



'''simple docstring'''


import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__="attention"       ):


					'''simple docstring'''







					a_       		=params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
					a_       		=params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
					a_       		=params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
					a_       		=params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
					return k, o, q, v






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__=False       ):


					'''simple docstring'''







					if split_mlp_wi:
										a_       		=params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
										a_       		=params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
										a_       		=(wi_a, wi_a)
					else:
										a_       		=params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

					a_       		=params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
					return wi, wo






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


					'''simple docstring'''







					return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]






def  UpperCAmelCase_       (		lowercase__       ,	*, lowercase__       ,	lowercase__       ):


					'''simple docstring'''







					a_       		=traverse_util.flatten_dict(variables["target"]       )
					a_       		={"/".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
					a_       		="encoder/layers_0/mlp/wi_0/kernel" in old
					print("Split MLP:"       ,	lowerCamelCase__       )

					a_       		=collections.OrderedDict()

					# Shared embeddings.
					a_       		=old["token_embedder/embedding"]

					# Encoder.
					for i in range(lowerCamelCase__       ):
										# Block i, layer 0 (Self Attention).
										a_       		=tax_layer_norm_lookup(lowerCamelCase__       ,	lowerCamelCase__       ,	"encoder"       ,	"pre_attention_layer_norm"       )
										a_       		=tax_attention_lookup(lowerCamelCase__       ,	lowerCamelCase__       ,	"encoder"       ,	"attention"       )
										a_       		=layer_norm
										a_       		=k.T
										a_       		=o.T
										a_       		=q.T
										a_       		=v.T

										# Block i, layer 1 (MLP).
										a_       		=tax_layer_norm_lookup(lowerCamelCase__       ,	lowerCamelCase__       ,	"encoder"       ,	"pre_mlp_layer_norm"       )
										a_       		=tax_mlp_lookup(lowerCamelCase__       ,	lowerCamelCase__       ,	"encoder"       ,	lowerCamelCase__       )
										a_       		=layer_norm
										if split_mlp_wi:
															a_       		=wi[0].T
															a_       		=wi[1].T
										else:
															a_       		=wi.T
										a_       		=wo.T

					a_       		=old[
					    "encoder/relpos_bias/rel_embedding"
					].T
					a_       		=old["encoder/encoder_norm/scale"]

					if not is_encoder_only:
										# Decoder.
										for i in range(lowerCamelCase__       ):
															# Block i, layer 0 (Self Attention).
															a_       		=tax_layer_norm_lookup(lowerCamelCase__       ,	lowerCamelCase__       ,	"decoder"       ,	"pre_self_attention_layer_norm"       )
															a_       		=tax_attention_lookup(lowerCamelCase__       ,	lowerCamelCase__       ,	"decoder"       ,	"self_attention"       )
															a_       		=layer_norm
															a_       		=k.T
															a_       		=o.T
															a_       		=q.T
															a_       		=v.T

															# Block i, layer 1 (Cross Attention).
															a_       		=tax_layer_norm_lookup(lowerCamelCase__       ,	lowerCamelCase__       ,	"decoder"       ,	"pre_cross_attention_layer_norm"       )
															a_       		=tax_attention_lookup(lowerCamelCase__       ,	lowerCamelCase__       ,	"decoder"       ,	"encoder_decoder_attention"       )
															a_       		=layer_norm
															a_       		=k.T
															a_       		=o.T
															a_       		=q.T
															a_       		=v.T

															# Block i, layer 2 (MLP).
															a_       		=tax_layer_norm_lookup(lowerCamelCase__       ,	lowerCamelCase__       ,	"decoder"       ,	"pre_mlp_layer_norm"       )
															a_       		=tax_mlp_lookup(lowerCamelCase__       ,	lowerCamelCase__       ,	"decoder"       ,	lowerCamelCase__       )
															a_       		=layer_norm
															if split_mlp_wi:
																				a_       		=wi[0].T
																				a_       		=wi[1].T
															else:
																				a_       		=wi.T
															a_       		=wo.T

										a_       		=old["decoder/decoder_norm/scale"]
										a_       		=old[
										    "decoder/relpos_bias/rel_embedding"
										].T

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

					return new






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


					'''simple docstring'''







					a_       		=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:
										a_       		=state_dict["shared.weight"]

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															a_       		=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."       )
															a_       		=state_dict["shared.weight"]

					return state_dict






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


					'''simple docstring'''







					a_       		=checkpoints.load_tax_checkpoint(lowerCamelCase__       )
					a_       		=convert_tax_to_pytorch(lowerCamelCase__       ,	num_layers=config.num_layers       ,	is_encoder_only=lowerCamelCase__       )
					a_       		=make_state_dict(lowerCamelCase__       ,	lowerCamelCase__       )
					model.load_state_dict(lowerCamelCase__       ,	strict=lowerCamelCase__       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__ = False       ):


					'''simple docstring'''







					a_       		=TaConfig.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:
										a_       		=TaEncoderModel(lowerCamelCase__       )
					else:
										a_       		=TaForConditionalGeneration(lowerCamelCase__       )

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(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__":
		lowercase							       =			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
		)
		lowercase							       =			parser.parse_args()
		convert_tax_checkpoint_to_pytorch(
		    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
		)







'''simple docstring'''


import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

lowercase							       =			logging.getLogger()






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_       		={}
     a_       		=os.path.join(lowercase__       ,	"all_results.json"       )
     if os.path.exists(lowercase__       ):
          with open(lowercase__       ,	"r"       ) as f:
               a_       		=json.load(lowercase__       )
     else:
          raise ValueError(F"""can't find {path}"""       )
     return results


lowercase							       =			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@require_torch_tpu
class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      import xla_spawn

      a_       		=self.get_auto_remove_tmp_dir()
      a_       		=f"""
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

      with patch.object(lowerCAmelCase_							,  "argv"							,  lowerCAmelCase_):
           a_       		=time()
           xla_spawn.main()
           a_       		=time()

           a_       		=get_results(lowerCAmelCase_)
           self.assertGreaterEqual(result["eval_accuracy"]							,  0.7_5)

           # Assert that the script takes less than 500 seconds to make sure it doesn't hang.
           self.assertLess(end - start							,  5_0_0)




 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      import xla_spawn

      a_       		="\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            ".split()
      with patch.object(lowerCAmelCase_							,  "argv"							,  lowerCAmelCase_):
           xla_spawn.main()


