
'''simple docstring'''




import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean

import pandas as pd
import torch
from tqdm import tqdm

import transformers


lowercase			:				Optional[Any]    		=			float('nan')





class  A				:





     def __init__(							self						,			SCREAMING_SNAKE_CASE	) ->				Optional[int]:






         """simple docstring"""






         A       :   Dict					=						sys.stdout
         A       :   List[Any]					=						open(SCREAMING_SNAKE_CASE__						,			'''a'''	)





     def __getattr__(							self						,			SCREAMING_SNAKE_CASE	) ->				Optional[int]:






         """simple docstring"""






         return getattr(self.stdout						,			SCREAMING_SNAKE_CASE__	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






         self.stdout.write(SCREAMING_SNAKE_CASE__	)
         # strip tqdm codes
         self.file.write(re.sub(R'''^.*\r'''						,			''''''						,			SCREAMING_SNAKE_CASE__						,			0						,			re.M	)	)







def 		lowerCAmelCase_				(   snake_case__=80		,				snake_case__=False						):




    '''simple docstring'''



    A       :   List[str]					=						[]

    # deal with critical env vars
    A       :   Any					=						['''CUDA_VISIBLE_DEVICES''']
    for key in env_keys:
        A       :   Optional[Any]					=						os.environ.get(a_		,				a_						)
        if val is not None:
            cmd.append(F'{key}={val}'						)

    # python executable (not always needed if the script is executable)
    A       :   Optional[Any]					=						sys.executable if full_python_path else sys.executable.split('''/'''						)[-1]
    cmd.append(a_						)

    # now the normal args
    cmd += list(map(shlex.quote		,				sys.argv						)						)

    # split up into up to MAX_WIDTH lines with shell multi-line escapes
    A       :   Optional[int]					=						[]
    A       :   Optional[int]					=						''''''
    while len(a_						) > 0:
        current_line += F'{cmd.pop(0						)} '
        if len(a_						) == 0 or len(a_						) + len(cmd[0]						) + 1 > max_width - 1:
            lines.append(a_						)
            A       :   Tuple					=						''''''
    return "\\\n".join(a_						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Tuple					=						re.sub(R'''[\\\n]+'''		,				''' '''		,				args.base_cmd						)

    # remove --output_dir if any and set our own
    A       :   Dict					=						re.sub('''--output_dir\s+[^\s]+'''		,				''''''		,				args.base_cmd						)
    args.base_cmd += F' --output_dir {output_dir}'

    # ensure we have --overwrite_output_dir
    A       :   int					=						re.sub('''--overwrite_output_dir\s+'''		,				''''''		,				args.base_cmd						)
    args.base_cmd += " --overwrite_output_dir"

    return [sys.executable] + shlex.split(args.base_cmd						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    if 0:
        import random
        from time import sleep

        sleep(0						)
        return dict(
            {k: random.uniform(0		,				100						) for k in metric_keys}		,				**{target_metric_key: random.choice([nan, 10.31, 1_00.2, 55.66_66, 2_22.22_22_22_22]						)}		,				)

    A       :   Tuple					=						subprocess.run(a_		,				capture_output=a_		,				text=a_						)

    if verbose:
        print('''STDOUT'''		,				result.stdout						)
        print('''STDERR'''		,				result.stderr						)

    # save the streams
    A       :   str					=						variation.replace(''' '''		,				'''-'''						)
    with open(Path(a_						) / F'log.{prefix}.stdout.txt'		,				'''w'''						) as f:
        f.write(result.stdout						)
    with open(Path(a_						) / F'log.{prefix}.stderr.txt'		,				'''w'''						) as f:
        f.write(result.stderr						)

    if result.returncode != 0:
        if verbose:
            print('''failed'''						)
        return {target_metric_key: nan}

    with io.open(F'{output_dir}/all_results.json'		,				'''r'''		,				encoding='''utf-8'''						) as f:
        A       :   Union[str, Any]					=						json.load(a_						)

    # filter out just the keys we want
    return {k: v for k, v in metrics.items() if k in metric_keys}







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				):




    '''simple docstring'''



    A       :   Optional[int]					=						[]
    A       :   Optional[Any]					=						[]
    A       :   Tuple					=						F'{id}: {variation:<{longest_variation_len}}'
    A       :   List[str]					=						F'{preamble}: '
    A       :   Any					=						set(report_metric_keys + [target_metric_key]						)
    for i in tqdm(range(a_						)		,				desc=a_		,				leave=a_						):
        A       :   Union[str, Any]					=						process_run_single(
            a_		,				a_		,				a_		,				a_		,				a_		,				a_		,				a_						)
        A       :   Tuple					=						single_run_metrics[target_metric_key]
        if not math.isnan(a_						):
            metrics.append(a_						)
            results.append(a_						)
            outcome += "✓"
        else:
            outcome += "✘"
    A       :   Tuple					=						F'\33[2K\r{outcome}'
    if len(a_						) > 0:
        A       :   List[str]					=						{k: fmean([x[k] for x in metrics]						) for k in metrics[0].keys()}
        A       :   int					=						round(mean_metrics[target_metric_key]		,				2						)
        A       :   List[Any]					=						F'{outcome} {mean_target}'
        if len(a_						) > 1:
            results_str += F' {tuple(round(a_		,				2						) for x in results						)}'
        print(a_						)
        A       :   List[str]					=						variation
        return mean_metrics
    else:
        print(a_						)
        return {variation_key: variation, target_metric_key: nan}







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    A       :   List[str]					=						torch.cuda.get_device_properties(torch.device('''cuda'''						)						)
    return F'\nDatetime    : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"						)}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch       : {torch.__version__}\ncuda        : {torch.version.cuda}\npython      : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB\n'







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   List[str]					=						pd.DataFrame(a_						)
    A       :   List[Any]					=						'''variation'''
    A       :   Union[str, Any]					=						'''diff_%'''

    A       :   Dict					=						nan
    if base_variation is not None and len(df[df[variation_key] == base_variation]						):
        # this may still return nan
        A       :   Tuple					=						df.loc[df[variation_key] == base_variation][target_metric_key].item()
    if math.isnan(a_						):
        # as a fallback, use the minimal value as the sentinel
        A       :   Tuple					=						df.loc[df[target_metric_key] != nan][target_metric_key].min()

    # create diff column if possible
    if not math.isnan(a_						):
        A       :   Optional[int]					=						df.apply(
            lambda snake_case__						: round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value						)
            if not math.isnan(r[target_metric_key]						)
            else 0		,				axis='''columns'''		,				)

    # re-order columns
    A       :   List[str]					=						[variation_key, target_metric_key, diff_key, *report_metric_keys]
    A       :   Optional[Any]					=						df.reindex(a_		,				axis='''columns'''						)  # reorder cols

    # capitalize
    A       :   Union[str, Any]					=						df.rename(str.capitalize		,				axis='''columns'''						)

    # make the cols as narrow as possible
    A       :   int					=						df.rename(lambda snake_case__						: c.replace('''_'''		,				'''<br>'''						)		,				axis='''columns'''						)
    A       :   Optional[int]					=						df.rename(lambda snake_case__						: c.replace('''_'''		,				'''\n'''						)		,				axis='''columns'''						)

    A       :   str					=						['''''', '''Copy between the cut-here-lines and paste as is to github or a forum''']
    report += ["----------8<-----------------8<--------"]
    report += ["*** Results:", df_github.to_markdown(index=a_		,				floatfmt='''.2f'''						)]
    report += ["```"]
    report += ["*** Setup:", get_versions()]
    report += ["*** The benchmark command line was:", get_original_command()]
    report += ["```"]
    report += ["----------8<-----------------8<--------"]
    report += ["*** Results (console):", df_console.to_markdown(index=a_		,				floatfmt='''.2f'''						)]

    print('''\n\n'''.join(a_						)						)







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    A       :   List[str]					=						argparse.ArgumentParser()
    parser.add_argument(
        '''--base-cmd'''		,				default=a_		,				type=a_		,				required=a_		,				help='''Base cmd'''		,				)
    parser.add_argument(
        '''--variations'''		,				default=a_		,				type=a_		,				nargs='''+'''		,				required=a_		,				help='''Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\''''		,				)
    parser.add_argument(
        '''--base-variation'''		,				default=a_		,				type=a_		,				help='''Baseline variation to compare to. if None the minimal target value will be used to compare against'''		,				)
    parser.add_argument(
        '''--target-metric-key'''		,				default=a_		,				type=a_		,				required=a_		,				help='''Target metric key in output_dir/all_results.json, e.g., train_samples_per_second'''		,				)
    parser.add_argument(
        '''--report-metric-keys'''		,				default=''''''		,				type=a_		,				help='''Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., \'train_loss train_samples'''		,				)
    parser.add_argument(
        '''--repeat-times'''		,				default=1		,				type=a_		,				help='''How many times to re-run each variation - an average will be reported'''		,				)
    parser.add_argument(
        '''--output_dir'''		,				default='''output_benchmark'''		,				type=a_		,				help='''The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked'''		,				)
    parser.add_argument(
        '''--verbose'''		,				default=a_		,				action='''store_true'''		,				help='''Whether to show the outputs of each run or just the benchmark progress'''		,				)
    A       :   int					=						parser.parse_args()

    A       :   Tuple					=						args.output_dir
    Path(a_						).mkdir(exist_ok=a_						)
    A       :   int					=						get_base_command(a_		,				a_						)

    # split each dimension into its --foo variations
    A       :   List[Any]					=						[list(map(str.strip		,				re.split(R'''\|'''		,				a_						)						)						) for x in args.variations]
    # build a cartesian product of dimensions and convert those back into cmd-line arg strings,
    # while stripping white space for inputs that were empty
    A       :   Any					=						list(map(str.strip		,				map(''' '''.join		,				itertools.product(*a_						)						)						)						)
    A       :   Union[str, Any]					=						max(len(a_						) for x in variations						)

    # split wanted keys
    A       :   int					=						args.report_metric_keys.split()

    # capture prints into a log file for convenience
    A       :   Any					=						F'benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S"						)}.txt'
    print(F'\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt'						)
    print(F'and this script\'s output is also piped into {report_fn}'						)

    A       :   Any					=						Tee(a_						)

    print(F'\n*** Running {len(a_						)} benchmarks:'						)
    print(F'Base command: {" ".join(a_						)}'						)

    A       :   Tuple					=						'''variation'''
    A       :   str					=						[]
    for id, variation in enumerate(tqdm(a_		,				desc='''Total completion: '''		,				leave=a_						)						):
        A       :   Tuple					=						base_cmd + variation.split()
        results.append(
            process_run(
                id + 1		,				a_		,				a_		,				a_		,				a_		,				args.target_metric_key		,				a_		,				args.repeat_times		,				a_		,				args.verbose		,				)						)

    process_results(a_		,				args.target_metric_key		,				a_		,				args.base_variation		,				a_						)


if __name__ == "__main__":
      main()











"""simple docstring"""
import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()



def      __lowerCamelCase   (							a_     :		Union[str, Any] ,   a_     :		Tuple ,   a_     :		str=None							)			->      Union[str, Any]:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
      __SCREAMING_SNAKE_CASE   :Dict						=  nn.Parameter(a_							)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  nn.Parameter(a_							)



def      __lowerCamelCase   (							a_     :		Dict ,   a_     :		str ,   a_     :		Optional[int]							)			->      Any:
      # set torch weights for 1-to-1 comparison
      __SCREAMING_SNAKE_CASE   :List[Any]						=  np.asarray(weights[0]							)
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.asarray(weights[1]							)
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.asarray(weights[2]							)

      set_param(
          torch_layer.self_attention.query_key ,   torch.tensor(a_							).transpose(1 ,   2							).contiguous().view(-1 ,   a_							) ,   )
      set_param(
          torch_layer.self_attention.value ,   torch.tensor(a_							).transpose(1 ,   2							).contiguous().view(-1 ,   a_							) ,   )
      set_param(
          torch_layer.output.dense ,   torch.tensor(a_							).view(-1 ,   a_							).contiguous().transpose(0 ,   1							) ,   )



def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		Dict ,   a_     :		List[str]							)			->      Union[str, Any]:
      # set torch weights for 1-to-1 comparison
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(weights[0]							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(weights[1]							)
      __SCREAMING_SNAKE_CASE   :Any						=  np.asarray(weights[2]							)
      __SCREAMING_SNAKE_CASE   :Dict						=  np.asarray(weights[3]							)

      set_param(
          torch_layer.self_attention.query ,   torch.tensor(a_							).transpose(1 ,   2							).contiguous().view(-1 ,   a_							) ,   )
      set_param(
          torch_layer.self_attention.key ,   torch.tensor(a_							).transpose(1 ,   2							).contiguous().view(-1 ,   a_							) ,   )
      set_param(
          torch_layer.self_attention.value ,   torch.tensor(a_							).transpose(1 ,   2							).contiguous().view(-1 ,   a_							) ,   )
      set_param(
          torch_layer.output.dense ,   torch.tensor(a_							).view(-1 ,   a_							).contiguous().transpose(0 ,   1							) ,   )



def      __lowerCamelCase   (							a_     :		Any ,   a_     :		List[str] ,   a_     :		Optional[int]							)			->      Union[str, Any]:
      # layernorm 1
      __SCREAMING_SNAKE_CASE   :Any						=  weights[0][0][0]
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(layer_norm_a[0]							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(layer_norm_a[1]							)
      set_param(
          torch_block.attention.layer_norm ,   torch.tensor(a_							) ,   torch.tensor(a_							) ,   )

      # lsh weights + output
      __SCREAMING_SNAKE_CASE   :List[Any]						=  weights[0][1]
      if len(a_							) < 4:
            set_layer_weights_in_torch_lsh(a_ ,   torch_block.attention ,   a_							)
      else:
            set_layer_weights_in_torch_local(a_ ,   torch_block.attention ,   a_							)

      # intermediate weighs
      __SCREAMING_SNAKE_CASE   :List[Any]						=  weights[2][0][1][2]

      # Chunked Feed Forward
      if len(a_							) == 4:
            __SCREAMING_SNAKE_CASE   :List[str]						=  intermediate_weights[2]

      # layernorm 2
      __SCREAMING_SNAKE_CASE   :Tuple						=  np.asarray(intermediate_weights[0][0]							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(intermediate_weights[0][1]							)
      set_param(
          torch_block.feed_forward.layer_norm ,   torch.tensor(a_							) ,   torch.tensor(a_							) ,   )

      # intermediate dense
      __SCREAMING_SNAKE_CASE   :int						=  np.asarray(intermediate_weights[1][0]							)
      __SCREAMING_SNAKE_CASE   :int						=  np.asarray(intermediate_weights[1][1]							)
      set_param(
          torch_block.feed_forward.dense.dense ,   torch.tensor(a_							).transpose(0 ,   1							).contiguous() ,   torch.tensor(a_							) ,   )

      # intermediate out
      __SCREAMING_SNAKE_CASE   :str						=  np.asarray(intermediate_weights[4][0]							)
      __SCREAMING_SNAKE_CASE   :str						=  np.asarray(intermediate_weights[4][1]							)
      set_param(
          torch_block.feed_forward.output.dense ,   torch.tensor(a_							).transpose(0 ,   1							).contiguous() ,   torch.tensor(a_							) ,   )



def      __lowerCamelCase   (							a_     :		List[str] ,   a_     :		str ,   a_     :		List[Any]							)			->      Optional[Any]:
      # reformer model
      __SCREAMING_SNAKE_CASE   :Dict						=  torch_model.reformer

      # word embeds
      __SCREAMING_SNAKE_CASE   :List[Any]						=  np.asarray(weights[1]							)
      set_param(
          torch_model_reformer.embeddings.word_embeddings ,   torch.tensor(a_							) ,   )

      if isinstance(weights[3] ,   a_							):
            __SCREAMING_SNAKE_CASE   :List[Any]						=  torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights							)							):
                  __SCREAMING_SNAKE_CASE   :List[str]						=  np.asarray(weights[3][emb_idx][0]							)
                  assert (
                      position_embeddings.weights[emb_idx].shape == emb_weights.shape
                  ), f'''{position_embeddings[emb_idx]} emb does not match'''
                  __SCREAMING_SNAKE_CASE   :str						=  nn.Parameter(torch.tensor(a_							)							)

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  weights[5]
      assert len(torch_model_reformer.encoder.layers							) * 4 == len(
          a_							), "HF and trax model do not have the same number of layers"
      for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers							):
            __SCREAMING_SNAKE_CASE   :Dict						=  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(a_ ,   a_ ,   a_							)

      # output layer norm
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.asarray(weights[7][0]							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  np.asarray(weights[7][1]							)
      set_param(
          torch_model_reformer.encoder.layer_norm ,   torch.tensor(a_							) ,   torch.tensor(a_							) ,   )

      # output embeddings
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.asarray(weights[9][0]							)
      __SCREAMING_SNAKE_CASE   :str						=  np.asarray(weights[9][1]							)
      set_param(
          torch_model.lm_head.decoder ,   torch.tensor(a_							).transpose(0 ,   1							).contiguous() ,   torch.tensor(a_							) ,   )



def      __lowerCamelCase   (							a_     :		Any ,   a_     :		Dict ,   a_     :		Dict							)			->      Tuple:
      # Initialise PyTorch model
      __SCREAMING_SNAKE_CASE   :List[str]						=  ReformerConfig.from_json_file(a_							)
      print(f'''Building PyTorch model from configuration: {config}'''							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  ReformerModelWithLMHead(a_							)

      with open(a_ ,   '''rb'''							) as f:
            __SCREAMING_SNAKE_CASE   :Any						=  pickle.load(a_							)['''weights''']

      set_model_weights_in_torch(a_ ,   a_ ,   config.hidden_size							)

      # Save pytorch-model
      print(f'''Save PyTorch model to {pytorch_dump_path}'''							)
      torch.save(model.state_dict() ,   a_							)


if __name__ == "__main__":
  lowerCamelCase_								=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained Reformer model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase_								=		parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)