






# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
#     ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
#     --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
#     --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
#    1. --tf32 0 --fp16 0
#    2. --tf32 0 --fp16 1
#    3. --tf32 0 --bf16 1
#    4. --tf32 1 --fp16 0
#    5. --tf32 1 --fp16 1
#    6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
#    --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
#    --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
#    1. --tf32 0
#    2. --tf32 0 --fp16
#    3. --tf32 0 --bf16
#    4. --tf32 1
#    5. --tf32 1 --fp16
#    6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
#    `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation       |     Train |   Diff |   Train |
# |                 |   samples |      % |    loss |
# |                 |       per |        |         |
# |                 |    second |        |         |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0        |    285.11 |      0 |    2.51 |
# | --tf32 1        |    342.09 |     20 |    2.51 |
# | --fp16 --tf32 0 |    423.49 |     49 |    2.51 |
# | --fp16 --tf32 1 |    423.13 |     48 |    2.51 |
# | --bf16 --tf32 0 |    416.80 |     46 |    2.52 |
# | --bf16 --tf32 1 |    415.87 |     46 |    2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
#    --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#

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


SCREAMING_SNAKE_CASE__				     =			float('''nan''')






class 			_UpperCamelCase:

		def __init__(					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     sys.stdout
					__a						:  Tuple									=     open(SCREAMING_SNAKE_CASE__    ,       'a'    )

		def __getattr__(					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ):



					'''simple docstring'''



					return getattr(self.stdout    ,       SCREAMING_SNAKE_CASE__    )



		def    __lowerCAmelCase  (					self		:    List[str]    ,       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        UpperCAmelCase__		(						lowerCamelCase_       :       Dict=8_0   ,  lowerCamelCase_       :       int=False				):
			__a						:  Tuple									=     []

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

    # python executable (not always needed if the script is executable)
			__a						:  Union[str, Any]									=     sys.executable if full_python_path else sys.executable.split('/'				)[-1]
			cmd.append(lowerCamelCase_				)

			# 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						:  List[Any]									=     []
			__a						:  Optional[Any]									=     ''
			while len(lowerCamelCase_				) > 0:
						current_line += f'''{cmd.pop(0				)} '''
						if len(lowerCamelCase_				) == 0 or len(lowerCamelCase_				) + len(cmd[0]				) + 1 > max_width - 1:
									lines.append(lowerCamelCase_				)
									__a						:  List[Any]									=     ''
			return "\\\n".join(lowerCamelCase_				)

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       Tuple				):
			# unwrap multi-line input
			__a						:  Dict									=     re.sub(R'[\\\n]+'   ,  ' '   ,  args.base_cmd				)

			# remove --output_dir if any and set our own
			__a						:  Union[str, Any]									=     re.sub('--output_dir\s+[^\s]+'   ,  ''   ,  args.base_cmd				)
			args.base_cmd += f''' --output_dir {output_dir}'''

			# ensure we have --overwrite_output_dir
			__a						:  List[Any]									=     re.sub('--overwrite_output_dir\s+'   ,  ''   ,  args.base_cmd				)
			args.base_cmd += " --overwrite_output_dir"

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

def        UpperCAmelCase__		(						lowerCamelCase_       :       Optional[Any]   ,  lowerCamelCase_       :       Union[str, Any]   ,  lowerCamelCase_       :       Optional[int]   ,  lowerCamelCase_       :       str   ,  lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       List[str]   ,  lowerCamelCase_       :       Union[str, Any]				):
			# Enable to debug everything but the run itself, to do it fast and see the progress.
			# This is useful for debugging the output formatting quickly - we can remove it later once
			# everybody is happy with the output
			if 0:
						import random
						from time import sleep

						sleep(0				)
						return dict(
						    {k: random.uniform(0   ,  1_0_0				) for k in metric_keys}   ,  **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.22222222]				)}   ,  )

			__a						:  Dict									=     subprocess.run(lowerCamelCase_   ,  capture_output=lowerCamelCase_   ,  text=lowerCamelCase_				)

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

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

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

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

def        UpperCAmelCase__		(						):
			__a						:  List[str]									=     torch.cuda.get_device_properties(torch.device('cuda'				)				)
			return f'''
Datetime    : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"				)}

Software:
transformers: {transformers.__version__}
torch       : {torch.__version__}
cuda        : {torch.version.cuda}
python      : {platform.python_version()}

Hardware:
{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**3_0:0.2f}GB
'''

def        UpperCAmelCase__		(						lowerCamelCase_       :       Tuple   ,  lowerCamelCase_       :       Optional[Any]   ,  lowerCamelCase_       :       Optional[int]   ,  lowerCamelCase_       :       Optional[int]   ,  lowerCamelCase_       :       List[Any]				):
			__a						:  Any									=     pd.DataFrame(lowerCamelCase_				)
			__a						:  Optional[int]									=     'variation'
			__a						:  Optional[Any]									=     'diff_%'

			__a						:  Union[str, Any]									=     nan
			if base_variation is not None and len(df[df[variation_key] == base_variation]				):
						# this may still return nan
						__a						:  str									=     df.loc[df[variation_key] == base_variation][target_metric_key].item()
			if math.isnan(lowerCamelCase_				):
						# as a fallback, use the minimal value as the sentinel
						__a						:  Optional[Any]									=     df.loc[df[target_metric_key] != nan][target_metric_key].min()

			# create diff column if possible
			if not math.isnan(lowerCamelCase_				):
						__a						:  str									=     df.apply(
						    lambda lowerCamelCase_				: round(1_0_0 * (r[target_metric_key] - sentinel_value) / sentinel_value				)
						    if not math.isnan(r[target_metric_key]				)
						    else 0   ,  axis='columns'   ,  )

			# re-order columns
			__a						:  int									=     [variation_key, target_metric_key, diff_key, *report_metric_keys]
			__a						:  List[str]									=     df.reindex(lowerCamelCase_   ,  axis='columns'				)  # reorder cols

			# capitalize
			__a						:  Dict									=     df.rename(str.capitalize   ,  axis='columns'				)

			# make the cols as narrow as possible
			__a						:  Dict									=     df.rename(lambda lowerCamelCase_				: c.replace('_'   ,  '<br>'				)   ,  axis='columns'				)
			__a						:  Optional[Any]									=     df.rename(lambda lowerCamelCase_				: 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=lowerCamelCase_   ,  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=lowerCamelCase_   ,  floatfmt='.2f'				)]

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

def        UpperCAmelCase__		(						):
			__a						:  Optional[int]									=     argparse.ArgumentParser()
			parser.add_argument(
			    '--base-cmd'   ,  default=lowerCamelCase_   ,  type=lowerCamelCase_   ,  required=lowerCamelCase_   ,  help='Base cmd'   ,  )
			parser.add_argument(
			    '--variations'   ,  default=lowerCamelCase_   ,  type=lowerCamelCase_   ,  nargs='+'   ,  required=lowerCamelCase_   ,  help='Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\''   ,  )
			parser.add_argument(
			    '--base-variation'   ,  default=lowerCamelCase_   ,  type=lowerCamelCase_   ,  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=lowerCamelCase_   ,  type=lowerCamelCase_   ,  required=lowerCamelCase_   ,  help='Target metric key in output_dir/all_results.json, e.g., train_samples_per_second'   ,  )
			parser.add_argument(
			    '--report-metric-keys'   ,  default=''   ,  type=lowerCamelCase_   ,  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=lowerCamelCase_   ,  help='How many times to re-run each variation - an average will be reported'   ,  )
			parser.add_argument(
			    '--output_dir'   ,  default='output_benchmark'   ,  type=lowerCamelCase_   ,  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=lowerCamelCase_   ,  action='store_true'   ,  help='Whether to show the outputs of each run or just the benchmark progress'   ,  )
			__a						:  int									=     parser.parse_args()

			__a						:  str									=     args.output_dir
			Path(lowerCamelCase_				).mkdir(exist_ok=lowerCamelCase_				)
			__a						:  int									=     get_base_command(lowerCamelCase_   ,  lowerCamelCase_				)

			# split each dimension into its --foo variations
			__a						:  List[str]									=     [list(map(str.strip   ,  re.split(R'\|'   ,  lowerCamelCase_				)				)				) 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(*lowerCamelCase_				)				)				)				)
			__a						:  Any									=     max(len(lowerCamelCase_				) for x in variations				)

			# split wanted keys
			__a						:  Dict									=     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						:  List[str]									=     Tee(lowerCamelCase_				)

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

			__a						:  List[Any]									=     'variation'
			__a						:  Dict									=     []
			for id, variation in enumerate(tqdm(lowerCamelCase_   ,  desc='Total completion: '   ,  leave=lowerCamelCase_				)				):
						__a						:  int									=     base_cmd + variation.split()
						results.append(
						    process_run(
						        id + 1   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  args.target_metric_key   ,  lowerCamelCase_   ,  args.repeat_times   ,  lowerCamelCase_   ,  args.verbose   ,  )				)

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


if __name__ == "__main__":
							main()













import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.roberta.modeling_flax_roberta import (
			    FlaxRobertaForCausalLM,
			    FlaxRobertaForMaskedLM,
			    FlaxRobertaForMultipleChoice,
			    FlaxRobertaForQuestionAnswering,
			    FlaxRobertaForSequenceClassification,
			    FlaxRobertaForTokenClassification,
			    FlaxRobertaModel,
			)




class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						def __init__(self:					Optional[int] ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					List[str]=13 ,					__UpperCAmelCase:					Any=7 ,					__UpperCAmelCase:					List[str]=True ,					__UpperCAmelCase:					Optional[int]=True ,					__UpperCAmelCase:					Dict=True ,					__UpperCAmelCase:					Optional[Any]=True ,					__UpperCAmelCase:					Optional[int]=99 ,					__UpperCAmelCase:					Optional[Any]=32 ,					__UpperCAmelCase:					int=5 ,					__UpperCAmelCase:					Dict=4 ,					__UpperCAmelCase:					Optional[int]=37 ,					__UpperCAmelCase:					int="gelu" ,					__UpperCAmelCase:					Tuple=0.1 ,					__UpperCAmelCase:					Any=0.1 ,					__UpperCAmelCase:					Union[str, Any]=512 ,					__UpperCAmelCase:					Optional[Any]=16 ,					__UpperCAmelCase:					List[Any]=2 ,					__UpperCAmelCase:					str=0.02 ,					__UpperCAmelCase:					int=4 ,					)    ->     str:




											'''simple docstring'''



											__a					:						Tuple      =					parent
											__a					:						int      =					batch_size
											__a					:						Optional[int]      =					seq_length
											__a					:						List[Any]      =					is_training
											__a					:						Tuple      =					use_attention_mask
											__a					:						Optional[int]      =					use_token_type_ids
											__a					:						Tuple      =					use_labels
											__a					:						str      =					vocab_size
											__a					:						Union[str, Any]      =					hidden_size
											__a					:						List[str]      =					num_hidden_layers
											__a					:						Optional[int]      =					num_attention_heads
											__a					:						Any      =					intermediate_size
											__a					:						Any      =					hidden_act
											__a					:						List[str]      =					hidden_dropout_prob
											__a					:						Dict      =					attention_probs_dropout_prob
											__a					:						Tuple      =					max_position_embeddings
											__a					:						Optional[int]      =					type_vocab_size
											__a					:						Tuple      =					type_sequence_label_size
											__a					:						List[Any]      =					initializer_range
											__a					:						int      =					num_choices



						def        UpperCAmelCase__					(self:					Tuple   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						List[Any]      =					ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size   )

											__a					:						Dict      =					None
											if self.use_attention_mask:
																__a					:						List[Any]      =					random_attention_mask([self.batch_size, self.seq_length]   )

											__a					:						Optional[int]      =					None
											if self.use_token_type_ids:
																__a					:						Dict      =					ids_tensor([self.batch_size, self.seq_length] ,					self.type_vocab_size   )

											__a					:						Dict      =					RobertaConfig(
											    vocab_size=self.vocab_size ,					hidden_size=self.hidden_size ,					num_hidden_layers=self.num_hidden_layers ,					num_attention_heads=self.num_attention_heads ,					intermediate_size=self.intermediate_size ,					hidden_act=self.hidden_act ,					hidden_dropout_prob=self.hidden_dropout_prob ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob ,					max_position_embeddings=self.max_position_embeddings ,					type_vocab_size=self.type_vocab_size ,					is_decoder=__UpperCAmelCase ,					initializer_range=self.initializer_range ,					)

											return config, input_ids, token_type_ids, attention_mask



						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					self.prepare_config_and_inputs()
											__a      ,			__a      ,			__a      ,			__a					:						Dict      =					config_and_inputs
											__a					:						str      =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
											return config, inputs_dict



						def        UpperCAmelCase__					(self:					Dict   )    ->     str:




											'''simple docstring'''



											__a					:						Optional[Any]      =					self.prepare_config_and_inputs()
											__a      ,			__a      ,			__a      ,			__a					:						Tuple      =					config_and_inputs

											__a					:						int      =					True
											__a					:						List[str]      =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
											__a					:						List[Any]      =					ids_tensor([self.batch_size, self.seq_length] ,					vocab_size=2   )

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)




@require_flax
class 							snake_case_       (    __UpperCamelCase		,							unittest.TestCase      ):

						"""simple docstring"""



						snake_case__        =      True

						snake_case__        =      (
						    (
						        FlaxRobertaModel,
						        FlaxRobertaForCausalLM,
						        FlaxRobertaForMaskedLM,
						        FlaxRobertaForSequenceClassification,
						        FlaxRobertaForTokenClassification,
						        FlaxRobertaForMultipleChoice,
						        FlaxRobertaForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)



						def        UpperCAmelCase__					(self:					Dict   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						Tuple      =					FlaxRobertaModelTester(self   )



						@slow
						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     List[Any]:




											'''simple docstring'''



											for model_class_name in self.all_model_classes:
																__a					:						int      =					model_class_name.from_pretrained("roberta-base" ,					from_pt=__UpperCAmelCase   )
																__a					:						Tuple      =					model(np.ones((1, 1)   )   )
																self.assertIsNotNone(__UpperCAmelCase   )





