaForConditionalGeneration(__lowerCamelCase      )

							# Load weights from tf checkpoint
							load_tax_weights_in_ta(__lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase      )

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}'      )
							model.save_pretrained(__lowerCamelCase      )

							# Verify that we can load the checkpoint.
							model.from_pretrained(__lowerCamelCase      )
							print("Done"      )


if __name__ == "__main__":
			_snake_case	:						Optional[Any]         =							argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
			# Required parameters
			parser.add_argument(
			    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
			)
			parser.add_argument(
			    "--config_file",
			    default=None,
			    type=str,
			    required=True,
			    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			parser.add_argument(
			    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
			)
			parser.add_argument(
			    "--scalable_attention",
			    action="store_true",
			    help="Whether the model uses scaled attention (umt5 model)",
			    default=False,
			)
			_snake_case	:						Optional[Any]         =							parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path,
			    args.config_file,
			    args.pytorch_dump_path,
			    args.is_encoder_only,
			    args.scalable_attention,
			)


"""simple docstring"""






import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__lowerCAmelCase       :			List[str]   	=[
    """cross_validation.py""",
    """gradient_accumulation.py""",
    """local_sgd.py""",
    """multi_process_metrics.py""",
    """memory.py""",
    """automatic_gradient_accumulation.py""",
    """fsdp_with_peak_mem_tracking.py""",
    """deepspeed_with_config_support.py""",
    """megatron_lm_gpt_pretraining.py""",
]





class 					_A		(			unittest.TestCase      ):

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None		):







            """simple docstring"""



            lowercase								=      None
            lowercase								=      os.path.abspath(os.path.join("""examples"""						,			"""by_feature"""		)		)
            lowercase								=      os.path.abspath("""examples"""		)
            for item in os.listdir(__lowerCAmelCase		):
                  if item not in EXCLUDE_EXAMPLES:
                        lowercase								=      os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)
                        if os.path.isfile(__lowerCAmelCase		) and ".py" in item_path:
                              with self.subTest(
                                  tested_script=__lowerCAmelCase						,			feature_script=__lowerCAmelCase						,			tested_section="""main()""" if parser_only else """training_function()"""						,			):
                                    lowercase								=      compare_against_test(
                                        os.path.join(__lowerCAmelCase						,			__lowerCAmelCase		)						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                                    lowercase								=      """\n""".join(__lowerCAmelCase		)
                                    if special_strings is not None:
                                          for string in special_strings:
                                                lowercase								=      diff.replace(__lowerCAmelCase						,			""""""		)
                                    self.assertEqual(__lowerCAmelCase						,			""""""		)

      def 							A__			(		self		):







            """simple docstring"""



            self.one_complete_example("""complete_nlp_example.py"""						,			__lowerCAmelCase		)
            self.one_complete_example("""complete_nlp_example.py"""						,			__lowerCAmelCase		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      os.path.abspath(os.path.join("""examples"""						,			"""cv_example.py"""		)		)
            lowercase								=      [
                """ """ * 16 + """{\n\n""",
                """ """ * 20 + """\"accuracy\": eval_metric[\"accuracy\"],\n\n""",
                """ """ * 20 + """\"f1\": eval_metric[\"f1\"],\n\n""",
                """ """ * 20 + """\"train_loss\": total_loss.item() / len(train_dataloader),\n\n""",
                """ """ * 20 + """\"epoch\": epoch,\n\n""",
                """ """ * 16 + """},\n\n""",
                """ """ * 16 + """step=epoch,\n""",
                """ """ * 12,
                """ """ * 8 + """for step, batch in enumerate(active_dataloader):\n""",
            ]
            self.one_complete_example("""complete_cv_example.py"""						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
            self.one_complete_example("""complete_cv_example.py"""						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)






@mock.patch.dict(os.environ    ,       {'TESTING_MOCKED_DATALOADERS': '1'}      )
class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Any				    =      False

      @classmethod
      def 							A__			(		cls		):







            """simple docstring"""



            super().setUpClass()
            lowercase								=      tempfile.mkdtemp()
            lowercase								=      os.path.join(cls._tmpdir						,			"""default_config.yml"""		)

            write_basic_config(save_location=cls.configPath		)
            lowercase								=      ["""accelerate""", """launch""", """--config_file""", cls.configPath]

      @classmethod
      def 							A__			(		cls		):







            """simple docstring"""



            super().tearDownClass()
            shutil.rmtree(cls._tmpdir		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
            run_command(self._launch_args + testargs		)
            self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,			"""epoch_0"""		)		)		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
            lowercase								=      run_command(self._launch_args + testargs		)
            self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,			"""step_2"""		)		)		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir						,			"epoch_0"		)}\n        '.split()
            lowercase								=      run_command(self._launch_args + testargs						,			return_stdout=__lowerCAmelCase		)
            self.assertNotIn("""epoch 0:"""						,			__lowerCAmelCase		)
            self.assertIn("""epoch 1:"""						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir						,			"step_2"		)}\n        '.split()
            lowercase								=      run_command(self._launch_args + testargs						,			return_stdout=__lowerCAmelCase		)
            if torch.cuda.is_available():
                  lowercase								=      torch.cuda.device_count()
            else:
                  lowercase								=      1
            if num_processes > 1:
                  self.assertNotIn("""epoch 0:"""						,			__lowerCAmelCase		)
                  self.assertIn("""epoch 1:"""						,			__lowerCAmelCase		)
            else:
                  self.assertIn("""epoch 0:"""						,			__lowerCAmelCase		)
                  self.assertIn("""epoch 1:"""						,			__lowerCAmelCase		)

      @slow
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """
        examples/by_feature/cross_validation.py
        --num_folds 2
        """.split()
            with mock.patch.dict(os.environ						,			{"""TESTING_MOCKED_DATALOADERS""": """0"""}		):
                  lowercase								=      run_command(self._launch_args + testargs						,			return_stdout=__lowerCAmelCase		)
                  lowercase								=      re.findall("""({.+})"""						,			__lowerCAmelCase		)
                  lowercase								=      [r for r in results if """accuracy""" in r][-1]
                  lowercase								=      ast.literal_eval(__lowerCAmelCase		)
                  self.assertGreaterEqual(results["""accuracy"""]						,			0.7_5		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ["""examples/by_feature/multi_process_metrics.py"""]
            run_command(self._launch_args + testargs		)

      @require_trackers
      @mock.patch.dict(os.environ						,			{"""WANDB_MODE""": """offline"""}		)
      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdir:
                  lowercase								=      f'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
                  run_command(self._launch_args + testargs		)
                  self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase						,			"""tracking"""		)		)		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ["""examples/by_feature/gradient_accumulation.py"""]
            run_command(self._launch_args + testargs		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ["""examples/by_feature/local_sgd.py"""]
            run_command(self._launch_args + testargs		)



