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'''






       _A=	argparse.ArgumentParser()
       parser.add_argument('-f'   )
       _A=	parser.parse_args()
       return args.f

def 							UpperCamelCase				(    lowerCAmelCase_   ):




       '''simple docstring'''






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

def 							UpperCamelCase				(    ):




       '''simple docstring'''






       _A=	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      lowerCAmelCase						(					_a			):



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



     @classmethod
     def 			a__  (					cls       ):
            shutil.rmtree(cls.tmpdir       )



     @mock.patch.dict(os.environ				,      {'WANDB_MODE': 'offline'}       )
     def 			a__  (					self       ):
            _A=	self.get_auto_remove_tmp_dir()
            _A=	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       )
            _A=	get_results(lowerCAmelCase__       )
            self.assertGreaterEqual(result['eval_accuracy']				,      0.75       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'epoch_0'       )       )       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'glue_no_trainer'       )       )       )



     @mock.patch.dict(os.environ				,      {'WANDB_MODE': 'offline'}       )
     def 			a__  (					self       ):
            _A=	self.get_auto_remove_tmp_dir()
            _A=	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       )
            _A=	get_results(lowerCAmelCase__       )
            self.assertLess(result['perplexity']				,      100       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'epoch_0'       )       )       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'clm_no_trainer'       )       )       )



     @mock.patch.dict(os.environ				,      {'WANDB_MODE': 'offline'}       )
     def 			a__  (					self       ):
            _A=	self.get_auto_remove_tmp_dir()
            _A=	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       )
            _A=	get_results(lowerCAmelCase__       )
            self.assertLess(result['perplexity']				,      42       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'epoch_0'       )       )       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'mlm_no_trainer'       )       )       )



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

            _A=	self.get_auto_remove_tmp_dir()
            _A=	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       )
            _A=	get_results(lowerCAmelCase__       )
            self.assertGreaterEqual(result['eval_accuracy']				,      0.75       )
            self.assertLess(result['train_loss']				,      0.5       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'epoch_0'       )       )       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'ner_no_trainer'       )       )       )



     @unittest.skip(reason='Fix me @muellerzr'       )
     @mock.patch.dict(os.environ				,      {'WANDB_MODE': 'offline'}       )
     def 			a__  (					self       ):
            _A=	self.get_auto_remove_tmp_dir()
            _A=	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       )
            _A=	get_results(lowerCAmelCase__       )
            # 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(lowerCAmelCase__				,      'epoch_0'       )       )       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'qa_no_trainer'       )       )       )



     @mock.patch.dict(os.environ				,      {'WANDB_MODE': 'offline'}       )
     def 			a__  (					self       ):
            _A=	self.get_auto_remove_tmp_dir()
            _A=	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       )
            _A=	get_results(lowerCAmelCase__       )
            self.assertGreaterEqual(result['eval_accuracy']				,      0.8       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'swag_no_trainer'       )       )       )



     @slow
     @mock.patch.dict(os.environ				,      {'WANDB_MODE': 'offline'}       )
     def 			a__  (					self       ):
            _A=	self.get_auto_remove_tmp_dir()
            _A=	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       )
            _A=	get_results(lowerCAmelCase__       )
            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(lowerCAmelCase__				,      'epoch_0'       )       )       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'summarization_no_trainer'       )       )       )



     @slow
     @mock.patch.dict(os.environ				,      {'WANDB_MODE': 'offline'}       )
     def 			a__  (					self       ):
            _A=	self.get_auto_remove_tmp_dir()
            _A=	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       )
            _A=	get_results(lowerCAmelCase__       )
            self.assertGreaterEqual(result['eval_bleu']				,      30       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'epoch_0'       )       )       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'translation_no_trainer'       )       )       )



     @slow
     def 			a__  (					self       ):
            _A=	logging.StreamHandler(sys.stdout       )
            logger.addHandler(lowerCAmelCase__       )

            _A=	self.get_auto_remove_tmp_dir()
            _A=	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       )
            _A=	get_results(lowerCAmelCase__       )
            self.assertGreaterEqual(result['eval_overall_accuracy']				,      0.10       )





     @mock.patch.dict(os.environ				,      {'WANDB_MODE': 'offline'}       )
     def 			a__  (					self       ):
            _A=	self.get_auto_remove_tmp_dir()
            _A=	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       )
            _A=	get_results(lowerCAmelCase__       )
            # The base model scores a 25%
            self.assertGreaterEqual(result['eval_accuracy']				,      0.6       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'step_1'       )       )       )
            self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__				,      'image_classification_no_trainer'       )       )       )
import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
      import jax

      from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
      from transformers.models.bert.modeling_flax_bert import FlaxBertModel
      from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel





@require_flax
class      lowerCAmelCase						(					unittest.TestCase			):



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxBertModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxRobertaModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'bert-base is not a local folder and is not a valid model identifier'       ):
                   _A=	FlaxAutoModel.from_pretrained('bert-base'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'       ):
                   _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__				,      revision='aaaaaa'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'				,      ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'       )





     def 			a__  (					self       ):
            with self.assertRaisesRegex(lowerCAmelCase__				,      'Use `from_pt=True` to load this model'       ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'       )