





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)

UpperCamelCase_												=	logging.getLogger()






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=argparse.ArgumentParser()
   parser.add_argument("-f"     )
   UpperCAmelCase__						=parser.parse_args()
   return args.f






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						={}
   UpperCAmelCase__						=os.path.join(A				,							"all_results.json"     )
   if os.path.exists(A     ):
      with open(A				,							"r"     ) as f:
         UpperCAmelCase__						=json.load(A     )
   else:
      raise ValueError(F"""can't find {path}"""     )
   return results






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=torch.cuda.is_available() and torch_device == "cuda"
   return is_using_cuda and is_apex_available()


UpperCamelCase_												=	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class     snake_case_		(   a							):






 '''simple docstring'''





 @classmethod
 def  __UpperCAmelCase						(  cls		)	->       Optional[Any]:
    # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
    UpperCAmelCase__						=tempfile.mkdtemp()
    UpperCAmelCase__						=os.path.join(cls.tmpdir,		"default_config.yml"		)
    write_basic_config(save_location=cls.configPath		)
    UpperCAmelCase__						=["accelerate", "launch", "--config_file", cls.configPath]

 @classmethod
 def  __UpperCAmelCase						(  cls		)	->       Tuple:
    shutil.rmtree(cls.tmpdir		)

 @mock.patch.dict(os.environ,		{"WANDB_MODE": "offline"}		)
 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=self.get_auto_remove_tmp_dir()
    UpperCAmelCase__						=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		)
    UpperCAmelCase__						=get_results(A_		)
    self.assertGreaterEqual(result["eval_accuracy"],		0.75		)
    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  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    UpperCAmelCase__						=self.get_auto_remove_tmp_dir()
    UpperCAmelCase__						=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		)
    UpperCAmelCase__						=get_results(A_		)
    self.assertLess(result["perplexity"],		100		)
    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  __UpperCAmelCase						(  self		)	->       Tuple:
    UpperCAmelCase__						=self.get_auto_remove_tmp_dir()
    UpperCAmelCase__						=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		)
    UpperCAmelCase__						=get_results(A_		)
    self.assertLess(result["perplexity"],		42		)
    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  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
    UpperCAmelCase__						=7 if get_gpu_count() > 1 else 2

    UpperCAmelCase__						=self.get_auto_remove_tmp_dir()
    UpperCAmelCase__						=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		)
    UpperCAmelCase__						=get_results(A_		)
    self.assertGreaterEqual(result["eval_accuracy"],		0.75		)
    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  __UpperCAmelCase						(  self		)	->       Any:
    UpperCAmelCase__						=self.get_auto_remove_tmp_dir()
    UpperCAmelCase__						=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		)
    UpperCAmelCase__						=get_results(A_		)
    # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
    self.assertGreaterEqual(result["eval_f1"],		28		)
    self.assertGreaterEqual(result["eval_exact"],		28		)
    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  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						=self.get_auto_remove_tmp_dir()
    UpperCAmelCase__						=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		)
    UpperCAmelCase__						=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  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=self.get_auto_remove_tmp_dir()
    UpperCAmelCase__						=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		)
    UpperCAmelCase__						=get_results(A_		)
    self.assertGreaterEqual(result["eval_rouge1"],		10		)
    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  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=self.get_auto_remove_tmp_dir()
    UpperCAmelCase__						=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		)
    UpperCAmelCase__						=get_results(A_		)
    self.assertGreaterEqual(result["eval_bleu"],		30		)
    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  __UpperCAmelCase						(  self		)	->       Optional[int]:
    UpperCAmelCase__						=logging.StreamHandler(sys.stdout		)
    logger.addHandler(A_		)

    UpperCAmelCase__						=self.get_auto_remove_tmp_dir()
    UpperCAmelCase__						=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		)
    UpperCAmelCase__						=get_results(A_		)
    self.assertGreaterEqual(result["eval_overall_accuracy"],		0.10		)






 @mock.patch.dict(os.environ,		{"WANDB_MODE": "offline"}		)
 def  __UpperCAmelCase						(  self		)	->       str:
    UpperCAmelCase__						=self.get_auto_remove_tmp_dir()
    UpperCAmelCase__						=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		)
    UpperCAmelCase__						=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"		)		)		)










from __future__ import annotations

import requests

UpperCamelCase_												=	set(
    'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports'.split()
)






def 				_UpperCAmelCase			(    A				,							A = 1				,							A = "new"				,							A = None     ):




   '''simple docstring'''

   UpperCAmelCase__						=wanted_data or []
   if invalid_search_terms := ", ".join(sorted(set(A     ) - valid_terms     )     ):
      UpperCAmelCase__						=F"""Invalid search term: {invalid_search_terms}"""
      raise ValueError(A     )
   UpperCAmelCase__						=requests.get(
       F"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"""				,							headers={"User-agent": "A random string"}				,							)
   if response.status_code == 429:
      raise requests.HTTPError

   UpperCAmelCase__						=response.json()
   if not wanted_data:
      return {id_: data["data"]["children"][id_] for id_ in range(A     )}

   UpperCAmelCase__						={}
   for id_ in range(A     ):
      UpperCAmelCase__						={
          item: data["data"]["children"][id_]["data"][item] for item in wanted_data
      }
   return data_dict


if __name__ == "__main__":
    # If you get Error 429, that means you are rate limited.Try after some time
    print(get_subreddit_data('learnpython', wanted_data=['title', 'url', 'selftext']))



