






import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

__lowerCamelCase :      str										=				logging.getLogger()







def 					SCREAMING_SNAKE_CASE    (			) ->    Optional[Any]:
	snake_case__			:				str       =  argparse.ArgumentParser()
	parser.add_argument("-f"       )
	snake_case__			:				Optional[int]       =  parser.parse_args()
	return args.f







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple       ) ->    Optional[Any]:
	snake_case__			:				List[Any]       =  {}
	snake_case__			:				Tuple       =  os.path.join(_UpperCAmelCase					,  "all_results.json"       )
	if os.path.exists(_UpperCAmelCase       ):
		with open(_UpperCAmelCase					,  "r"       ) as f:
			snake_case__			:				Optional[int]       =  json.load(_UpperCAmelCase       )
	else:
		raise ValueError(F'''can\'t find {path}'''       )
	return results







def 					SCREAMING_SNAKE_CASE    (			) ->    Any:
	snake_case__			:				Union[str, Any]       =  torch.cuda.is_available() and torch_device == 'cuda'
	return is_using_cuda and is_apex_available()


__lowerCamelCase :      Optional[int]										=				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 							SCREAMING_SNAKE_CASE__						(    SCREAMING_SNAKE_CASE__						):





					"""simple docstring"""






					@classmethod
					def 	_lowercase					( cls					:  Optional[int]	):
						# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
						snake_case__			:				Tuple       =  tempfile.mkdtemp()
						snake_case__			:				str       =  os.path.join(cls.tmpdir							,		"default_config.yml"	)
						write_basic_config(save_location=cls.configPath	)
						snake_case__			:				Dict       =  ['accelerate', 'launch', '--config_file', cls.configPath]



					@classmethod
					def 	_lowercase					( cls					:  List[str]	):
						shutil.rmtree(cls.tmpdir	)



					@mock.patch.dict(os.environ							,		{"WANDB_MODE": "offline"}	)
					def 	_lowercase					( self					:  str	):
						snake_case__			:				Dict       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				Optional[Any]       =  f'''
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

						if is_cuda_and_apex_available():
							testargs.append("--fp16"	)

						run_command(self._launch_args + testargs	)
						snake_case__			:				List[str]       =  get_results(__A	)
						self.assertGreaterEqual(result["eval_accuracy"]							,		0.7_5	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"epoch_0"	)	)	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"glue_no_trainer"	)	)	)



					@mock.patch.dict(os.environ							,		{"WANDB_MODE": "offline"}	)
					def 	_lowercase					( self					:  Tuple	):
						snake_case__			:				List[str]       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				int       =  f'''
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

						if torch.cuda.device_count() > 1:
							# Skipping because there are not enough batches to train the model + would need a drop_last to work.
							return

						run_command(self._launch_args + testargs	)
						snake_case__			:				Tuple       =  get_results(__A	)
						self.assertLess(result["perplexity"]							,		1_0_0	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"epoch_0"	)	)	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"clm_no_trainer"	)	)	)



					@mock.patch.dict(os.environ							,		{"WANDB_MODE": "offline"}	)
					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Union[str, Any]       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				Dict       =  f'''
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

						run_command(self._launch_args + testargs	)
						snake_case__			:				Optional[int]       =  get_results(__A	)
						self.assertLess(result["perplexity"]							,		4_2	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"epoch_0"	)	)	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"mlm_no_trainer"	)	)	)



					@mock.patch.dict(os.environ							,		{"WANDB_MODE": "offline"}	)
					def 	_lowercase					( self					:  Any	):
						# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
						snake_case__			:				Optional[Any]       =  7 if get_gpu_count() > 1 else 2

						snake_case__			:				Optional[Any]       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				List[str]       =  f'''
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

						run_command(self._launch_args + testargs	)
						snake_case__			:				Dict       =  get_results(__A	)
						self.assertGreaterEqual(result["eval_accuracy"]							,		0.7_5	)
						self.assertLess(result["train_loss"]							,		0.5	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"epoch_0"	)	)	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"ner_no_trainer"	)	)	)



					@unittest.skip(reason="Fix me @muellerzr"	)
					@mock.patch.dict(os.environ							,		{"WANDB_MODE": "offline"}	)
					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				str       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				Optional[Any]       =  f'''
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

						run_command(self._launch_args + testargs	)
						snake_case__			:				Dict       =  get_results(__A	)
						# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
						self.assertGreaterEqual(result["eval_f1"]							,		2_8	)
						self.assertGreaterEqual(result["eval_exact"]							,		2_8	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"epoch_0"	)	)	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"qa_no_trainer"	)	)	)



					@mock.patch.dict(os.environ							,		{"WANDB_MODE": "offline"}	)
					def 	_lowercase					( self					:  int	):
						snake_case__			:				str       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				List[str]       =  f'''
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        '''.split()

						run_command(self._launch_args + testargs	)
						snake_case__			:				Optional[int]       =  get_results(__A	)
						self.assertGreaterEqual(result["eval_accuracy"]							,		0.8	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"swag_no_trainer"	)	)	)



					@slow
					@mock.patch.dict(os.environ							,		{"WANDB_MODE": "offline"}	)
					def 	_lowercase					( self					:  Any	):
						snake_case__			:				Dict       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				Any       =  f'''
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

						run_command(self._launch_args + testargs	)
						snake_case__			:				Optional[Any]       =  get_results(__A	)
						self.assertGreaterEqual(result["eval_rouge1"]							,		1_0	)
						self.assertGreaterEqual(result["eval_rouge2"]							,		2	)
						self.assertGreaterEqual(result["eval_rougeL"]							,		7	)
						self.assertGreaterEqual(result["eval_rougeLsum"]							,		7	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"epoch_0"	)	)	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"summarization_no_trainer"	)	)	)



					@slow
					@mock.patch.dict(os.environ							,		{"WANDB_MODE": "offline"}	)
					def 	_lowercase					( self					:  int	):
						snake_case__			:				Union[str, Any]       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				int       =  f'''
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

						run_command(self._launch_args + testargs	)
						snake_case__			:				List[Any]       =  get_results(__A	)
						self.assertGreaterEqual(result["eval_bleu"]							,		3_0	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"epoch_0"	)	)	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"translation_no_trainer"	)	)	)



					@slow
					def 	_lowercase					( self					:  Any	):
						snake_case__			:				str       =  logging.StreamHandler(sys.stdout	)
						logger.addHandler(__A	)

						snake_case__			:				Tuple       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				Union[str, Any]       =  f'''
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        '''.split()

						run_command(self._launch_args + testargs	)
						snake_case__			:				List[Any]       =  get_results(__A	)
						self.assertGreaterEqual(result["eval_overall_accuracy"]							,		0.1_0	)






					@mock.patch.dict(os.environ							,		{"WANDB_MODE": "offline"}	)
					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Dict       =  self.get_auto_remove_tmp_dir()
						snake_case__			:				int       =  f'''
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        '''.split()

						if is_cuda_and_apex_available():
							testargs.append("--fp16"	)

						run_command(self._launch_args + testargs	)
						snake_case__			:				Optional[int]       =  get_results(__A	)
						# The base model scores a 25%
						self.assertGreaterEqual(result["eval_accuracy"]							,		0.6	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"step_1"	)	)	)
						self.assertTrue(os.path.exists(os.path.join(__A							,		"image_classification_no_trainer"	)	)	)











import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__lowerCamelCase :      int										=				logging.get_logger(__name__)


__lowerCamelCase :      List[str]										=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase :      Any										=				{
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase :      Optional[int]										=				{
    """allenai/led-base-16384""": 1_6384,
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		VOCAB_FILES_NAMES
					a_     =		PRETRAINED_VOCAB_FILES_MAP
					a_     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_     =		LEDTokenizer
					a_     =		["input_ids", "attention_mask"]



					def __init__( self					:  int							,		__A					:  Optional[int]=None							,		__A					:  Any=None							,		__A					:  Any=None							,		__A					:  int="replace"							,		__A					:  Optional[Any]="<s>"							,		__A					:  List[Any]="</s>"							,		__A					:  Dict="</s>"							,		__A					:  Union[str, Any]="<s>"							,		__A					:  List[Any]="<unk>"							,		__A					:  Optional[Any]="<pad>"							,		__A					:  Optional[int]="<mask>"							,		__A					:  str=False							,		__A					:  Optional[Any]=True							,		**__A					:  List[Any]							,		):
						super().__init__(
						    __A							,		__A							,		tokenizer_file=__A							,		errors=__A							,		bos_token=__A							,		eos_token=__A							,		sep_token=__A							,		cls_token=__A							,		unk_token=__A							,		pad_token=__A							,		mask_token=__A							,		add_prefix_space=__A							,		trim_offsets=__A							,		**__A							,		)

						snake_case__			:				List[Any]       =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
						if pre_tok_state.get("add_prefix_space"							,		__A	) != add_prefix_space:
							snake_case__			:				List[str]       =  getattr(__A							,		pre_tok_state.pop("type"	)	)
							snake_case__			:				Tuple       =  add_prefix_space
							snake_case__			:				Any       =  pre_tok_class(**__A	)

						snake_case__			:				List[Any]       =  add_prefix_space

						# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
						snake_case__			:				str       =  "post_processor"
						snake_case__			:				Union[str, Any]       =  getattr(self.backend_tokenizer							,		__A							,		__A	)
						if tokenizer_component_instance:
							snake_case__			:				Any       =  json.loads(tokenizer_component_instance.__getstate__()	)

							# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
							if "sep" in state:
								snake_case__			:				int       =  tuple(state["sep"]	)
							if "cls" in state:
								snake_case__			:				Optional[int]       =  tuple(state["cls"]	)

							snake_case__			:				Union[str, Any]       =  False

							if state.get("add_prefix_space"							,		__A	) != add_prefix_space:
								snake_case__			:				Optional[Any]       =  add_prefix_space
								snake_case__			:				List[str]       =  True

							if state.get("trim_offsets"							,		__A	) != trim_offsets:
								snake_case__			:				str       =  trim_offsets
								snake_case__			:				Tuple       =  True

							if changes_to_apply:
								snake_case__			:				Any       =  getattr(__A							,		state.pop("type"	)	)
								snake_case__			:				Optional[Any]       =  component_class(**__A	)
								setattr(self.backend_tokenizer							,		__A							,		__A	)



					@property
					# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
					def 	_lowercase					( self					:  Union[str, Any]	):
						if self._mask_token is None:
							if self.verbose:
								logger.error("Using mask_token, but it is not set yet."	)
							return None
						return str(self._mask_token	)



					@mask_token.setter
					def 	_lowercase					( self					:  str							,		__A					:  int	):
						snake_case__			:				str       =  AddedToken(__A							,		lstrip=__A							,		rstrip=__A	) if isinstance(__A							,		__A	) else value
						snake_case__			:				Optional[int]       =  value



					def 	_lowercase					( self					:  List[str]							,		*__A					:  Union[str, Any]							,		**__A					:  int	):
						snake_case__			:				Dict       =  kwargs.get("is_split_into_words"							,		__A	)

						if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
							    "to use it with pretokenized inputs."	)

						return super()._batch_encode_plus(*__A							,		**__A	)



					def 	_lowercase					( self					:  Union[str, Any]							,		*__A					:  Tuple							,		**__A					:  Union[str, Any]	):
						snake_case__			:				List[str]       =  kwargs.get("is_split_into_words"							,		__A	)

						if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
							    "to use it with pretokenized inputs."	)

						return super()._encode_plus(*__A							,		**__A	)



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  str							,		__A					:  Optional[str] = None	):
						snake_case__			:				str       =  self._tokenizer.model.save(__A							,		name=__A	)
						return tuple(__A	)



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  int							,		__A					:  str=None	):
						snake_case__			:				int       =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
							return output

						return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



					def 	_lowercase					( self					:  Any							,		__A					:  List[int]							,		__A					:  Optional[List[int]] = None	):
						snake_case__			:				Optional[Any]       =  [self.sep_token_id]
						snake_case__			:				Any       =  [self.cls_token_id]

						if token_ids_a is None:
							return len(cls + token_ids_a + sep	) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]






					def 	_lowercase					( self					:  str							,		__A					:  Union[Dict[str, EncodedInput], BatchEncoding]							,		__A					:  Optional[int] = None							,		__A					:  PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,		__A					:  Optional[int] = None							,		__A					:  Optional[bool] = None							,		):
						snake_case__			:				Dict       =  super()._pad(
						    encoded_inputs=__A							,		max_length=__A							,		padding_strategy=__A							,		pad_to_multiple_of=__A							,		return_attention_mask=__A							,		)

						# Load from model defaults
						if return_attention_mask is None:
							snake_case__			:				Optional[Any]       =  "attention_mask" in self.model_input_names

						if return_attention_mask and "global_attention_mask" in encoded_inputs:
							snake_case__			:				List[Any]       =  encoded_inputs[self.model_input_names[0]]
							# `global_attention_mask` need to have the same length as other (sequential) inputs.
							snake_case__			:				Union[str, Any]       =  len(encoded_inputs["global_attention_mask"]	) != len(__A	)

							if needs_to_be_padded:
								snake_case__			:				Dict       =  len(__A	) - len(encoded_inputs["global_attention_mask"]	)

								if self.padding_side == "right":
									# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
									snake_case__			:				Optional[Any]       =  (
									    encoded_inputs["global_attention_mask"] + [-1] * difference
									)
								elif self.padding_side == "left":
									snake_case__			:				Optional[Any]       =  [-1] * difference + encoded_inputs[
									    "global_attention_mask"
									]
								else:
									raise ValueError("Invalid padding strategy:" + str(self.padding_side	)	)

						return encoded_inputs



