


import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

snake_case__     							= logging.getLogger()
snake_case__     							= torch.cuda.is_available()
snake_case__     							= {
    'max_tokens_per_batch': None,
    'supervise_forward': True,
    'normalize_hidden': True,
    'label_smoothing': 0.2,
    'eval_max_gen_length': None,
    'eval_beams': 1,
    'val_metric': 'loss',
    'save_top_k': 1,
    'adafactor': True,
    'early_stopping_patience': 2,
    'logger_name': 'default',
    'length_penalty': 0.5,
    'cache_dir': '',
    'task': 'summarization',
    'num_workers': 2,
    'alpha_hid': 0,
    'freeze_embeds': True,
    'enc_only': False,
    'tgt_suffix': '',
    'resume_from_checkpoint': None,
    'sortish_sampler': True,
    'student_decoder_layers': 1,
    'val_check_interval': 1.0,
    'output_dir': '',
    'fp16': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    'no_teacher': False,
    'fp16_opt_level': 'O1',
    'gpus': 1 if CUDA_AVAILABLE else 0,
    'n_tpu_cores': 0,
    'max_grad_norm': 1.0,
    'do_train': True,
    'do_predict': True,
    'accumulate_grad_batches': 1,
    'server_ip': '',
    'server_port': '',
    'seed': 42,
    'model_name_or_path': 'sshleifer/bart-tiny-random',
    'config_name': '',
    'tokenizer_name': 'facebook/bart-large',
    'do_lower_case': False,
    'learning_rate': 0.3,
    'lr_scheduler': 'linear',
    'weight_decay': 0.0,
    'adam_epsilon': 1E-08,
    'warmup_steps': 0,
    'max_epochs': 1,
    'train_batch_size': 2,
    'eval_batch_size': 2,
    'max_source_length': 12,
    'max_target_length': 12,
    'val_max_target_length': 12,
    'test_max_target_length': 12,
    'fast_dev_run': False,
    'no_cache': False,
    'n_train': -1,
    'n_val': -1,
    'n_test': -1,
    'student_encoder_layers': 1,
    'freeze_encoder': False,
    'auto_scale_batch_size': False,
    'overwrite_output_dir': False,
    'student': None,
}
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Path      ,						lowercase__  :list				)       ->  str:
						__magic_name__				:							List[Any]				=      '\n'.join(lowercase__				)
						Path(lowercase__				).open('w'				).writelines(lowercase__				)


snake_case__     							= [' Sam ate lunch today.', 'Sams lunch ingredients.']
snake_case__     							= ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
snake_case__     							= 'patrickvonplaten/t5-tiny-random'
snake_case__     							= 'sshleifer/t5-tinier-random'
snake_case__     							= 'sshleifer/bart-tiny-random'
snake_case__     							= 'sshleifer/tiny-mbart'
snake_case__     							= 'sshleifer/tiny-marian-en-de'
snake_case__     							= 'stas/tiny-wmt19-en-de'


snake_case__     							= logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Union[str, Any]				)       ->  Dict:
						for split in ["train", "val", "test"]:
												_dump_articles(os.path.join(lowercase__      ,						f"{split}.source"				)      ,						lowercase__				)
												_dump_articles(os.path.join(lowercase__      ,						f"{split}.target"				)      ,						lowercase__				)
						return tmp_dir



class     snake_case__  (      _A  ):
				@classmethod
				def  UpperCamelCase_						(  cls      ):




										"""simple docstring"""




										logging.disable(logging.CRITICAL      )  # remove noisy download output from tracebacks
										return cls
				@slow
				@require_torch_gpu
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Optional[Any]				=      list_models()
										__magic_name__				:							Optional[int]				=      'sshleifer'
										__magic_name__				:							Any				=      [x.modelId for x in model_list if x.modelId.startswith(_UpperCAmelCase      )]
										__magic_name__				:							Optional[int]				=      ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
										__magic_name__				:							List[Any]				=      []
										for m in model_ids:
																if m in allowed_to_be_broken:
																						continue
																try:
																						AutoConfig.from_pretrained(_UpperCAmelCase      )
																except Exception:
																						failures.append(_UpperCAmelCase      )
										assert not failures, F"The following models could not be loaded through AutoConfig: {failures}"
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
										self._test_distiller_cli(_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      {
										    'student_encoder_layers': 2,
										    'student_decoder_layers': 1,
										    'max_epochs': 4,
										    'val_check_interval': 0.25,
										    'alpha_hid': 2.0,
										    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
										}
										__magic_name__				:							Dict				=      self._test_distiller_cli(_UpperCAmelCase     ,      check_contents=_UpperCAmelCase      )

										__magic_name__				:							Any				=      list(Path(model.output_dir      ).glob('*.ckpt'      )      )
										self.assertEqual(1     ,      len(_UpperCAmelCase      )      )
										__magic_name__				:							str				=      list(Path(model.output_dir      ).glob('**/*.bin'      )      )
										self.assertEqual(len(_UpperCAmelCase      )     ,      2      )
										__magic_name__				:							Tuple				=      lmap(str.strip     ,      Path(model.hparams.data_dir      ).joinpath('test.source'      ).open().readlines()      )
										__magic_name__				:							List[Any]				=      tempfile.mktemp()  # XXX: not being cleaned up
										generate_summaries_or_translations(_UpperCAmelCase     ,      _UpperCAmelCase     ,      str(model.output_dir / 'best_tfmr'      )      )
										self.assertTrue(Path(_UpperCAmelCase      ).exists()      )

										__magic_name__				:							Tuple				=      self.get_auto_remove_tmp_dir()
										convert_pl_to_hf(ckpts[0]     ,      transformer_ckpts[0].parent     ,      _UpperCAmelCase      )
										assert os.path.exists(os.path.join(_UpperCAmelCase     ,      'pytorch_model.bin'      )      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase      )
										__magic_name__		, __magic_name__				:							Optional[int]				=      model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
										__magic_name__				:							Optional[Any]				=      torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]     ,      dtype=torch.long     ,      device=model.device      )
										__magic_name__				:							List[str]				=      target_ids[:, :-1].contiguous()  # Why this line?
										__magic_name__				:							Dict				=      target_ids[:, 1:].clone()  # why clone?
										__magic_name__				:							Optional[Any]				=      model(
										    _UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      decoder_input_ids=_UpperCAmelCase     ,      labels=_UpperCAmelCase     ,      use_cache=_UpperCAmelCase      ).loss

										__magic_name__				:							List[str]				=      model(_UpperCAmelCase     ,      attention_mask=_UpperCAmelCase     ,      decoder_input_ids=_UpperCAmelCase     ,      use_cache=_UpperCAmelCase      ).logits

										__magic_name__				:							Optional[int]				=      nn.functional.log_softmax(_UpperCAmelCase     ,      dim=-1      )
										__magic_name__		, __magic_name__				:							Optional[Any]				=      label_smoothed_nll_loss(
										    _UpperCAmelCase     ,      _UpperCAmelCase     ,      0.1     ,      ignore_index=model.config.pad_token_id      )
										with self.assertRaises(_UpperCAmelCase      ):
																# TODO: understand why this breaks
																self.assertEqual(_UpperCAmelCase     ,      _UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Dict				=      {
										    'student_encoder_layers': 2,
										    'student_decoder_layers': 1,
										    'num_train_epochs': 4,
										    'val_check_interval': 0.25,
										    'alpha_hid': 2.0,
										    'task': 'translation',
										    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
										    'tokenizer_name': MBART_TINY,
										    'teacher': MBART_TINY,
										    'src_lang': 'en_XX',
										    'tgt_lang': 'ro_RO',
										}
										__magic_name__				:							List[Any]				=      self._test_distiller_cli(_UpperCAmelCase     ,      check_contents=_UpperCAmelCase      )
										assert model.model.config.model_type == "mbart"

										__magic_name__				:							Tuple				=      list(Path(model.output_dir      ).glob('*.ckpt'      )      )
										self.assertEqual(1     ,      len(_UpperCAmelCase      )      )
										__magic_name__				:							Optional[Any]				=      list(Path(model.output_dir      ).glob('**/*.bin'      )      )
										__magic_name__				:							Any				=      list(Path(model.output_dir      ).glob('best_tfmr/*'      )      )
										assert len(_UpperCAmelCase      ) > 2
										self.assertEqual(len(_UpperCAmelCase      )     ,      2      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							str				=      {
										    'student_encoder_layers': 1,
										    'student_decoder_layers': 1,
										    'alpha_hid': 2.0,
										    'teacher': T5_TINY,
										    'model_name_or_path': T5_TINY,
										    'tokenizer_name': T5_TINY,
										}
										self._test_distiller_cli(_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							Any				=      {
										    'teacher': T5_TINY,
										    'student': T5_TINIER,
										    'model_name_or_path': T5_TINIER,
										    'tokenizer_name': T5_TINIER,
										}
										self._test_distiller_cli(_UpperCAmelCase      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=True      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      {
										    'label_smoothing': 0.0,
										    'early_stopping_patience': -1,
										    'train_batch_size': 1,
										    'eval_batch_size': 2,
										    'max_epochs': 2,
										    'alpha_mlm': 0.2,
										    'alpha_ce': 0.8,
										    'do_predict': True,
										    'model_name_or_path': 'sshleifer/tinier_bart',
										    'teacher': CHEAP_ARGS['model_name_or_path'],
										    'val_check_interval': 0.5,
										}
										default_updates.update(_UpperCAmelCase      )
										__magic_name__				:							dict				=      CHEAP_ARGS.copy()
										__magic_name__				:							Optional[int]				=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
										__magic_name__				:							Tuple				=      self.get_auto_remove_tmp_dir()

										args_d.update(data_dir=_UpperCAmelCase     ,      output_dir=_UpperCAmelCase     ,      **_UpperCAmelCase      )
										__magic_name__				:							Dict				=      distill_main(argparse.Namespace(**_UpperCAmelCase      )      )
										if not check_contents:
																return model
										__magic_name__				:							Optional[Any]				=      os.listdir(_UpperCAmelCase      )
										__magic_name__				:							List[str]				=      {os.path.basename(_UpperCAmelCase      ) for p in contents}
										__magic_name__				:							Optional[int]				=      [p for p in contents if p.endswith('ckpt'      )]
										assert len(_UpperCAmelCase      ) > 0

										self.assertIn('test_generations.txt'     ,      _UpperCAmelCase      )
										self.assertIn('test_results.txt'     ,      _UpperCAmelCase      )

										__magic_name__				:							List[str]				=      load_json(model.metrics_save_path      )
										__magic_name__				:							Tuple				=      metrics['val'][-1]
										self.assertGreaterEqual(last_step_stats['val_avg_gen_time']     ,      0.01      )
										self.assertGreaterEqual(1.0     ,      last_step_stats['val_avg_gen_time']      )
										self.assertIsInstance(last_step_stats[F"val_avg_{model.val_metric}"]     ,      _UpperCAmelCase      )
										__magic_name__				:							int				=      int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1      )
										self.assertEqual(len(metrics['val']      )     ,      _UpperCAmelCase      )
										self.assertEqual(len(metrics['test']      )     ,      1      )
										return model






class     snake_case__  (      _A  ):
				@parameterized.expand(
				    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]     ,      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							dict				=      CHEAP_ARGS.copy()
										__magic_name__				:							List[str]				=      'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
										__magic_name__				:							Tuple				=      0.1 if task == 'translation' else 0

										__magic_name__				:							List[Any]				=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
										__magic_name__				:							Tuple				=      self.get_auto_remove_tmp_dir()
										args_d.update(
										    data_dir=_UpperCAmelCase     ,      model_name_or_path=_UpperCAmelCase     ,      tokenizer_name=_UpperCAmelCase     ,      train_batch_size=2     ,      eval_batch_size=2     ,      output_dir=_UpperCAmelCase     ,      do_predict=_UpperCAmelCase     ,      task=_UpperCAmelCase     ,      src_lang='en_XX'     ,      tgt_lang='ro_RO'     ,      freeze_encoder=_UpperCAmelCase     ,      freeze_embeds=_UpperCAmelCase     ,      )
										assert "n_train" in args_d
										__magic_name__				:							int				=      argparse.Namespace(**_UpperCAmelCase      )
										__magic_name__				:							Optional[int]				=      main(_UpperCAmelCase      )

										__magic_name__				:							Dict				=      module.model.get_input_embeddings()
										assert not input_embeds.weight.requires_grad
										if model == T5_TINY:
																__magic_name__				:							List[str]				=      module.model.lm_head
																assert not lm_head.weight.requires_grad
																assert (lm_head.weight == input_embeds.weight).all().item()
										elif model == FSMT_TINY:
																__magic_name__				:							Dict				=      module.model.model
																__magic_name__				:							Dict				=      fsmt.decoder.embed_positions
																assert not embed_pos.weight.requires_grad
																assert not fsmt.decoder.embed_tokens.weight.requires_grad
																# check that embeds are not the same
																assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
										else:
																__magic_name__				:							Any				=      module.model.model
																__magic_name__				:							Optional[Any]				=      bart.decoder.embed_positions
																assert not embed_pos.weight.requires_grad
																assert not bart.shared.weight.requires_grad
																# check that embeds are the same
																assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
																assert bart.decoder.embed_tokens == bart.shared

										__magic_name__				:							Dict				=      load_json(module.output_dir / 'text_batch.json'      )
										assert isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      )
										assert len(_UpperCAmelCase      ) >= 4
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							dict				=      CHEAP_ARGS.copy()

										__magic_name__				:							Union[str, Any]				=      'summarization'
										__magic_name__				:							List[str]				=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )

										args_d.update(
										    data_dir=_UpperCAmelCase     ,      tokenizer_name=_UpperCAmelCase     ,      train_batch_size=2     ,      eval_batch_size=2     ,      do_predict=_UpperCAmelCase     ,      task=_UpperCAmelCase     ,      src_lang='en_XX'     ,      tgt_lang='ro_RO'     ,      freeze_encoder=_UpperCAmelCase     ,      freeze_embeds=_UpperCAmelCase     ,      )

										# test models whose config includes the extra_model_args
										__magic_name__				:							str				=      BART_TINY
										__magic_name__				:							List[str]				=      self.get_auto_remove_tmp_dir()
										__magic_name__				:							Any				=      args_d.copy()
										args_da.update(
										    model_name_or_path=_UpperCAmelCase     ,      output_dir=_UpperCAmelCase     ,      )
										__magic_name__				:							List[str]				=      ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
										for p in extra_model_params:
																__magic_name__				:							List[str]				=      0.5
										__magic_name__				:							str				=      argparse.Namespace(**_UpperCAmelCase      )
										__magic_name__				:							Any				=      main(_UpperCAmelCase      )
										for p in extra_model_params:
																assert getattr(model.config     ,      _UpperCAmelCase      ) == 0.5, F"failed to override the model config for param {p}"

										# test models whose config doesn't include the extra_model_args
										__magic_name__				:							List[str]				=      T5_TINY
										__magic_name__				:							str				=      self.get_auto_remove_tmp_dir()
										__magic_name__				:							List[Any]				=      args_d.copy()
										args_da.update(
										    model_name_or_path=_UpperCAmelCase     ,      output_dir=_UpperCAmelCase     ,      )
										__magic_name__				:							Optional[Any]				=      'encoder_layerdrop'
										__magic_name__				:							Optional[Any]				=      0.5
										__magic_name__				:							Optional[int]				=      argparse.Namespace(**_UpperCAmelCase      )
										with pytest.raises(_UpperCAmelCase      ) as excinfo:
																__magic_name__				:							Dict				=      main(_UpperCAmelCase      )
										assert str(excinfo.value      ) == F"model config doesn't have a `{unsupported_param}` attribute"





				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							dict				=      CHEAP_ARGS.copy()

										__magic_name__				:							str				=      'summarization'
										__magic_name__				:							str				=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )

										__magic_name__				:							Union[str, Any]				=      BART_TINY
										__magic_name__				:							str				=      self.get_auto_remove_tmp_dir()

										args_d.update(
										    data_dir=_UpperCAmelCase     ,      model_name_or_path=_UpperCAmelCase     ,      output_dir=_UpperCAmelCase     ,      tokenizer_name=_UpperCAmelCase     ,      train_batch_size=2     ,      eval_batch_size=2     ,      do_predict=_UpperCAmelCase     ,      task=_UpperCAmelCase     ,      src_lang='en_XX'     ,      tgt_lang='ro_RO'     ,      freeze_encoder=_UpperCAmelCase     ,      freeze_embeds=_UpperCAmelCase     ,      )

										# emulate finetune.py
										__magic_name__				:							List[Any]				=      argparse.ArgumentParser()
										__magic_name__				:							List[str]				=      pl.Trainer.add_argparse_args(_UpperCAmelCase      )
										__magic_name__				:							Union[str, Any]				=      SummarizationModule.add_model_specific_args(_UpperCAmelCase     ,      os.getcwd()      )
										__magic_name__				:							Optional[int]				=      {'--help': True}

										# --help test
										with pytest.raises(_UpperCAmelCase      ) as excinfo:
																with CaptureStdout() as cs:
																						__magic_name__				:							Any				=      parser.parse_args(_UpperCAmelCase      )
																assert False, "--help is expected to sys.exit"
										assert excinfo.type == SystemExit
										__magic_name__				:							List[str]				=      lightning_base.arg_to_scheduler_metavar
										assert expected in cs.out, "--help is expected to list the supported schedulers"

										# --lr_scheduler=non_existing_scheduler test
										__magic_name__				:							Optional[int]				=      'non_existing_scheduler'
										__magic_name__				:							Optional[Any]				=      {F"--lr_scheduler={unsupported_param}"}
										with pytest.raises(_UpperCAmelCase      ) as excinfo:
																with CaptureStderr() as cs:
																						__magic_name__				:							int				=      parser.parse_args(_UpperCAmelCase      )
																assert False, "invalid argument is expected to sys.exit"
										assert excinfo.type == SystemExit
										__magic_name__				:							str				=      F"invalid choice: '{unsupported_param}'"
										assert expected in cs.err, F"should have bailed on invalid choice of scheduler {unsupported_param}"

										# --lr_scheduler=existing_scheduler test
										__magic_name__				:							Optional[Any]				=      'cosine'
										__magic_name__				:							Optional[Any]				=      args_d.copy()
										__magic_name__				:							Dict				=      supported_param
										__magic_name__				:							Optional[int]				=      argparse.Namespace(**_UpperCAmelCase      )
										__magic_name__				:							List[str]				=      main(_UpperCAmelCase      )
										assert (
										    getattr(model.hparams     ,      'lr_scheduler'      ) == supported_param
										), F"lr_scheduler={supported_param} shouldn't fail"






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict



class     snake_case__  (      _A  ):
				pass






def  __SCREAMING_SNAKE_CASE       (				lowercase__  :str      ,						lowercase__  :Dict[str, Callable]      ,						lowercase__  :Union[str, Any]=None      ,						lowercase__  :str=False				)       ->  List[str]:
						try:
												__magic_name__				:							str				=      ast.parse(lowercase__				)
						except SyntaxError as e:
												print('The code generated by the agent is not valid.\n'      ,						lowercase__				)
												return
						if state is None:
												__magic_name__				:							Optional[Any]				=      {}
						__magic_name__				:							Dict				=      None
						for idx, node in enumerate(expression.body				):
												try:
																		__magic_name__				:							Any				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
												except InterpretorError as e:
																		__magic_name__				:							Tuple				=      f"Evaluation of the code stopped at line {idx} before the end because of the following error"
																		if chat_mode:
																								msg += (
																								    f". Copy paste the following error message and send it back to the agent:\nI get an error: '{e}'"
																								)
																		else:
																								msg += f":\n{e}"
																		print(lowercase__				)
																		break
												if line_result is not None:
																		__magic_name__				:							List[str]				=      line_result

						return result
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :ast.AST      ,						lowercase__  :Dict[str, Any]      ,						lowercase__  :Dict[str, Callable]				)       ->  Dict:
						if isinstance(lowercase__      ,						ast.Assign				):
												# Assignement -> we evaluate the assignement which should update the state
												# We return the variable assigned as it may be used to determine the final result.
												return evaluate_assign(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.Call				):
												# Function call -> we return the value of the function call
												return evaluate_call(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.Constant				):
												# Constant -> just return the value
												return expression.value
						elif isinstance(lowercase__      ,						ast.Dict				):
												# Dict -> evaluate all keys and values
												__magic_name__				:							Any				=      [evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				) for k in expression.keys]
												__magic_name__				:							Dict				=      [evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				) for v in expression.values]
												return dict(zip(lowercase__      ,						lowercase__				)				)
						elif isinstance(lowercase__      ,						ast.Expr				):
												# Expression -> evaluate the content
												return evaluate_ast(expression.value      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.For				):
												# For loop -> execute the loop
												return evaluate_for(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.FormattedValue				):
												# Formatted value (part of f-string) -> evaluate the content and return
												return evaluate_ast(expression.value      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.If				):
												# If -> execute the right branch
												return evaluate_if(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif hasattr(lowercase__      ,						'Index'				) and isinstance(lowercase__      ,						ast.Index				):
												return evaluate_ast(expression.value      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.JoinedStr				):
												return "".join([str(evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)				) for v in expression.values]				)
						elif isinstance(lowercase__      ,						ast.List				):
												# List -> evaluate all elements
												return [evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				) for elt in expression.elts]
						elif isinstance(lowercase__      ,						ast.Name				):
												# Name -> pick up the value in the state
												return evaluate_name(lowercase__      ,						lowercase__      ,						lowercase__				)
						elif isinstance(lowercase__      ,						ast.Subscript				):
												# Subscript -> return the value of the indexing
												return evaluate_subscript(lowercase__      ,						lowercase__      ,						lowercase__				)
						else:
												# For now we refuse anything else. Let's add things as we need them.
												raise InterpretorError(f"{expression.__class__.__name__} is not supported."				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[Any]      ,						lowercase__  :Optional[Any]      ,						lowercase__  :Union[str, Any]				)       ->  List[Any]:
						__magic_name__				:							Union[str, Any]				=      assign.targets
						__magic_name__				:							int				=      evaluate_ast(assign.value      ,						lowercase__      ,						lowercase__				)

						if len(lowercase__				) == 1:
												__magic_name__				:							Any				=      result
						else:
												if len(lowercase__				) != len(lowercase__				):
																		raise InterpretorError(f"Expected {len(lowercase__				)} values but got {len(lowercase__				)}."				)
												for var_name, r in zip(lowercase__      ,						lowercase__				):
																		__magic_name__				:							str				=      r
						return result
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :List[str]      ,						lowercase__  :Tuple				)       ->  Optional[Any]:
						if not isinstance(call.func      ,						ast.Name				):
												raise InterpretorError(
												    f"It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of "
												    f"type {type(call.func				)}."				)
						__magic_name__				:							Dict				=      call.func.id
						if func_name not in tools:
												raise InterpretorError(
												    f"It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id})."				)

						__magic_name__				:							Tuple				=      tools[func_name]
						# Todo deal with args
						__magic_name__				:							List[str]				=      [evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				) for arg in call.args]
						__magic_name__				:							int				=      {keyword.arg: evaluate_ast(keyword.value      ,						lowercase__      ,						lowercase__				) for keyword in call.keywords}
						return func(*lowercase__      ,						**lowercase__				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Union[str, Any]      ,						lowercase__  :int      ,						lowercase__  :int				)       ->  List[Any]:
						__magic_name__				:							Optional[int]				=      evaluate_ast(subscript.slice      ,						lowercase__      ,						lowercase__				)
						__magic_name__				:							Union[str, Any]				=      evaluate_ast(subscript.value      ,						lowercase__      ,						lowercase__				)
						if isinstance(lowercase__      ,						(list, tuple)				):
												return value[int(lowercase__				)]
						if index in value:
												return value[index]
						if isinstance(lowercase__      ,						lowercase__				) and isinstance(lowercase__      ,						lowercase__				):
												__magic_name__				:							Any				=      difflib.get_close_matches(lowercase__      ,						list(value.keys()				)				)
												if len(lowercase__				) > 0:
																		return value[close_matches[0]]

						raise InterpretorError(f"Could not index {value} with '{index}'."				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[str]      ,						lowercase__  :Optional[Any]      ,						lowercase__  :Optional[int]				)       ->  int:
						if name.id in state:
												return state[name.id]
						__magic_name__				:							Union[str, Any]				=      difflib.get_close_matches(name.id      ,						list(state.keys()				)				)
						if len(lowercase__				) > 0:
												return state[close_matches[0]]
						raise InterpretorError(f"The variable `{name.id}` is not defined."				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Any      ,						lowercase__  :Union[str, Any]      ,						lowercase__  :Optional[Any]				)       ->  List[str]:
						if len(condition.ops				) > 1:
												raise InterpretorError('Cannot evaluate conditions with multiple operators'				)

						__magic_name__				:							int				=      evaluate_ast(condition.left      ,						lowercase__      ,						lowercase__				)
						__magic_name__				:							Optional[int]				=      condition.ops[0]
						__magic_name__				:							Union[str, Any]				=      evaluate_ast(condition.comparators[0]      ,						lowercase__      ,						lowercase__				)

						if isinstance(lowercase__      ,						ast.Eq				):
												return left == right
						elif isinstance(lowercase__      ,						ast.NotEq				):
												return left != right
						elif isinstance(lowercase__      ,						ast.Lt				):
												return left < right
						elif isinstance(lowercase__      ,						ast.LtE				):
												return left <= right
						elif isinstance(lowercase__      ,						ast.Gt				):
												return left > right
						elif isinstance(lowercase__      ,						ast.GtE				):
												return left >= right
						elif isinstance(lowercase__      ,						ast.Is				):
												return left is right
						elif isinstance(lowercase__      ,						ast.IsNot				):
												return left is not right
						elif isinstance(lowercase__      ,						ast.In				):
												return left in right
						elif isinstance(lowercase__      ,						ast.NotIn				):
												return left not in right
						else:
												raise InterpretorError(f"Operator not supported: {comparator}"				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :str      ,						lowercase__  :Tuple				)       ->  int:
						__magic_name__				:							Optional[int]				=      None
						if evaluate_condition(if_statement.test      ,						lowercase__      ,						lowercase__				):
												for line in if_statement.body:
																		__magic_name__				:							List[Any]				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
																		if line_result is not None:
																								__magic_name__				:							Tuple				=      line_result
						else:
												for line in if_statement.orelse:
																		__magic_name__				:							Optional[int]				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
																		if line_result is not None:
																								__magic_name__				:							List[Any]				=      line_result
						return result

def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]      ,						lowercase__  :Union[str, Any]      ,						lowercase__  :List[str]				)       ->  Dict:
						__magic_name__				:							int				=      None
						__magic_name__				:							List[Any]				=      evaluate_ast(for_loop.iter      ,						lowercase__      ,						lowercase__				)
						for counter in iterator:
												__magic_name__				:							Tuple				=      counter
												for expression in for_loop.body:
																		__magic_name__				:							Dict				=      evaluate_ast(lowercase__      ,						lowercase__      ,						lowercase__				)
																		if line_result is not None:
																								__magic_name__				:							Dict				=      line_result
						return result


