




from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.distilbert.modeling_tf_distilbert import (
		    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDistilBertForMaskedLM,
		    TFDistilBertForMultipleChoice,
		    TFDistilBertForQuestionAnswering,
		    TFDistilBertForSequenceClassification,
		    TFDistilBertForTokenClassification,
		    TFDistilBertModel,
		)




class     snake_case__							:
				"""simple docstring"""



				def __init__(      self				:     Tuple					,      __lowerCamelCase				:     str					,      )					->     str:
							a    =			parent
							a    =			13
							a    =			7
							a    =			True
							a    =			True
							a    =			False
							a    =			True
							a    =			99
							a    =			32
							a    =			2
							a    =			4
							a    =			37
							a    =			"gelu"
							a    =			0.1
							a    =			0.1
							a    =			5_12
							a    =			16
							a    =			2
							a    =			0.02
							a    =			3
							a    =			4
							a    =			None





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     int:
							a    =			ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size							)

							a    =			None
							if self.use_input_mask:
										a    =			random_attention_mask([self.batch_size, self.seq_length]							)

							a    =			None
							a    =			None
							a    =			None
							if self.use_labels:
										a    =			ids_tensor([self.batch_size]					,      self.type_sequence_label_size							)
										a    =			ids_tensor([self.batch_size, self.seq_length]					,      self.num_labels							)
										a    =			ids_tensor([self.batch_size]					,      self.num_choices							)

							a    =			DistilBertConfig(
							    vocab_size=self.vocab_size					,      dim=self.hidden_size					,      n_layers=self.num_hidden_layers					,      n_heads=self.num_attention_heads					,      hidden_dim=self.intermediate_size					,      hidden_act=self.hidden_act					,      dropout=self.hidden_dropout_prob					,      attention_dropout=self.attention_probs_dropout_prob					,      max_position_embeddings=self.max_position_embeddings					,      initializer_range=self.initializer_range					,      )

							return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





				def 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Tuple							)					->     Union[str, Any]:
							a    =			TFDistilBertModel(config=UpperCamelCase__							)
							a    =			{"input_ids": input_ids, "attention_mask": input_mask}

							a    =			model(UpperCamelCase__							)

							a    =			[input_ids, input_mask]

							a    =			model(UpperCamelCase__							)

							self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Union[str, Any]							)					->     Any:
							a    =			TFDistilBertForMaskedLM(config=UpperCamelCase__							)
							a    =			{"input_ids": input_ids, "attention_mask": input_mask}
							a    =			model(UpperCamelCase__							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     List[str]							)					->     str:
							a    =			TFDistilBertForQuestionAnswering(config=UpperCamelCase__							)
							a    =			{
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							}
							a    =			model(UpperCamelCase__							)
							self.parent.assertEqual(result.start_logits.shape					,      (self.batch_size, self.seq_length)							)
							self.parent.assertEqual(result.end_logits.shape					,      (self.batch_size, self.seq_length)							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     List[Any]							)					->     str:
							a    =			self.num_labels
							a    =			TFDistilBertForSequenceClassification(UpperCamelCase__							)
							a    =			{"input_ids": input_ids, "attention_mask": input_mask}
							a    =			model(UpperCamelCase__							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     List[str]							)					->     Any:
							a    =			self.num_choices
							a    =			TFDistilBertForMultipleChoice(UpperCamelCase__							)
							a    =			tf.tile(tf.expand_dims(UpperCamelCase__					,      1							)					,      (1, self.num_choices, 1)							)
							a    =			tf.tile(tf.expand_dims(UpperCamelCase__					,      1							)					,      (1, self.num_choices, 1)							)
							a    =			{
							    "input_ids": multiple_choice_inputs_ids,
							    "attention_mask": multiple_choice_input_mask,
							}
							a    =			model(UpperCamelCase__							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_choices)							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     Dict							)					->     Dict:
							a    =			self.num_labels
							a    =			TFDistilBertForTokenClassification(UpperCamelCase__							)
							a    =			{"input_ids": input_ids, "attention_mask": input_mask}
							a    =			model(UpperCamelCase__							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Optional[int]:
							a    =			self.prepare_config_and_inputs()
							(a)    =			config_and_inputs
							a    =			{"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict




@require_tf
class     snake_case__							(snake_case_    ,       snake_case_    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							List[str]							 =					(
				    (
				        TFDistilBertModel,
				        TFDistilBertForMaskedLM,
				        TFDistilBertForQuestionAnswering,
				        TFDistilBertForSequenceClassification,
				        TFDistilBertForTokenClassification,
				        TFDistilBertForMultipleChoice,
				    )
				    if is_tf_available()
				    else None
				)
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					(
				    {
				        '''feature-extraction''': TFDistilBertModel,
				        '''fill-mask''': TFDistilBertForMaskedLM,
				        '''question-answering''': TFDistilBertForQuestionAnswering,
				        '''text-classification''': TFDistilBertForSequenceClassification,
				        '''token-classification''': TFDistilBertForTokenClassification,
				        '''zero-shot''': TFDistilBertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					False
				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					False





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     int:
							a    =			TFDistilBertModelTester(self							)
							a    =			ConfigTester(self					,      config_class=UpperCamelCase__					,      dim=37							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Tuple:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Any:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_model(*UpperCamelCase__							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Dict:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCamelCase__							)





				def 							__UpperCAmelCase							(      self				:     str							)					->     Union[str, Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCamelCase__							)





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Any:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCamelCase__							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[str]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCamelCase__							)





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Optional[int]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCamelCase__							)





				@slow
				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     List[str]:




							for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]							):
										a    =			TFDistilBertModel.from_pretrained(UpperCamelCase__							)
										self.assertIsNotNone(UpperCamelCase__							)




@require_tf
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Dict:
							a    =			TFDistilBertModel.from_pretrained("distilbert-base-uncased"							)
							a    =			tf.constant([[0, 1, 2, 3, 4, 5]]							)
							a    =			model(UpperCamelCase__							)[0]

							a    =			[1, 6, 7_68]
							self.assertEqual(output.shape					,      UpperCamelCase__							)

							a    =			tf.constant(
							    [
							        [
							            [0.19_261_885, -0.13_732_955, 0.4_119_799],
							            [0.22_150_156, -0.07_422_661, 0.39_037_204],
							            [0.22_756_018, -0.0_896_414, 0.3_701_467],
							        ]
							    ]							)
							tf.debugging.assert_near(output[:, :3, :3]					,      UpperCamelCase__					,      atol=1e-4							)










"""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)

__snake_case   			=    logging.getLogger()







def   __lowerCAmelCase						(						)	->						str:


    """simple docstring"""





    snake_case    :      List[str]      					=    argparse.ArgumentParser()
    parser.add_argument("-f" )
    snake_case    :      Tuple      					=    parser.parse_args()
    return args.f







def   __lowerCAmelCase						(						lowercase  :  Optional[int] )	->						Dict:


    """simple docstring"""





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







def   __lowerCAmelCase						(						)	->						Tuple:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    torch.cuda.is_available() and torch_device == "cuda"
    return is_using_cuda and is_apex_available()


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







class _lowerCAmelCase				(      snake_case_	):







   @classmethod
   def  lowerCamelCase		(	cls						)	->		Optional[Any]:

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    tempfile.mkdtemp()
       snake_case    :      int      					=    os.path.join(cls.tmpdir						,	"default_config.yml"						)
       write_basic_config(save_location=cls.configPath						)
       snake_case    :      Union[str, Any]      					=    ["accelerate", "launch", "--config_file", cls.configPath]







   @classmethod
   def  lowerCamelCase		(	cls						)	->		Optional[Any]:

       '''simple docstring'''




       shutil.rmtree(cls.tmpdir						)







   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      str      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      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						)
       snake_case    :      Union[str, Any]      					=    get_results(UpperCamelCase__						)
       self.assertGreaterEqual(result["eval_accuracy"]						,	0.75						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"epoch_0"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"glue_no_trainer"						)						)						)







   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      Dict      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      Dict      					=    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						)
       snake_case    :      Any      					=    get_results(UpperCamelCase__						)
       self.assertLess(result["perplexity"]						,	100						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"epoch_0"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"clm_no_trainer"						)						)						)







   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      str      					=    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						)
       snake_case    :      Dict      					=    get_results(UpperCamelCase__						)
       self.assertLess(result["perplexity"]						,	42						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"epoch_0"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"mlm_no_trainer"						)						)						)







   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    7 if get_gpu_count() > 1 else 2

       snake_case    :      Optional[int]      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      List[str]      					=    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						)
       snake_case    :      str      					=    get_results(UpperCamelCase__						)
       self.assertGreaterEqual(result["eval_accuracy"]						,	0.75						)
       self.assertLess(result["train_loss"]						,	0.5						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"epoch_0"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"ner_no_trainer"						)						)						)







   @unittest.skip(reason="Fix me @muellerzr"						)
   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      Dict      					=    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						)
       snake_case    :      Dict      					=    get_results(UpperCamelCase__						)
       # 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(UpperCamelCase__						,	"epoch_0"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"qa_no_trainer"						)						)						)







   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      List[str]      					=    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						)
       snake_case    :      Optional[Any]      					=    get_results(UpperCamelCase__						)
       self.assertGreaterEqual(result["eval_accuracy"]						,	0.8						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"swag_no_trainer"						)						)						)







   @slow
   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      Dict      					=    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						)
       snake_case    :      Any      					=    get_results(UpperCamelCase__						)
       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(UpperCamelCase__						,	"epoch_0"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"summarization_no_trainer"						)						)						)







   @slow
   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      int      					=    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						)
       snake_case    :      Optional[int]      					=    get_results(UpperCamelCase__						)
       self.assertGreaterEqual(result["eval_bleu"]						,	30						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"epoch_0"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"translation_no_trainer"						)						)						)







   @slow
   def  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      str      					=    logging.StreamHandler(sys.stdout						)
       logger.addHandler(UpperCamelCase__						)

       snake_case    :      Dict      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      Dict      					=    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						)
       snake_case    :      Optional[Any]      					=    get_results(UpperCamelCase__						)
       self.assertGreaterEqual(result["eval_overall_accuracy"]						,	0.10						)







   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      Dict      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      str      					=    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						)
       snake_case    :      str      					=    get_results(UpperCamelCase__						)
       # The base model scores a 25%
       self.assertGreaterEqual(result["eval_accuracy"]						,	0.6						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"step_1"						)						)						)
       self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__						,	"image_classification_no_trainer"						)						)						)





