





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_					:   Tuple 							=						logging.getLogger()






def 							_lowerCAmelCase (  )	->			Union[str, Any]:
     lowerCAmelCase_					:       List[str]    =		argparse.ArgumentParser()
     parser.add_argument("""-f"""			)
     lowerCAmelCase_					:       Dict    =		parser.parse_args()
     return args.f






def 							_lowerCAmelCase (  _a   :					Tuple			)	->			Optional[Any]:
     lowerCAmelCase_					:       Union[str, Any]    =		{}
     lowerCAmelCase_					:       Union[str, Any]    =		os.path.join(_a					,   """all_results.json"""			)
     if os.path.exists(_a			):
          with open(_a					,   """r"""			) as f:
               lowerCAmelCase_					:       Dict    =		json.load(_a			)
     else:
          raise ValueError(F'can\'t find {path}'			)
     return results






def 							_lowerCAmelCase (  )	->			Optional[Any]:
     lowerCAmelCase_					:       List[Any]    =		torch.cuda.is_available() and torch_device == """cuda"""
     return is_using_cuda and is_apex_available()


UpperCAmelCase_					:   Dict 							=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class      lowercase__    (							__A  ):







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







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







  @mock.patch.dict(os.environ    ,						{"""WANDB_MODE""": """offline"""}						)
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Dict    =		self.get_auto_remove_tmp_dir()
       lowerCAmelCase_					:       Optional[Any]    =		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						)
       lowerCAmelCase_					:       Any    =		get_results(_lowercase						)
       self.assertGreaterEqual(result["""eval_accuracy"""]    ,						0.75						)
       self.assertTrue(os.path.exists(os.path.join(_lowercase    ,						"""epoch_0"""						)						)						)
       self.assertTrue(os.path.exists(os.path.join(_lowercase    ,						"""glue_no_trainer"""						)						)						)







  @mock.patch.dict(os.environ    ,						{"""WANDB_MODE""": """offline"""}						)
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Any    =		self.get_auto_remove_tmp_dir()
       lowerCAmelCase_					:       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						)
       lowerCAmelCase_					:       Tuple    =		get_results(_lowercase						)
       self.assertLess(result["""perplexity"""]    ,						100						)
       self.assertTrue(os.path.exists(os.path.join(_lowercase    ,						"""epoch_0"""						)						)						)
       self.assertTrue(os.path.exists(os.path.join(_lowercase    ,						"""clm_no_trainer"""						)						)						)







  @mock.patch.dict(os.environ    ,						{"""WANDB_MODE""": """offline"""}						)
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Any    =		self.get_auto_remove_tmp_dir()
       lowerCAmelCase_					:       int    =		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						)
       lowerCAmelCase_					:       List[str]    =		get_results(_lowercase						)
       self.assertLess(result["""perplexity"""]    ,						42						)
       self.assertTrue(os.path.exists(os.path.join(_lowercase    ,						"""epoch_0"""						)						)						)
       self.assertTrue(os.path.exists(os.path.join(_lowercase    ,						"""mlm_no_trainer"""						)						)						)







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

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







  @unittest.skip(reason="""Fix me @muellerzr"""						)
  @mock.patch.dict(os.environ    ,						{"""WANDB_MODE""": """offline"""}						)
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       int    =		self.get_auto_remove_tmp_dir()
       lowerCAmelCase_					:       Tuple    =		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						)
       lowerCAmelCase_					:       Optional[int]    =		get_results(_lowercase						)
       # 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(_lowercase    ,						"""epoch_0"""						)						)						)
       self.assertTrue(os.path.exists(os.path.join(_lowercase    ,						"""qa_no_trainer"""						)						)						)







  @mock.patch.dict(os.environ    ,						{"""WANDB_MODE""": """offline"""}						)
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Any    =		self.get_auto_remove_tmp_dir()
       lowerCAmelCase_					:       List[Any]    =		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						)
       lowerCAmelCase_					:       Any    =		get_results(_lowercase						)
       self.assertGreaterEqual(result["""eval_accuracy"""]    ,						0.8						)
       self.assertTrue(os.path.exists(os.path.join(_lowercase    ,						"""swag_no_trainer"""						)						)						)







  @slow
  @mock.patch.dict(os.environ    ,						{"""WANDB_MODE""": """offline"""}						)
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Dict    =		self.get_auto_remove_tmp_dir()
       lowerCAmelCase_					:       Optional[Any]    =		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						)
       lowerCAmelCase_					:       Optional[Any]    =		get_results(_lowercase						)
       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(_lowercase    ,						"""epoch_0"""						)						)						)
       self.assertTrue(os.path.exists(os.path.join(_lowercase    ,						"""summarization_no_trainer"""						)						)						)







  @slow
  @mock.patch.dict(os.environ    ,						{"""WANDB_MODE""": """offline"""}						)
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Union[str, Any]    =		self.get_auto_remove_tmp_dir()
       lowerCAmelCase_					:       Optional[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						)
       lowerCAmelCase_					:       List[Any]    =		get_results(_lowercase						)
       self.assertGreaterEqual(result["""eval_bleu"""]    ,						30						)
       self.assertTrue(os.path.exists(os.path.join(_lowercase    ,						"""epoch_0"""						)						)						)
       self.assertTrue(os.path.exists(os.path.join(_lowercase    ,						"""translation_no_trainer"""						)						)						)







  @slow
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Tuple    =		logging.StreamHandler(sys.stdout						)
       logger.addHandler(_lowercase						)

       lowerCAmelCase_					:       Optional[Any]    =		self.get_auto_remove_tmp_dir()
       lowerCAmelCase_					:       Union[str, Any]    =		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						)
       lowerCAmelCase_					:       Optional[Any]    =		get_results(_lowercase						)
       self.assertGreaterEqual(result["""eval_overall_accuracy"""]    ,						0.10						)







  @mock.patch.dict(os.environ    ,						{"""WANDB_MODE""": """offline"""}						)
  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       int    =		self.get_auto_remove_tmp_dir()
       lowerCAmelCase_					:       Union[str, Any]    =		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						)
       lowerCAmelCase_					:       Tuple    =		get_results(_lowercase						)
       # The base model scores a 25%
       self.assertGreaterEqual(result["""eval_accuracy"""]    ,						0.6						)
       self.assertTrue(os.path.exists(os.path.join(_lowercase    ,						"""step_1"""						)						)						)
       self.assertTrue(os.path.exists(os.path.join(_lowercase    ,						"""image_classification_no_trainer"""						)						)						)







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase_					:   List[str] 							=						logging.get_logger(__name__)

UpperCAmelCase_					:   int 							=						{
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}





class      lowercase__    (							__A     ,					__A  ):
  __UpperCamelCase	     =  """bit"""
  __UpperCamelCase	     =  ["""preactivation""", """bottleneck"""]
  __UpperCamelCase	     =  ["""SAME""", """VALID"""]







  def __init__(	self    ,						_lowercase=3    ,						_lowercase=64    ,						_lowercase=[256, 512, 1_024, 2_048]    ,						_lowercase=[3, 4, 6, 3]    ,						_lowercase="preactivation"    ,						_lowercase="relu"    ,						_lowercase=None    ,						_lowercase=32    ,						_lowercase=0.0    ,						_lowercase=False    ,						_lowercase=32    ,						_lowercase=1    ,						_lowercase=None    ,						_lowercase=None    ,						**_lowercase    ,						):
       super().__init__(**_lowercase						)
       if layer_type not in self.layer_types:
            raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
       if global_padding is not None:
            if global_padding.upper() in self.supported_padding:
                 lowerCAmelCase_					:       int    =		global_padding.upper()
            else:
                 raise ValueError(F'Padding strategy {global_padding} not supported'						)
       lowerCAmelCase_					:       Optional[Any]    =		num_channels
       lowerCAmelCase_					:       List[str]    =		embedding_size
       lowerCAmelCase_					:       Dict    =		hidden_sizes
       lowerCAmelCase_					:       Optional[int]    =		depths
       lowerCAmelCase_					:       List[Any]    =		layer_type
       lowerCAmelCase_					:       Union[str, Any]    =		hidden_act
       lowerCAmelCase_					:       Optional[int]    =		global_padding
       lowerCAmelCase_					:       Optional[Any]    =		num_groups
       lowerCAmelCase_					:       Optional[Any]    =		drop_path_rate
       lowerCAmelCase_					:       Tuple    =		embedding_dynamic_padding
       lowerCAmelCase_					:       Any    =		output_stride
       lowerCAmelCase_					:       List[Any]    =		width_factor

       lowerCAmelCase_					:       Union[str, Any]    =		["""stem"""] + [F'stage{idx}' for idx in range(1    ,						len(_lowercase						) + 1						)]
       lowerCAmelCase_   ,  lowerCAmelCase_					:       Tuple    =		get_aligned_output_features_output_indices(
           out_features=_lowercase    ,						out_indices=_lowercase    ,						stage_names=self.stage_names						)
