






"""simple docstring"""




import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
      from comet_ml import OfflineExperiment

if is_tensorboard_available():
      import struct

      import tensorboard.compat.proto.event_pba as event_pba

lowerCamelCase__			=   logging.getLogger(__name__)







@require_tensorboard
class 					_UpperCamelCase		( unittest.TestCase):






    def  A    (self   ):


      """simple docstring"""
      A__											=							"""test_project_with_config"""
      with tempfile.TemporaryDirectory() as dirpath:
        A__											=							Accelerator(log_with="""tensorboard"""						,      project_dir=lowerCamelCase__   )
        A__											=							{"""num_iterations""": 1_2, """learning_rate""": 1E-2, """some_boolean""": False, """some_string""": """some_value"""}
        accelerator.init_trackers(lowerCamelCase__						,      lowerCamelCase__   )
        accelerator.end_training()
        for child in Path(F"""{dirpath}/{project_name}"""   ).glob("""*/**"""   ):
          A__											=							list(filter(lambda lowerCamelCase__   : x.is_file()						,      child.iterdir()   )   )[0]
        self.assertNotEqual(str(lowerCamelCase__   )						,      """"""   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							"""test_project_with_log"""
      with tempfile.TemporaryDirectory() as dirpath:
        A__											=							Accelerator(log_with="""tensorboard"""						,      project_dir=lowerCamelCase__   )
        accelerator.init_trackers(lowerCamelCase__   )
        A__											=							{"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
        accelerator.log(lowerCamelCase__						,      step=0   )
        accelerator.end_training()
        # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
        # Names are randomly generated each time
        A__											=							list(filter(lambda lowerCamelCase__   : x.is_file()						,      Path(F"""{dirpath}/{project_name}"""   ).iterdir()   )   )[0]
        self.assertNotEqual(str(lowerCamelCase__   )						,      """"""   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							"""test_project_with_log"""
      with tempfile.TemporaryDirectory() as dirpath:
        A__											=							Accelerator(log_with="""tensorboard"""						,      project_dir=lowerCamelCase__   )
        accelerator.init_trackers(lowerCamelCase__   )
        A__											=							{"""tensor""": torch.tensor(1   )}
        accelerator.log(lowerCamelCase__						,      step=0   )
        accelerator.end_training()
        # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
        # Names are randomly generated each time
        A__											=							list(filter(lambda lowerCamelCase__   : x.is_file()						,      Path(F"""{dirpath}/{project_name}"""   ).iterdir()   )   )[0]
        # Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
        with open(lowerCamelCase__						,      """rb"""   ) as f:
          A__											=							f.read()
        A__											=							False
        while data:
          A__											=							struct.unpack("""Q"""						,      data[:8]   )

          A__											=							data[1_2 : 1_2 + int(header[0]   )]  # 8+4
          A__											=							data[1_2 + int(header[0]   ) + 4 :]
          A__											=							event_pba.Event()

          event.ParseFromString(lowerCamelCase__   )
          if event.HasField("""summary"""   ):
            for value in event.summary.value:
              if value.simple_value == 1.0 and value.tag == "tensor":
                A__											=							True
        self.assertTrue(lowerCamelCase__						,      """Converted tensor was not found in the log file!"""   )






    def  A    (self   ):


      """simple docstring"""
      with self.assertRaisesRegex(lowerCamelCase__						,      """Logging with `tensorboard` requires a `logging_dir`"""   ):
        A__											=							Accelerator(log_with="""tensorboard"""   )
      with tempfile.TemporaryDirectory() as dirpath:
        A__											=							Accelerator(log_with="""tensorboard"""						,      project_dir=lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							ProjectConfiguration(total_limit=3_0   )
      with tempfile.TemporaryDirectory() as dirpath:
        A__											=							Accelerator(log_with="""tensorboard"""						,      project_dir=lowerCamelCase__						,      project_config=lowerCamelCase__   )







@require_wandb
@mock.patch.dict(os.environ						, {"WANDB_MODE": "offline"})
class 					_UpperCamelCase		( __snake_case						, __snake_case):






    def  A    (self   ):


      """simple docstring"""
      super().setUp()
      # wandb let's us override where logs are stored to via the WANDB_DIR env var
      self.add_mocks(mock.patch.dict(os.environ						,      {"""WANDB_DIR""": self.tmpdir}   )   )






    @staticmethod
    def  A    (lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__ = True   ):


      """simple docstring"""
      A__											=							rF"""{section} ([\S\s]*?)\n\n"""
      if record:
        A__											=							rF"""Record: {pattern}"""
      A__											=							re.findall(lowerCamelCase__						,      lowerCamelCase__   )[0]
      # A config
      if section == "config" or section == "history":
        A__											=							re.findall(r"""\"([a-zA-Z0-9_.,]+)"""						,      lowerCamelCase__   )
        return {key: val for key, val in zip(cleaned_record[0::2]						,      cleaned_record[1::2]   )}
      # Everything else
      else:
        return dict(re.findall(r"""(\w+): \"([^\s]+)\""""						,      lowerCamelCase__   )   )






    @skip
    def  A    (self   ):


      """simple docstring"""
      A__											=							"""test_project_with_config"""
      A__											=							Accelerator(log_with="""wandb"""   )
      A__											=							{"""num_iterations""": 1_2, """learning_rate""": 1E-2, """some_boolean""": False, """some_string""": """some_value"""}
      A__											=							{"""wandb""": {"""tags""": ["""my_tag"""]}}
      accelerator.init_trackers(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__   )
      A__											=							{"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
      accelerator.log(lowerCamelCase__						,      step=0   )
      accelerator.end_training()
      # The latest offline log is stored at wandb/latest-run/*.wandb
      for child in Path(F"""{self.tmpdir}/wandb/latest-run"""   ).glob("""*"""   ):
        if child.is_file() and child.suffix == ".wandb":
          A__											=							subprocess.check_output(
              ["""wandb""", """sync""", """--view""", """--verbose""", str(lowerCamelCase__   )]						,      env=os.environ.copy()   ).decode("""utf8"""						,      """ignore"""   )
          break

        # Check HPS through careful parsing and cleaning
      A__											=							self.parse_log(lowerCamelCase__						,      """config"""   )
      self.assertEqual(logged_items["""num_iterations"""]						,      """12"""   )
      self.assertEqual(logged_items["""learning_rate"""]						,      """0.01"""   )
      self.assertEqual(logged_items["""some_boolean"""]						,      """false"""   )
      self.assertEqual(logged_items["""some_string"""]						,      """some_value"""   )
      self.assertEqual(logged_items["""some_string"""]						,      """some_value"""   )

      # Run tags
      A__											=							self.parse_log(lowerCamelCase__						,      """run"""						,      lowerCamelCase__   )
      self.assertEqual(logged_items["""tags"""]						,      """my_tag"""   )

      # Actual logging
      A__											=							self.parse_log(lowerCamelCase__						,      """history"""   )
      self.assertEqual(logged_items["""total_loss"""]						,      """0.1"""   )
      self.assertEqual(logged_items["""iteration"""]						,      """1"""   )
      self.assertEqual(logged_items["""my_text"""]						,      """some_value"""   )
      self.assertEqual(logged_items["""_step"""]						,      """0"""   )






def 						_SCREAMING_SNAKE_CASE							( self     :	Optional[Any]		,				UpperCamelCase     :	str		,				UpperCamelCase     :	str    ):
  A__											=							run_name
  A__											=							OfflineExperiment(project_name=UpperCamelCase		,				offline_directory=UpperCamelCase    )
  logger.info(F"""Initialized offline CometML project {self.run_name}"""    )
  logger.info("""Make sure to log any initial configurations with `self.store_init_configuration` before training!"""    )







@require_comet_ml
@mock.patch.object(__snake_case						, "__init__"						, __snake_case)
class 					_UpperCamelCase		( unittest.TestCase):






    @staticmethod
    def  A    (lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__ = False   ):


      """simple docstring"""
      for log in log_list:
        A__											=							json.loads(lowerCamelCase__   )["""payload"""]
        if is_param and "param" in j.keys():
          if j["param"]["paramName"] == key:
            return j["param"]["paramValue"]
        if "log_other" in j.keys():
          if j["log_other"]["key"] == key:
            return j["log_other"]["val"]
        if "metric" in j.keys():
          if j["metric"]["metricName"] == key:
            return j["metric"]["metricValue"]






    def  A    (self   ):


      """simple docstring"""
      with tempfile.TemporaryDirectory() as d:
        A__											=							CometMLTracker("""test_project_with_config"""						,      lowerCamelCase__   )
        A__											=							Accelerator(log_with=lowerCamelCase__   )
        A__											=							{"""num_iterations""": 1_2, """learning_rate""": 1E-2, """some_boolean""": False, """some_string""": """some_value"""}
        accelerator.init_trackers(lowerCamelCase__						,      lowerCamelCase__   )
        accelerator.end_training()
        A__											=							os.listdir(lowerCamelCase__   )[0]  # Comet is nice, it's just a zip file here
        # We parse the raw logs
        A__											=							os.path.join(lowerCamelCase__						,      lowerCamelCase__   )
        A__											=							zipfile.ZipFile(lowerCamelCase__						,      """r"""   )
        A__											=							archive.open("""messages.json"""   ).read().decode("""utf-8"""   )
      A__											=							log.split("""\n"""   )[:-1]
      self.assertEqual(self.get_value_from_key(lowerCamelCase__						,      """num_iterations"""						,      lowerCamelCase__   )						,      1_2   )
      self.assertEqual(self.get_value_from_key(lowerCamelCase__						,      """learning_rate"""						,      lowerCamelCase__   )						,      0.0_1   )
      self.assertEqual(self.get_value_from_key(lowerCamelCase__						,      """some_boolean"""						,      lowerCamelCase__   )						,      lowerCamelCase__   )
      self.assertEqual(self.get_value_from_key(lowerCamelCase__						,      """some_string"""						,      lowerCamelCase__   )						,      """some_value"""   )






    def  A    (self   ):


      """simple docstring"""
      with tempfile.TemporaryDirectory() as d:
        A__											=							CometMLTracker("""test_project_with_config"""						,      lowerCamelCase__   )
        A__											=							Accelerator(log_with=lowerCamelCase__   )
        accelerator.init_trackers(lowerCamelCase__   )
        A__											=							{"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
        accelerator.log(lowerCamelCase__						,      step=0   )
        accelerator.end_training()
        A__											=							os.listdir(lowerCamelCase__   )[0]  # Comet is nice, it's just a zip file here
        # We parse the raw logs
        A__											=							os.path.join(lowerCamelCase__						,      lowerCamelCase__   )
        A__											=							zipfile.ZipFile(lowerCamelCase__						,      """r"""   )
        A__											=							archive.open("""messages.json"""   ).read().decode("""utf-8"""   )
      A__											=							log.split("""\n"""   )[:-1]
      self.assertEqual(self.get_value_from_key(lowerCamelCase__						,      """curr_step"""						,      lowerCamelCase__   )						,      0   )
      self.assertEqual(self.get_value_from_key(lowerCamelCase__						,      """total_loss"""   )						,      0.1   )
      self.assertEqual(self.get_value_from_key(lowerCamelCase__						,      """iteration"""   )						,      1   )
      self.assertEqual(self.get_value_from_key(lowerCamelCase__						,      """my_text"""   )						,      """some_value"""   )







class 					_UpperCamelCase		( __snake_case):
    __lowerCamelCase         =  [
        "total_loss",
        "iteration",
        "my_text",
        "learning_rate",
        "num_iterations",
        "some_boolean",
        "some_string",
    ]

    __lowerCamelCase         =  "my_custom_tracker"
    __lowerCamelCase         =  False






    def __init__(self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							open(F"""{dir}/log.csv"""						,      """w+"""   )
      A__											=							csv.DictWriter(self.f						,      fieldnames=self._col_names   )
      self.writer.writeheader()






    @property
    def  A    (self   ):


      """simple docstring"""
      return self.writer






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      logger.info("""Call init"""   )
      self.writer.writerow(lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      logger.info("""Call log"""   )
      self.writer.writerow(lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      self.f.close()







class 					_UpperCamelCase		( unittest.TestCase):






    def  A    (self   ):


      """simple docstring"""
      with tempfile.TemporaryDirectory() as d:
        A__											=							MyCustomTracker(lowerCamelCase__   )
        A__											=							Accelerator(log_with=lowerCamelCase__   )
        A__											=							{"""num_iterations""": 1_2, """learning_rate""": 1E-2, """some_boolean""": False, """some_string""": """some_value"""}
        accelerator.init_trackers("""Some name"""						,      lowerCamelCase__   )
        accelerator.end_training()
        with open(F"""{d}/log.csv"""						,      """r"""   ) as f:
          A__											=							csv.DictReader(lowerCamelCase__   )
          A__											=							next(lowerCamelCase__   )
          A__											=							{
              """total_loss""": """""",
              """iteration""": """""",
              """my_text""": """""",
              """learning_rate""": """0.01""",
              """num_iterations""": """12""",
              """some_boolean""": """False""",
              """some_string""": """some_value""",
          }
          self.assertDictEqual(lowerCamelCase__						,      lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      with tempfile.TemporaryDirectory() as d:
        A__											=							MyCustomTracker(lowerCamelCase__   )
        A__											=							Accelerator(log_with=lowerCamelCase__   )
        accelerator.init_trackers("""Some name"""   )
        A__											=							{"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
        accelerator.log(lowerCamelCase__						,      step=0   )
        accelerator.end_training()
        with open(F"""{d}/log.csv"""						,      """r"""   ) as f:
          A__											=							csv.DictReader(lowerCamelCase__   )
          A__											=							next(lowerCamelCase__   )
          A__											=							{
              """total_loss""": """0.1""",
              """iteration""": """1""",
              """my_text""": """some_value""",
              """learning_rate""": """""",
              """num_iterations""": """""",
              """some_boolean""": """""",
              """some_string""": """""",
          }
          self.assertDictEqual(lowerCamelCase__						,      lowerCamelCase__   )












"""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 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Any		,				UpperCamelCase     :	Optional[Any]		,				UpperCamelCase     :	Dict    ):
  # Initialise PyTorch model
  A__											=							FNetConfig.from_json_file(UpperCamelCase    )
  print(F"""Building PyTorch model from configuration: {config}"""    )
  A__											=							FNetForPreTraining(UpperCamelCase    )

  A__											=							restore_checkpoint(UpperCamelCase		,				UpperCamelCase    )
  A__											=							checkpoint_dict["""target"""]

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

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

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

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

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

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

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

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

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

  # Load State Dict
  fnet_pretraining_model.load_state_dict(UpperCamelCase    )

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


if __name__ == "__main__":
      lowerCamelCase__			=   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.")
      lowerCamelCase__			=   parser.parse_args()
      convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




