
"""simple docstring"""






import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


__UpperCAmelCase       =  pytest.mark.integration




@require_faiss
class 			_SCREAMING_SNAKE_CASE					(			A__							):






			def  __lowerCAmelCase (					self       )			->     Any:
						lowerCAmelCase_    :List[str]			     =  Dataset.from_dict({"""filename""": ["""my_name-train""" + """_""" + str(__UpperCAmelCase       ) for x in np.arange(30       ).tolist()]}       )
						return dset






			def  __lowerCAmelCase (					self       )			->     Any:
						import faiss

						lowerCAmelCase_    :int			     =  self._create_dummy_dataset()
						lowerCAmelCase_    :List[str]			     =  dset.map(
						    lambda __A       ,   __A       : {"vecs": i * np.ones(5       ,   dtype=np.floataa       )}       ,   with_indices=__UpperCAmelCase       ,   keep_in_memory=__UpperCAmelCase       )
						lowerCAmelCase_    :List[Any]			     =  dset.add_faiss_index("""vecs"""       ,   batch_size=100       ,   metric_type=faiss.METRIC_INNER_PRODUCT       )
						lowerCAmelCase_     ,       lowerCAmelCase_    :List[Any]			     =  dset.get_nearest_examples("""vecs"""       ,   np.ones(5       ,   dtype=np.floataa       )       )
						self.assertEqual(examples["""filename"""][0]       ,   """my_name-train_29"""       )
						dset.drop_index("""vecs"""       )






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						import faiss

						lowerCAmelCase_    :Any			     =  self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)       ) * np.arange(30       ).reshape(-1       ,   1       )       ,   index_name="""vecs"""       ,   batch_size=100       ,   metric_type=faiss.METRIC_INNER_PRODUCT       ,   )
						lowerCAmelCase_     ,       lowerCAmelCase_    :Optional[Any]			     =  dset.get_nearest_examples("""vecs"""       ,   np.ones(5       ,   dtype=np.floataa       )       )
						self.assertEqual(examples["""filename"""][0]       ,   """my_name-train_29"""       )






			def  __lowerCAmelCase (					self       )			->     Union[str, Any]:
						import faiss

						lowerCAmelCase_    :List[str]			     =  self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)       ) * np.arange(30       ).reshape(-1       ,   1       )       ,   index_name="""vecs"""       ,   metric_type=faiss.METRIC_INNER_PRODUCT       ,   )

						# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
						# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
						# see https://bugs.python.org/issue14243 and
						# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
						with tempfile.NamedTemporaryFile(delete=__UpperCAmelCase       ) as tmp_file:
									dset.save_faiss_index("""vecs"""       ,   tmp_file.name       )
									dset.load_faiss_index("""vecs2"""       ,   tmp_file.name       )
						os.unlink(tmp_file.name       )

						lowerCAmelCase_     ,       lowerCAmelCase_    :List[Any]			     =  dset.get_nearest_examples("""vecs2"""       ,   np.ones(5       ,   dtype=np.floataa       )       )
						self.assertEqual(examples["""filename"""][0]       ,   """my_name-train_29"""       )






			def  __lowerCAmelCase (					self       )			->     Any:
						lowerCAmelCase_    :List[str]			     =  self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)       ) * np.arange(30       ).reshape(-1       ,   1       )       ,   index_name="""vecs"""       )
						dset.drop_index("""vecs"""       )
						self.assertRaises(__UpperCAmelCase       ,   partial(dset.get_nearest_examples       ,   """vecs2"""       ,   np.ones(5       ,   dtype=np.floataa       )       )       )






			def  __lowerCAmelCase (					self       )			->     List[str]:
						from elasticsearch import Elasticsearch

						lowerCAmelCase_    :Optional[Any]			     =  self._create_dummy_dataset()
						with patch("""elasticsearch.Elasticsearch.search"""       ) as mocked_search, patch(
						    """elasticsearch.client.IndicesClient.create"""       ) as mocked_index_create, patch("""elasticsearch.helpers.streaming_bulk"""       ) as mocked_bulk:
									lowerCAmelCase_    :List[Any]			     =  {"""acknowledged""": True}
									mocked_bulk.return_value([(True, None)] * 30       )
									lowerCAmelCase_    :Tuple			     =  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 29}]}}
									lowerCAmelCase_    :Union[str, Any]			     =  Elasticsearch()

									dset.add_elasticsearch_index("""filename"""       ,   es_client=__UpperCAmelCase       )
									lowerCAmelCase_     ,       lowerCAmelCase_    :Dict			     =  dset.get_nearest_examples("""filename"""       ,   """my_name-train_29"""       )
									self.assertEqual(examples["""filename"""][0]       ,   """my_name-train_29"""       )




@require_faiss
class 			_SCREAMING_SNAKE_CASE					(			A__							):






			def  __lowerCAmelCase (					self       )			->     Any:
						import faiss

						lowerCAmelCase_    :Tuple			     =  FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT       )

						# add vectors
						index.add_vectors(np.eye(5       ,   dtype=np.floataa       )       )
						self.assertIsNotNone(index.faiss_index       )
						self.assertEqual(index.faiss_index.ntotal       ,   5       )
						index.add_vectors(np.zeros((5, 5)       ,   dtype=np.floataa       )       )
						self.assertEqual(index.faiss_index.ntotal       ,   10       )

						# single query
						lowerCAmelCase_    :Union[str, Any]			     =  np.zeros(5       ,   dtype=np.floataa       )
						lowerCAmelCase_    :Union[str, Any]			     =  1
						lowerCAmelCase_     ,       lowerCAmelCase_    :Optional[int]			     =  index.search(__UpperCAmelCase       )
						self.assertRaises(__UpperCAmelCase       ,   index.search       ,   query.reshape(-1       ,   1       )       )
						self.assertGreater(scores[0]       ,   0       )
						self.assertEqual(indices[0]       ,   1       )

						# batched queries
						lowerCAmelCase_    :Optional[Any]			     =  np.eye(5       ,   dtype=np.floataa       )[::-1]
						lowerCAmelCase_     ,       lowerCAmelCase_    :List[str]			     =  index.search_batch(__UpperCAmelCase       )
						self.assertRaises(__UpperCAmelCase       ,   index.search_batch       ,   queries[0]       )
						lowerCAmelCase_    :Optional[Any]			     =  [scores[0] for scores in total_scores]
						lowerCAmelCase_    :str			     =  [indices[0] for indices in total_indices]
						self.assertGreater(np.min(__UpperCAmelCase       )       ,   0       )
						self.assertListEqual([4, 3, 2, 1, 0]       ,   __UpperCAmelCase       )






			def  __lowerCAmelCase (					self       )			->     List[Any]:
						import faiss

						lowerCAmelCase_    :Optional[Any]			     =  FaissIndex(string_factory="""Flat"""       )
						index.add_vectors(np.eye(5       ,   dtype=np.floataa       )       )
						self.assertIsInstance(index.faiss_index       ,   faiss.IndexFlat       )
						lowerCAmelCase_    :Tuple			     =  FaissIndex(string_factory="""LSH"""       )
						index.add_vectors(np.eye(5       ,   dtype=np.floataa       )       )
						self.assertIsInstance(index.faiss_index       ,   faiss.IndexLSH       )
						with self.assertRaises(__UpperCAmelCase       ):
									lowerCAmelCase_    :List[str]			     =  FaissIndex(string_factory="""Flat"""       ,   custom_index=faiss.IndexFlat(5       )       )






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						import faiss

						lowerCAmelCase_    :Optional[int]			     =  faiss.IndexFlat(5       )
						lowerCAmelCase_    :List[Any]			     =  FaissIndex(custom_index=__UpperCAmelCase       )
						index.add_vectors(np.eye(5       ,   dtype=np.floataa       )       )
						self.assertIsInstance(index.faiss_index       ,   faiss.IndexFlat       )






			def  __lowerCAmelCase (					self       )			->     Dict:
						import faiss

						lowerCAmelCase_    :Optional[Any]			     =  FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT       )
						index.add_vectors(np.eye(5       ,   dtype=np.floataa       )       )

						# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
						# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
						# see https://bugs.python.org/issue14243 and
						# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
						with tempfile.NamedTemporaryFile(delete=__UpperCAmelCase       ) as tmp_file:
									index.save(tmp_file.name       )
									lowerCAmelCase_    :List[Any]			     =  FaissIndex.load(tmp_file.name       )
						os.unlink(tmp_file.name       )

						lowerCAmelCase_    :Any			     =  np.zeros(5       ,   dtype=np.floataa       )
						lowerCAmelCase_    :Union[str, Any]			     =  1
						lowerCAmelCase_     ,       lowerCAmelCase_    :Union[str, Any]			     =  index.search(__UpperCAmelCase       )
						self.assertGreater(scores[0]       ,   0       )
						self.assertEqual(indices[0]       ,   1       )






@require_faiss
def 						_snake_case       (	lowercase__		:  Dict		)  ->				Any:


			'''simple docstring'''





			import faiss

			lowerCAmelCase_    :str			     =  FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT		)
			index.add_vectors(np.eye(5      ,     dtype=np.floataa		)		)

			lowerCAmelCase_    :Union[str, Any]			     =  """index.faiss"""
			lowerCAmelCase_    :int			     =  f"""mock://{index_name}"""
			index.save(lowercase__      ,     storage_options=mockfs.storage_options		)
			lowerCAmelCase_    :Any			     =  FaissIndex.load(lowercase__      ,     storage_options=mockfs.storage_options		)

			lowerCAmelCase_    :Tuple			     =  np.zeros(5      ,     dtype=np.floataa		)
			lowerCAmelCase_    :Union[str, Any]			     =  1
			lowerCAmelCase_     ,       lowerCAmelCase_    :int			     =  index.search(lowercase__		)
			assert scores[0] > 0
			assert indices[0] == 1




@require_elasticsearch
class 			_SCREAMING_SNAKE_CASE					(			A__							):






			def  __lowerCAmelCase (					self       )			->     List[Any]:
						from elasticsearch import Elasticsearch

						with patch("""elasticsearch.Elasticsearch.search"""       ) as mocked_search, patch(
						    """elasticsearch.client.IndicesClient.create"""       ) as mocked_index_create, patch("""elasticsearch.helpers.streaming_bulk"""       ) as mocked_bulk:
									lowerCAmelCase_    :List[str]			     =  Elasticsearch()
									lowerCAmelCase_    :Optional[Any]			     =  {"""acknowledged""": True}
									lowerCAmelCase_    :str			     =  ElasticSearchIndex(es_client=__UpperCAmelCase       )
									mocked_bulk.return_value([(True, None)] * 3       )
									index.add_documents(["""foo""", """bar""", """foobar"""]       )

									# single query
									lowerCAmelCase_    :Any			     =  """foo"""
									lowerCAmelCase_    :Any			     =  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
									lowerCAmelCase_     ,       lowerCAmelCase_    :int			     =  index.search(__UpperCAmelCase       )
									self.assertEqual(scores[0]       ,   1       )
									self.assertEqual(indices[0]       ,   0       )

									# single query with timeout
									lowerCAmelCase_    :int			     =  """foo"""
									lowerCAmelCase_    :Any			     =  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
									lowerCAmelCase_     ,       lowerCAmelCase_    :Union[str, Any]			     =  index.search(__UpperCAmelCase       ,   request_timeout=30       )
									self.assertEqual(scores[0]       ,   1       )
									self.assertEqual(indices[0]       ,   0       )

									# batched queries
									lowerCAmelCase_    :Optional[int]			     =  ["""foo""", """bar""", """foobar"""]
									lowerCAmelCase_    :int			     =  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
									lowerCAmelCase_     ,       lowerCAmelCase_    :Optional[Any]			     =  index.search_batch(__UpperCAmelCase       )
									lowerCAmelCase_    :Optional[Any]			     =  [scores[0] for scores in total_scores]
									lowerCAmelCase_    :int			     =  [indices[0] for indices in total_indices]
									self.assertGreater(np.min(__UpperCAmelCase       )       ,   0       )
									self.assertListEqual([1, 1, 1]       ,   __UpperCAmelCase       )

									# batched queries with timeout
									lowerCAmelCase_    :int			     =  ["""foo""", """bar""", """foobar"""]
									lowerCAmelCase_    :Any			     =  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
									lowerCAmelCase_     ,       lowerCAmelCase_    :Optional[int]			     =  index.search_batch(__UpperCAmelCase       ,   request_timeout=30       )
									lowerCAmelCase_    :Union[str, Any]			     =  [scores[0] for scores in total_scores]
									lowerCAmelCase_    :Dict			     =  [indices[0] for indices in total_indices]
									self.assertGreater(np.min(__UpperCAmelCase       )       ,   0       )
									self.assertListEqual([1, 1, 1]       ,   __UpperCAmelCase       )














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 						_a    (      )							->			Optional[int]:
 a		  =      argparse.ArgumentParser()
 parser.add_argument('''-f''' )
 a		  =      parser.parse_args()
 return args.f
def 						_a    (      a :Any )							->			Tuple:
 a		  =      {}
 a		  =      os.path.join(a	,							'''all_results.json''' )
 if os.path.exists(a ):
  with open(a	,							'''r''' ) as f:
   a		  =      json.load(a )
 else:
  raise ValueError(F"""can't find {path}""" )
 return results
def 						_a    (      )							->			int:
 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 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      @classmethod
      def       __lowerCAmelCase					(      cls      :						str  )    ->Tuple:




       """simple docstring"""


       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       __lowerCAmelCase					(      cls      :						Optional[int]  )    ->Union[str, Any]:




       """simple docstring"""


       shutil.rmtree(cls.tmpdir  )







      @mock.patch.dict(os.environ ,					{'''WANDB_MODE''': '''offline'''}  )
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      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  )
       a		  =      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      :						Optional[Any]  )    ->Any:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      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  )
       a		  =      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      :						Optional[int]  )    ->int:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      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  )
       a		  =      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[int]  )    ->str:




       """simple docstring"""


       a		  =      7 if get_gpu_count() > 1 else 2

       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      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  )
       a		  =      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      :						Any  )    ->int:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      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  )
       a		  =      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      :						Optional[Any]  )    ->Any:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      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  )
       a		  =      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      :						Union[str, Any]  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      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  )
       a		  =      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      :						Optional[int]  )    ->List[str]:




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      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  )
       a		  =      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      :						List[str]  )    ->int:




       """simple docstring"""


       a		  =      logging.StreamHandler(sys.stdout  )
       logger.addHandler(__UpperCAmelCase  )

       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      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  )
       a		  =      get_results(__UpperCAmelCase  )
       self.assertGreaterEqual(result['''eval_overall_accuracy'''] ,					0.10  )







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




       """simple docstring"""


       a		  =      self.get_auto_remove_tmp_dir()
       a		  =      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  )
       a		  =      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'''  )  )  )


