
"""simple docstring"""


# 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


_lowercase							   =				float('''nan''')







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      Any  ,A_     :      int      )     ->   Optional[int]:
							A						   =   sys.stdout
							A						   =   open(A_  ,'a'      )


						def __getattr__( self     :      Union[str, Any]  ,A_     :      int      )     ->   List[str]:
							return getattr(self.stdout  ,A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      List[Any]      )     ->   Optional[Any]:
							self.stdout.write(A_      )
							# strip tqdm codes
							self.file.write(re.sub(R'^.*\r'  ,''  ,A_  ,0  ,re.M      )      )






def       _snake_case				(		snake_case__   :     List[Any]=80	,      snake_case__   :     Dict=False     ):
	A						   =   []

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

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

	# 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						   =   []
	A						   =   ''
	while len(snake_case__     ) > 0:
		current_line += F'{cmd.pop(0     )} '
		if len(snake_case__     ) == 0 or len(snake_case__     ) + len(cmd[0]     ) + 1 > max_width - 1:
			lines.append(snake_case__     )
			A						   =   ''
	return "\\\n".join(snake_case__     )






def       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     Optional[Any]     ):
	# unwrap multi-line input
	A						   =   re.sub(r'[\\\n]+'	,      ' '	,      args.base_cmd     )

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

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

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






def       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     Union[str, Any]	,      snake_case__   :     Dict	,      snake_case__   :     List[Any]	,      snake_case__   :     Optional[Any]	,      snake_case__   :     List[str]	,      snake_case__   :     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	,      100     ) for k in metric_keys}	,      **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.22222222]     )}	,      )

	A						   =   subprocess.run(snake_case__	,      capture_output=snake_case__	,      text=snake_case__     )

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

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

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






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






def       _snake_case				(		):
	A						   =   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       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     Any	,      snake_case__   :     int	,      snake_case__   :     Dict	,      snake_case__   :     List[Any]     ):
	A						   =   pd.DataFrame(snake_case__     )
	A						   =   'variation'
	A						   =   'diff_%'

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

	# create diff column if possible
	if not math.isnan(snake_case__     ):
		A						   =   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						   =   [variation_key, target_metric_key, diff_key, *report_metric_keys]
	A						   =   df.reindex(snake_case__	,      axis='columns'     )  # reorder cols

	# capitalize
	A						   =   df.rename(str.capitalize	,      axis='columns'     )

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

	A						   =   ['', '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=snake_case__	,      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=snake_case__	,      floatfmt='.2f'     )]

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






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

	A						   =   args.output_dir
	Path(snake_case__     ).mkdir(exist_ok=snake_case__     )
	A						   =   get_base_command(snake_case__	,      snake_case__     )

	# split each dimension into its --foo variations
	A						   =   [list(map(str.strip	,      re.split(r'\|'	,      snake_case__     )     )     ) 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						   =   list(map(str.strip	,      map(' '.join	,      itertools.product(*snake_case__     )     )     )     )
	A						   =   max(len(snake_case__     ) for x in variations     )

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

	# capture prints into a log file for convenience
	A						   =   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						   =   Tee(snake_case__     )

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

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

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


if __name__ == "__main__":
		main()

"""simple docstring"""


import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMWithLMHeadModel,
		)
		from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      Any  ,A_     :      Optional[Any]  ,A_     :      Tuple=13  ,A_     :      Optional[Any]=7  ,A_     :      Dict=True  ,A_     :      Optional[Any]=True  ,A_     :      str=True  ,A_     :      Union[str, Any]=True  ,A_     :      Optional[Any]=True  ,A_     :      Tuple=False  ,A_     :      Optional[int]=False  ,A_     :      str=False  ,A_     :      int=2  ,A_     :      Union[str, Any]=99  ,A_     :      int=0  ,A_     :      Dict=32  ,A_     :      List[str]=5  ,A_     :      Any=4  ,A_     :      str=0.1  ,A_     :      Any=0.1  ,A_     :      int=512  ,A_     :      List[Any]=2  ,A_     :      Union[str, Any]=0.02  ,A_     :      Optional[Any]=2  ,A_     :      List[str]=4  ,A_     :      Optional[int]="last"  ,A_     :      str=True  ,A_     :      List[str]=None  ,A_     :      List[Any]=0  ,)     ->   int:
							A						   =   parent
							A						   =   batch_size
							A						   =   seq_length
							A						   =   is_training
							A						   =   use_input_lengths
							A						   =   use_token_type_ids
							A						   =   use_labels
							A						   =   gelu_activation
							A						   =   sinusoidal_embeddings
							A						   =   causal
							A						   =   asm
							A						   =   n_langs
							A						   =   vocab_size
							A						   =   n_special
							A						   =   hidden_size
							A						   =   num_hidden_layers
							A						   =   num_attention_heads
							A						   =   hidden_dropout_prob
							A						   =   attention_probs_dropout_prob
							A						   =   max_position_embeddings
							A						   =   type_sequence_label_size
							A						   =   initializer_range
							A						   =   num_labels
							A						   =   num_choices
							A						   =   summary_type
							A						   =   use_proj
							A						   =   scope
							A						   =   bos_token_id


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Dict:
							A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size      )
							A						   =   random_attention_mask([self.batch_size, self.seq_length]      )

							A						   =   None
							if self.use_input_lengths:
								A						   =   (
								    ids_tensor([self.batch_size]  ,vocab_size=2      ) + self.seq_length - 2
								)  # small variation of seq_length

							A						   =   None
							if self.use_token_type_ids:
								A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.n_langs      )

							A						   =   None
							A						   =   None
							A						   =   None
							if self.use_labels:
								A						   =   ids_tensor([self.batch_size]  ,self.type_sequence_label_size      )
								A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.num_labels      )
								A						   =   ids_tensor([self.batch_size]  ,2      ).float()
								A						   =   ids_tensor([self.batch_size]  ,self.num_choices      )

							A						   =   self.get_config()

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_lengths,
							    sequence_labels,
							    token_labels,
							    is_impossible_labels,
							    choice_labels,
							    input_mask,
							)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Any:
							return XLMConfig(
							    vocab_size=self.vocab_size  ,n_special=self.n_special  ,emb_dim=self.hidden_size  ,n_layers=self.num_hidden_layers  ,n_heads=self.num_attention_heads  ,dropout=self.hidden_dropout_prob  ,attention_dropout=self.attention_probs_dropout_prob  ,gelu_activation=self.gelu_activation  ,sinusoidal_embeddings=self.sinusoidal_embeddings  ,asm=self.asm  ,causal=self.causal  ,n_langs=self.n_langs  ,max_position_embeddings=self.max_position_embeddings  ,initializer_range=self.initializer_range  ,summary_type=self.summary_type  ,use_proj=self.use_proj  ,num_labels=self.num_labels  ,bos_token_id=self.bos_token_id  ,)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      List[str]  ,A_     :      int  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      Optional[Any]  ,A_     :      Any  ,A_     :      List[str]  ,A_     :      Optional[int]  ,)     ->   Tuple:
							A						   =   XLMModel(config=A_      )
							model.to(A_      )
							model.eval()
							A						   =   model(A_  ,lengths=A_  ,langs=A_      )
							A						   =   model(A_  ,langs=A_      )
							A						   =   model(A_      )
							self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      str  ,A_     :      Union[str, Any]  ,A_     :      Any  ,A_     :      Any  ,A_     :      Any  ,A_     :      Union[str, Any]  ,A_     :      List[str]  ,A_     :      List[str]  ,A_     :      List[str]  ,)     ->   Union[str, Any]:
							A						   =   XLMWithLMHeadModel(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_  ,token_type_ids=A_  ,labels=A_      )
							self.parent.assertEqual(result.loss.shape  ,()      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Tuple  ,A_     :      Tuple  ,A_     :      str  ,A_     :      int  ,A_     :      str  ,A_     :      Optional[Any]  ,A_     :      Any  ,A_     :      Any  ,A_     :      Dict  ,)     ->   List[str]:
							A						   =   XLMForQuestionAnsweringSimple(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_      )

							A						   =   model(A_  ,start_positions=A_  ,end_positions=A_      )
							A						   =   outputs
							self.parent.assertEqual(result.start_logits.shape  ,(self.batch_size, self.seq_length)      )
							self.parent.assertEqual(result.end_logits.shape  ,(self.batch_size, self.seq_length)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Optional[int]  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      List[Any]  ,A_     :      List[str]  ,A_     :      List[Any]  ,A_     :      Optional[Any]  ,A_     :      str  ,A_     :      Any  ,)     ->   Optional[int]:
							A						   =   XLMForQuestionAnswering(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_      )

							A						   =   model(
							    A_  ,start_positions=A_  ,end_positions=A_  ,cls_index=A_  ,is_impossible=A_  ,p_mask=A_  ,)

							A						   =   model(
							    A_  ,start_positions=A_  ,end_positions=A_  ,cls_index=A_  ,is_impossible=A_  ,)

							((A)   ,      )						   =   result_with_labels.to_tuple()

							A						   =   model(A_  ,start_positions=A_  ,end_positions=A_      )

							((A)   ,      )						   =   result_with_labels.to_tuple()

							self.parent.assertEqual(result_with_labels.loss.shape  ,()      )
							self.parent.assertEqual(result.start_top_log_probs.shape  ,(self.batch_size, model.config.start_n_top)      )
							self.parent.assertEqual(result.start_top_index.shape  ,(self.batch_size, model.config.start_n_top)      )
							self.parent.assertEqual(
							    result.end_top_log_probs.shape  ,(self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
							self.parent.assertEqual(
							    result.end_top_index.shape  ,(self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
							self.parent.assertEqual(result.cls_logits.shape  ,(self.batch_size,)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      Union[str, Any]  ,A_     :      Tuple  ,A_     :      int  ,A_     :      Union[str, Any]  ,A_     :      List[Any]  ,A_     :      Optional[Any]  ,A_     :      Tuple  ,A_     :      Union[str, Any]  ,A_     :      str  ,)     ->   List[Any]:
							A						   =   XLMForSequenceClassification(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_      )
							A						   =   model(A_  ,labels=A_      )
							self.parent.assertEqual(result.loss.shape  ,()      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.type_sequence_label_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      str  ,A_     :      Tuple  ,A_     :      List[str]  ,A_     :      Dict  ,A_     :      Dict  ,A_     :      Union[str, Any]  ,A_     :      Dict  ,A_     :      Any  ,)     ->   Any:
							A						   =   self.num_labels
							A						   =   XLMForTokenClassification(A_      )
							model.to(A_      )
							model.eval()

							A						   =   model(A_  ,attention_mask=A_  ,labels=A_      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.num_labels)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      int  ,A_     :      str  ,A_     :      int  ,A_     :      Tuple  ,A_     :      List[Any]  ,A_     :      List[str]  ,A_     :      List[str]  ,A_     :      Optional[Any]  ,A_     :      int  ,)     ->   Tuple:
							A						   =   self.num_choices
							A						   =   XLMForMultipleChoice(config=A_      )
							model.to(A_      )
							model.eval()
							A						   =   input_ids.unsqueeze(1      ).expand(-1  ,self.num_choices  ,-1      ).contiguous()
							A						   =   token_type_ids.unsqueeze(1      ).expand(-1  ,self.num_choices  ,-1      ).contiguous()
							A						   =   input_mask.unsqueeze(1      ).expand(-1  ,self.num_choices  ,-1      ).contiguous()
							A						   =   model(
							    A_  ,attention_mask=A_  ,token_type_ids=A_  ,labels=A_  ,)
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_choices)      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   List[Any]:
							A						   =   self.prepare_config_and_inputs()
							(
							    (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      
							)						   =   config_and_inputs
							A						   =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
							return config, inputs_dict







@require_torch
class 				lowerCAmelCase_				(      _lowercase		,					_lowercase		,					_lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Dict      =    (
						    (
						        XLMModel,
						        XLMWithLMHeadModel,
						        XLMForQuestionAnswering,
						        XLMForSequenceClassification,
						        XLMForQuestionAnsweringSimple,
						        XLMForTokenClassification,
						        XLMForMultipleChoice,
						    )
						    if is_torch_available()
						    else ()
						)
						_lowerCamelCase:    Dict      =    (
						    (XLMWithLMHeadModel,) if is_torch_available() else ()
						)  # TODO (PVP): Check other models whether language generation is also applicable
						_lowerCamelCase:    Optional[Any]      =    (
						    {
						        '''feature-extraction''': XLMModel,
						        '''fill-mask''': XLMWithLMHeadModel,
						        '''question-answering''': XLMForQuestionAnsweringSimple,
						        '''text-classification''': XLMForSequenceClassification,
						        '''text-generation''': XLMWithLMHeadModel,
						        '''token-classification''': XLMForTokenClassification,
						        '''zero-shot''': XLMForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      Tuple  ,A_     :      Any  ,A_     :      str      )     ->   Tuple:
							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith('Fast'      )
							):
								# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
								# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
								# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
								return True

							return False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      Tuple  ,A_     :      str=False      )     ->   Dict:
							A						   =   super()._prepare_for_class(A_  ,A_  ,return_labels=A_      )

							if return_labels:
								if model_class.__name__ == "XLMForQuestionAnswering":
									A						   =   torch.zeros(
									    self.model_tester.batch_size  ,dtype=torch.long  ,device=A_      )
									A						   =   torch.zeros(
									    self.model_tester.batch_size  ,dtype=torch.long  ,device=A_      )

							return inputs_dict


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   List[str]:
							A						   =   XLMModelTester(self      )
							A						   =   ConfigTester(self  ,config_class=A_  ,emb_dim=37      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   str:
							self.config_tester.run_common_tests()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Optional[int]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_model(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_lm_head(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_simple_qa(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Tuple:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_qa(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   List[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_sequence_classif(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Tuple:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_token_classif(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_for_multiple_choice(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Dict  ,A_     :      List[Any]  ,A_     :      List[Any]  ,A_     :      Dict  ,A_     :      Union[str, Any]  ,A_     :      List[Any]=False  ,A_     :      Tuple=1      )     ->   List[Any]:
							self.assertIsInstance(A_  ,A_      )
							self.assertListEqual(
							    [isinstance(A_  ,A_      ) for iter_attentions in attentions]  ,[True] * len(A_      )      )
							self.assertEqual(len(A_      )  ,(max_length - min_length) * num_beam_groups      )

							for idx, iter_attentions in enumerate(A_      ):
								# adds PAD dummy token
								A						   =   min_length + idx + 1
								A						   =   min_length + idx + 1

								A						   =   (
								    batch_size * num_beam_groups,
								    config.num_attention_heads,
								    tgt_len,
								    src_len,
								)
								# check attn size
								self.assertListEqual(
								    [layer_attention.shape for layer_attention in iter_attentions]  ,[expected_shape] * len(A_      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[Any]  ,A_     :      List[str]  ,A_     :      Dict  ,A_     :      Optional[Any]  ,A_     :      Dict  ,A_     :      Tuple=False  ,A_     :      Optional[Any]=1      )     ->   List[str]:
							self.assertIsInstance(A_  ,A_      )
							self.assertListEqual(
							    [isinstance(A_  ,A_      ) for iter_hidden_states in hidden_states]  ,[True] * len(A_      )  ,)
							self.assertEqual(len(A_      )  ,(max_length - min_length) * num_beam_groups      )

							for idx, iter_hidden_states in enumerate(A_      ):
								# adds PAD dummy token
								A						   =   min_length + idx + 1
								A						   =   (batch_size * num_beam_groups, seq_len, config.hidden_size)
								# check hidden size
								self.assertListEqual(
								    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]  ,[expected_shape] * len(A_      )  ,)
							pass




						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[str]:
							for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A						   =   XLMModel.from_pretrained(A_      )
								self.assertIsNotNone(A_      )







@require_torch
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Dict:
							A						   =   XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'      )
							model.to(A_      )
							A						   =   torch.tensor([[14, 447]]  ,dtype=torch.long  ,device=A_      )  # the president
							A						   =   [
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							    14,
							    447,
							]  # the president the president the president the president the president the president the president the president the president the president
							# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
							A						   =   model.generate(A_  ,do_sample=A_      )
							self.assertListEqual(output_ids[0].cpu().numpy().tolist()  ,A_      )