



"""simple docstring"""




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    : List[Any]							=							logging.getLogger()

def 						__snake_case						(				)  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[Any]        =   argparse.ArgumentParser()
     parser.add_argument("-f"    )
     _UpperCAmelCase      :     Optional[Any]        =   parser.parse_args()
     return args.f

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[int]        =   {}
     _UpperCAmelCase      :     Any        =   os.path.join(SCREAMING_SNAKE_CASE__						,  "all_results.json"    )
     if os.path.exists(SCREAMING_SNAKE_CASE__    ):
          with open(SCREAMING_SNAKE_CASE__						,  "r"    ) as f:
               _UpperCAmelCase      :     str        =   json.load(SCREAMING_SNAKE_CASE__    )
     else:
          raise ValueError(f'can\'t find {path}'    )
     return results

def 						__snake_case						(				)  ->       List[Any]:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[Any]        =   torch.cuda.is_available() and torch_device == "cuda"
     return is_using_cuda and is_apex_available()


_lowerCAmelCase    : Any							=							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class      UpperCAmelCase_  (    _UpperCamelCase						):



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



      @classmethod
      def 						snake_case_ (					cls					:     Dict						):
           shutil.rmtree(cls.tmpdir						)



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

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

           run_command(self._launch_args + testargs						)
           _UpperCAmelCase      :     List[Any]        =   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 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.get_auto_remove_tmp_dir()
           _UpperCAmelCase      :     Union[str, Any]        =   f'\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        '.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      :     Optional[Any]        =   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 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     Optional[int]        =   self.get_auto_remove_tmp_dir()
           _UpperCAmelCase      :     List[Any]        =   f'\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

           run_command(self._launch_args + testargs						)
           _UpperCAmelCase      :     Union[str, Any]        =   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 						snake_case_ (					self					:     Dict						):
           # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
           _UpperCAmelCase      :     str        =   7 if get_gpu_count() > 1 else 2

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

           run_command(self._launch_args + testargs						)
           _UpperCAmelCase      :     str        =   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 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     Any        =   self.get_auto_remove_tmp_dir()
           _UpperCAmelCase      :     Tuple        =   f'\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

           run_command(self._launch_args + testargs						)
           _UpperCAmelCase      :     Optional[Any]        =   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 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     Optional[Any]        =   self.get_auto_remove_tmp_dir()
           _UpperCAmelCase      :     Any        =   f'\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        '.split()

           run_command(self._launch_args + testargs						)
           _UpperCAmelCase      :     Any        =   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 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     Optional[int]        =   self.get_auto_remove_tmp_dir()
           _UpperCAmelCase      :     Union[str, Any]        =   f'\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

           run_command(self._launch_args + testargs						)
           _UpperCAmelCase      :     Union[str, 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 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.get_auto_remove_tmp_dir()
           _UpperCAmelCase      :     Tuple        =   f'\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

           run_command(self._launch_args + testargs						)
           _UpperCAmelCase      :     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 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     Dict        =   logging.StreamHandler(sys.stdout						)
           logger.addHandler(A						)

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

           run_command(self._launch_args + testargs						)
           _UpperCAmelCase      :     Dict        =   get_results(A						)
           self.assertGreaterEqual(result["eval_overall_accuracy"]					,     0.10						)



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

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

           run_command(self._launch_args + testargs						)
           _UpperCAmelCase      :     Dict        =   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"						)						)						)





"""simple docstring"""




# 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 re

from ..utils import cached_file


# docstyle-ignore
_lowerCAmelCase    : Any							=							"\nHuman: <<task>>\n\nAssistant: "


_lowerCAmelCase    : str							=							"huggingface-tools/default-prompts"
_lowerCAmelCase    : Union[str, Any]							=							{"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						Optional[int]						,  SCREAMING_SNAKE_CASE__     :						int="run"    )  ->       int:





     '''simple docstring'''

     if prompt_or_repo_id is None:
          _UpperCAmelCase      :     Optional[int]        =   DEFAULT_PROMPTS_REPO

     # prompt is considered a repo ID when it does not contain any kind of space
     if re.search("\\s"						,  SCREAMING_SNAKE_CASE__    ) is not None:
          return prompt_or_repo_id

     _UpperCAmelCase      :     Dict        =   cached_file(
         SCREAMING_SNAKE_CASE__						,  PROMPT_FILES[mode]						,  repo_type="dataset"						,  user_agent={"agent": agent_name}    )
     with open(SCREAMING_SNAKE_CASE__						,  "r"						,  encoding="utf-8"    ) as f:
          return f.read()
