'''simple docstring'''




import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
__lowercase				   =  {
    '''translation''': ['''bleu'''],
    '''summarization''': ROUGE_KEYS,
}






def     snake_case__							(      _A: Optional[Any]			)   ->     Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase        =  search.split()
		lowerCAmelCase        =  dict((g.split("""="""			) for g in groups)			)
		lowerCAmelCase        =  list(entries.keys()			)
		lowerCAmelCase        =  [[f"--{k} {v}" for v in vs.split(""":"""			)] for k, vs in entries.items()]
		lowerCAmelCase        =  [list(_A			) for x in itertools.product(*_A			)]
		return matrix, entry_names






def     snake_case__							(      )   ->     List[Any]:

		'''simple docstring'''


		lowerCAmelCase        =  sys.argv[0]

		lowerCAmelCase        =  argparse.ArgumentParser(
		    usage=(
		        """\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore"""
		        """ refer to `run_eval.py -h` for the complete list."""
		    )			)
		parser.add_argument(
		    """--search"""				,       type=_A				,       required=_A				,       help="""param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\""""				,       )
		parser.add_argument(
		    """--bs"""				,       type=_A				,       default=8				,       required=_A				,       help="""initial batch size (may get reduced if it's too big)"""			)
		parser.add_argument("""--task"""				,       type=_A				,       help="""used for task_specific_params + metrics"""			)
		parser.add_argument(
		    """--info"""				,       nargs="""?"""				,       type=_A				,       const=datetime_now()				,       help=(
		        """add custom notes to be printed before the results table. If no value is passed, the current datetime"""
		        """ string will be used."""
		    )				,       )
		lowerCAmelCase ,					lowerCAmelCase        =  parser.parse_known_args()
		# we share some of the args
		args_main.extend(["""--task""", args.task]			)
		lowerCAmelCase        =  [prog] + args_main

		# to support variations like translation_en_to_de"
		lowerCAmelCase        =  """translation""" if """translation""" in args.task else """summarization"""

		lowerCAmelCase ,					lowerCAmelCase        =  parse_search_arg(args.search			)
		lowerCAmelCase        =  task_score_names[task]  # score cols first
		lowerCAmelCase        =  {col: len(str(_A			)			) for col in col_names}
		lowerCAmelCase        =  []
		for r in matrix:
				lowerCAmelCase        =  dict((x.replace("""--"""				,       """"""			).split() for x in r)			)
				lowerCAmelCase        =  """ """.join(_A			).split()
				args_exp.extend(["""--bs""", str(args.bs			)]			)  # in case we need to reduce its size due to CUDA OOM
				lowerCAmelCase        =  args_normal + args_exp

				# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

				lowerCAmelCase        =  run_generate(verbose=_A			)
				# make sure scores are first in the table
				lowerCAmelCase        =  OrderedDict()
				for score in task_score_names[task]:
						lowerCAmelCase        =  scores[score]
				result.update(_A			)
				results.append(_A			)

				# find widest entries
				for k, v in result.items():
						lowerCAmelCase        =  len(str(_A			)			)
						if l > col_widths[k]:
								lowerCAmelCase        =  l

		lowerCAmelCase        =  sorted(_A				,       key=operator.itemgetter(*task_score_names[task]			)				,       reverse=_A			)
		print(""" | """.join([f"{col:{col_widths[col]}}" for col in col_names]			)			)
		print(""" | """.join([f"{'-'*col_widths[col]}" for col in col_names]			)			)
		for row in results_sorted:
				print(""" | """.join([f"{row[col]:{col_widths[col]}}" for col in col_names]			)			)

		lowerCAmelCase        =  results_sorted[0]
		for score in task_score_names[task]:
				del best[score]
		lowerCAmelCase        =  [f"--{k} {v}" for k, v in best.items()]
		lowerCAmelCase        =  ["""--bs""", str(args.bs			)]
		if args.info:
				print(f"\nInfo: {args.info}"			)
		print("""\nBest score args:"""			)
		print(""" """.join(args_main + best_args + dyn_args			)			)

		return results_sorted


if __name__ == "__main__":
						# Usage:
						# [normal-run_eval_search.py cmd plus] \
						# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
						#
						# Example:
						# PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
						# $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
						# --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
						# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
						run_search()





'''simple docstring'''




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()






def     snake_case__							(      _A: Dict				,       _A: str				,       _A: int			)   ->     Any:

		'''simple docstring'''


		lowerCAmelCase        =  FNetConfig.from_json_file(_A			)
		print(f"Building PyTorch model from configuration: {config}"			)
		lowerCAmelCase        =  FNetForPreTraining(_A			)

		lowerCAmelCase        =  restore_checkpoint(_A				,       _A			)
		lowerCAmelCase        =  checkpoint_dict["""target"""]

		# Embeddings
		# Position IDs
		lowerCAmelCase        =  fnet_pretraining_model.state_dict()

		lowerCAmelCase        =  state_dict["""fnet.embeddings.position_ids"""]
		lowerCAmelCase        =  {"""fnet.embeddings.position_ids""": position_ids}
		# Embedding Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]			)

		# Encoder Layers
		for layer in range(config.num_hidden_layers			):
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""mixing_layer_norm"""]["""scale"""]			)
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""mixing_layer_norm"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""output_layer_norm"""]["""scale"""]			)
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""output_layer_norm"""]["""bias"""]			)

		# Pooler Layers
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]			)

		# Masked LM Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_output"""]["""output_bias"""]			)
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]			)

		# Seq Relationship Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""classification"""]["""output_kernel"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""classification"""]["""output_bias"""]			)

		# Load State Dict
		fnet_pretraining_model.load_state_dict(_A			)

		# Save PreTrained
		print(f"Saving pretrained model to {save_path}"			)
		fnet_pretraining_model.save_pretrained(_A			)


if __name__ == "__main__":
						__lowercase				   =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--fnet_config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        '''The config json file corresponding to the pre-trained FNet model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
						__lowercase				   =  parser.parse_args()
						convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




