


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase__				    =       {
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase__				    =       [
         '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''Data2VecAudioForAudioFrameClassification''',
         '''Data2VecAudioForCTC''',
         '''Data2VecAudioForSequenceClassification''',
         '''Data2VecAudioForXVector''',
         '''Data2VecAudioModel''',
         '''Data2VecAudioPreTrainedModel''',
     ]
     lowerCAmelCase__				    =       [
         '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''Data2VecTextForCausalLM''',
         '''Data2VecTextForMaskedLM''',
         '''Data2VecTextForMultipleChoice''',
         '''Data2VecTextForQuestionAnswering''',
         '''Data2VecTextForSequenceClassification''',
         '''Data2VecTextForTokenClassification''',
         '''Data2VecTextModel''',
         '''Data2VecTextPreTrainedModel''',
     ]
     lowerCAmelCase__				    =       [
         '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''Data2VecVisionForImageClassification''',
         '''Data2VecVisionForMaskedImageModeling''',
         '''Data2VecVisionForSemanticSegmentation''',
         '''Data2VecVisionModel''',
         '''Data2VecVisionPreTrainedModel''',
     ]

if is_tf_available():
     lowerCAmelCase__				    =       [
         '''TFData2VecVisionForImageClassification''',
         '''TFData2VecVisionForSemanticSegmentation''',
         '''TFData2VecVisionModel''',
         '''TFData2VecVisionPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
     from .configuration_dataavec_text import (
         DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DataaVecTextConfig,
         DataaVecTextOnnxConfig,
     )
     from .configuration_dataavec_vision import (
         DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DataaVecVisionConfig,
         DataaVecVisionOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dataavec_audio import (
              DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
              DataaVecAudioForAudioFrameClassification,
              DataaVecAudioForCTC,
              DataaVecAudioForSequenceClassification,
              DataaVecAudioForXVector,
              DataaVecAudioModel,
              DataaVecAudioPreTrainedModel,
          )
          from .modeling_dataavec_text import (
              DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DataaVecTextForCausalLM,
              DataaVecTextForMaskedLM,
              DataaVecTextForMultipleChoice,
              DataaVecTextForQuestionAnswering,
              DataaVecTextForSequenceClassification,
              DataaVecTextForTokenClassification,
              DataaVecTextModel,
              DataaVecTextPreTrainedModel,
          )
          from .modeling_dataavec_vision import (
              DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
              DataaVecVisionForImageClassification,
              DataaVecVisionForMaskedImageModeling,
              DataaVecVisionForSemanticSegmentation,
              DataaVecVisionModel,
              DataaVecVisionPreTrainedModel,
          )
     if is_tf_available():
          from .modeling_tf_dataavec_vision import (
              TFDataaVecVisionForImageClassification,
              TFDataaVecVisionForSemanticSegmentation,
              TFDataaVecVisionModel,
              TFDataaVecVisionPreTrainedModel,
          )

else:
     import sys

     lowerCAmelCase__				    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









"""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__				    =       logging.getLogger()
def  a__      (    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							argparse.ArgumentParser()
  parser.add_argument("-f"    )
  lowerCAmelCase					:					Tuple								=							parser.parse_args()
  return args.f
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ):


  '''simple docstring'''







  lowerCAmelCase					:					Union[str, Any]								=							{}
  lowerCAmelCase					:					List[str]								=							os.path.join(SCREAMING_SNAKE_CASE    ,				"all_results.json"    )
  if os.path.exists(SCREAMING_SNAKE_CASE    ):
    with open(SCREAMING_SNAKE_CASE    ,				"r"    ) as f:
      lowerCAmelCase					:					str								=							json.load(SCREAMING_SNAKE_CASE    )
  else:
    raise ValueError(f"""can't find {path}"""    )
  return results
def  a__      (    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							torch.cuda.is_available() and torch_device == "cuda"
  return is_using_cuda and is_apex_available()


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





class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       @classmethod
       def 	lowercase__							(						cls						):






         """simple docstring"""





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

       @classmethod
       def 	lowercase__							(						cls						):






         """simple docstring"""





         shutil.rmtree(cls.tmpdir						)

       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					int								=							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						)
         lowerCAmelCase					:					Dict								=							get_results(snake_case__						)
         self.assertGreaterEqual(result["eval_accuracy"]		,					0.75						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"epoch_0"						)						)						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"glue_no_trainer"						)						)						)

       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					Tuple								=							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						)
         lowerCAmelCase					:					List[str]								=							get_results(snake_case__						)
         self.assertLess(result["perplexity"]		,					100						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"epoch_0"						)						)						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"clm_no_trainer"						)						)						)

       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





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

       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							7 if get_gpu_count() > 1 else 2

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

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






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					List[str]								=							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						)
         lowerCAmelCase					:					Optional[Any]								=							get_results(snake_case__						)
         # 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(snake_case__		,					"epoch_0"						)						)						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"qa_no_trainer"						)						)						)

       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





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

       @slow
       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					Tuple								=							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						)
         lowerCAmelCase					:					str								=							get_results(snake_case__						)
         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(snake_case__		,					"epoch_0"						)						)						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"summarization_no_trainer"						)						)						)

       @slow
       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					Tuple								=							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						)
         lowerCAmelCase					:					int								=							get_results(snake_case__						)
         self.assertGreaterEqual(result["eval_bleu"]		,					30						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"epoch_0"						)						)						)
         self.assertTrue(os.path.exists(os.path.join(snake_case__		,					"translation_no_trainer"						)						)						)

       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							logging.StreamHandler(sys.stdout						)
         logger.addHandler(snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase					:					Any								=							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						)
         lowerCAmelCase					:					int								=							get_results(snake_case__						)
         self.assertGreaterEqual(result["eval_overall_accuracy"]		,					0.10						)







       @mock.patch.dict(os.environ		,					{"WANDB_MODE": "offline"}						)
       def 	lowercase__							(						self						):






         """simple docstring"""





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





