


"""simple docstring"""







import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__				    =       logging.get_logger(__name__)
def  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ):


		'''simple docstring'''







		lowerCAmelCase					:					Union[str, Any]								=							OrderedDict()
		for key, value in state_dict.items():
				if key.startswith("module.encoder"    ):
						lowerCAmelCase					:					Dict								=							key.replace("module.encoder"    ,				"glpn.encoder"    )
				if key.startswith("module.decoder"    ):
						lowerCAmelCase					:					str								=							key.replace("module.decoder"    ,				"decoder.stages"    )
				if "patch_embed" in key:
						# replace for example patch_embed1 by patch_embeddings.0
						lowerCAmelCase					:					str								=							key[key.find("patch_embed"    ) + len("patch_embed"    )]
						lowerCAmelCase					:					Dict								=							key.replace(f"""patch_embed{idx}"""    ,				f"""patch_embeddings.{int(a_    )-1}"""    )
				if "norm" in key:
						lowerCAmelCase					:					int								=							key.replace("norm"    ,				"layer_norm"    )
				if "glpn.encoder.layer_norm" in key:
						# replace for example layer_norm1 by layer_norm.0
						lowerCAmelCase					:					Optional[int]								=							key[key.find("glpn.encoder.layer_norm"    ) + len("glpn.encoder.layer_norm"    )]
						lowerCAmelCase					:					Any								=							key.replace(f"""layer_norm{idx}"""    ,				f"""layer_norm.{int(a_    )-1}"""    )
				if "layer_norm1" in key:
						lowerCAmelCase					:					str								=							key.replace("layer_norm1"    ,				"layer_norm_1"    )
				if "layer_norm2" in key:
						lowerCAmelCase					:					Optional[Any]								=							key.replace("layer_norm2"    ,				"layer_norm_2"    )
				if "block" in key:
						# replace for example block1 by block.0
						lowerCAmelCase					:					Union[str, Any]								=							key[key.find("block"    ) + len("block"    )]
						lowerCAmelCase					:					List[Any]								=							key.replace(f"""block{idx}"""    ,				f"""block.{int(a_    )-1}"""    )
				if "attn.q" in key:
						lowerCAmelCase					:					Tuple								=							key.replace("attn.q"    ,				"attention.self.query"    )
				if "attn.proj" in key:
						lowerCAmelCase					:					List[str]								=							key.replace("attn.proj"    ,				"attention.output.dense"    )
				if "attn" in key:
						lowerCAmelCase					:					Union[str, Any]								=							key.replace("attn"    ,				"attention.self"    )
				if "fc1" in key:
						lowerCAmelCase					:					int								=							key.replace("fc1"    ,				"dense1"    )
				if "fc2" in key:
						lowerCAmelCase					:					List[Any]								=							key.replace("fc2"    ,				"dense2"    )
				if "linear_pred" in key:
						lowerCAmelCase					:					Dict								=							key.replace("linear_pred"    ,				"classifier"    )
				if "linear_fuse" in key:
						lowerCAmelCase					:					Optional[int]								=							key.replace("linear_fuse.conv"    ,				"linear_fuse"    )
						lowerCAmelCase					:					Optional[int]								=							key.replace("linear_fuse.bn"    ,				"batch_norm"    )
				if "linear_c" in key:
						# replace for example linear_c4 by linear_c.3
						lowerCAmelCase					:					int								=							key[key.find("linear_c"    ) + len("linear_c"    )]
						lowerCAmelCase					:					Any								=							key.replace(f"""linear_c{idx}"""    ,				f"""linear_c.{int(a_    )-1}"""    )
				if "bot_conv" in key:
						lowerCAmelCase					:					int								=							key.replace("bot_conv"    ,				"0.convolution"    )
				if "skip_conv1" in key:
						lowerCAmelCase					:					Tuple								=							key.replace("skip_conv1"    ,				"1.convolution"    )
				if "skip_conv2" in key:
						lowerCAmelCase					:					Optional[int]								=							key.replace("skip_conv2"    ,				"2.convolution"    )
				if "fusion1" in key:
						lowerCAmelCase					:					Dict								=							key.replace("fusion1"    ,				"1.fusion"    )
				if "fusion2" in key:
						lowerCAmelCase					:					str								=							key.replace("fusion2"    ,				"2.fusion"    )
				if "fusion3" in key:
						lowerCAmelCase					:					Optional[int]								=							key.replace("fusion3"    ,				"3.fusion"    )
				if "fusion" in key and "conv" in key:
						lowerCAmelCase					:					str								=							key.replace("conv"    ,				"convolutional_layer"    )
				if key.startswith("module.last_layer_depth"    ):
						lowerCAmelCase					:					Dict								=							key.replace("module.last_layer_depth"    ,				"head.head"    )
				lowerCAmelCase					:					Dict								=							value

		return new_state_dict
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Any    ):


		'''simple docstring'''







		for i in range(config.num_encoder_blocks    ):
				for j in range(config.depths[i]    ):
						# read in weights + bias of keys and values (which is a single matrix in the original implementation)
						lowerCAmelCase					:					Union[str, Any]								=							state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight"""    )
						lowerCAmelCase					:					Any								=							state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias"""    )
						# next, add keys and values (in that order) to the state dict
						lowerCAmelCase					:					Optional[int]								=							kv_weight[
						    : config.hidden_sizes[i], :
						]
						lowerCAmelCase					:					Tuple								=							kv_bias[: config.hidden_sizes[i]]
						lowerCAmelCase					:					Tuple								=							kv_weight[
						    config.hidden_sizes[i] :, :
						]
						lowerCAmelCase					:					str								=							kv_bias[config.hidden_sizes[i] :]
def  a__      (    ):


		'''simple docstring'''







		lowerCAmelCase					:					Optional[Any]								=							"http://images.cocodataset.org/val2017/000000039769.jpg"
		lowerCAmelCase					:					List[str]								=							Image.open(requests.get(a_    ,				stream=a_    ).raw    )

		return image
@torch.no_grad()
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   List[Any]=False    ,				SCREAMING_SNAKE_CASE   :   List[str]=None    ):


		'''simple docstring'''







		lowerCAmelCase					:					List[Any]								=							GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]    ,				decoder_hidden_size=6_4    ,				depths=[3, 8, 2_7, 3]    )

		# load image processor (only resize + rescale)
		lowerCAmelCase					:					List[str]								=							GLPNImageProcessor()

		# prepare image
		lowerCAmelCase					:					Optional[Any]								=							prepare_img()
		lowerCAmelCase					:					Tuple								=							image_processor(images=a_    ,				return_tensors="pt"    ).pixel_values

		logger.info("Converting model..."    )

		# load original state dict
		lowerCAmelCase					:					Dict								=							torch.load(a_    ,				map_location=torch.device("cpu"    )    )

		# rename keys
		lowerCAmelCase					:					List[Any]								=							rename_keys(a_    )

		# key and value matrices need special treatment
		read_in_k_v(a_    ,				a_    )

		# create HuggingFace model and load state dict
		lowerCAmelCase					:					List[str]								=							GLPNForDepthEstimation(a_    )
		model.load_state_dict(a_    )
		model.eval()

		# forward pass
		lowerCAmelCase					:					Union[str, Any]								=							model(a_    )
		lowerCAmelCase					:					Tuple								=							outputs.predicted_depth

		# verify output
		if model_name is not None:
				if "nyu" in model_name:
						lowerCAmelCase					:					Dict								=							torch.tensor(
						    [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]    )
				elif "kitti" in model_name:
						lowerCAmelCase					:					Optional[int]								=							torch.tensor(
						    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]    )
				else:
						raise ValueError(f"""Unknown model name: {model_name}"""    )

				lowerCAmelCase					:					List[str]								=							torch.Size([1, 4_8_0, 6_4_0]    )

				assert predicted_depth.shape == expected_shape
				assert torch.allclose(predicted_depth[0, :3, :3]    ,				a_    ,				atol=1E-4    )
				print("Looks ok!"    )

		# finally, push to hub if required
		if push_to_hub:
				logger.info("Pushing model and image processor to the hub..."    )
				model.push_to_hub(
				    repo_path_or_name=Path(a_    ,				a_    )    ,				organization="nielsr"    ,				commit_message="Add model"    ,				use_temp_dir=a_    ,				)
				image_processor.push_to_hub(
				    repo_path_or_name=Path(a_    ,				a_    )    ,				organization="nielsr"    ,				commit_message="Add image processor"    ,				use_temp_dir=a_    ,				)


if __name__ == "__main__":
					lowerCAmelCase__				    =       argparse.ArgumentParser()

					parser.add_argument(
					    '''--checkpoint_path''',
					    default=None,
					    type=str,
					    help='''Path to the original PyTorch checkpoint (.pth file).''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
					)
					parser.add_argument(
					    '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
					)
					parser.add_argument(
					    '''--model_name''',
					    default='''glpn-kitti''',
					    type=str,
					    help='''Name of the model in case you\'re pushing to the hub.''',
					)
					lowerCAmelCase__				    =       parser.parse_args()
					convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









"""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"						)						)						)





