






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'''  )  )  )







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
     import tensorflow as tf

     from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM






@require_tf
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""		)
        A :       Dict          =   AutoTokenizer.from_pretrained("""google/mt5-small"""		)

        A :       Tuple          =   tokenizer("""Hello there""",		return_tensors="""tf"""		).input_ids
        A :       Optional[int]          =   tokenizer("""Hi I am""",		return_tensors="""tf"""		).input_ids

        A :       List[Any]          =   model(lowerCamelCase__,		labels=lowerCamelCase__		).loss
        A :       str          =   -tf.math.reduce_mean(lowerCamelCase__		).numpy()

        A :       Tuple          =   -21.22_8168
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 2e-4		)


