


"""simple docstring"""







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


lowerCAmelCase__				    =       logging.get_logger(__name__)

lowerCAmelCase__				    =       {
    'shi-labs/dinat-mini-in1k-224': 'https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}


class 				SCREAMING_SNAKE_CASE__    (		__SCREAMING_SNAKE_CASE       ,   __SCREAMING_SNAKE_CASE    ):
							"""simple docstring"""


							a  :    Optional[int]       ="dinat"

							a  :    int       ={
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}

							def __init__(						self		,					snake_case__=4		,					snake_case__=3		,					snake_case__=64		,					snake_case__=[3, 4, 6, 5]		,					snake_case__=[2, 4, 8, 16]		,					snake_case__=7		,					snake_case__=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]		,					snake_case__=3.0		,					snake_case__=True		,					snake_case__=0.0		,					snake_case__=0.0		,					snake_case__=0.1		,					snake_case__="gelu"		,					snake_case__=0.02		,					snake_case__=1e-5		,					snake_case__=0.0		,					snake_case__=None		,					snake_case__=None		,					**snake_case__		,					):






									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)

									lowerCAmelCase					:					int								=							patch_size
									lowerCAmelCase					:					Dict								=							num_channels
									lowerCAmelCase					:					Optional[int]								=							embed_dim
									lowerCAmelCase					:					str								=							depths
									lowerCAmelCase					:					Union[str, Any]								=							len(_SCREAMING_SNAKE_CASE						)
									lowerCAmelCase					:					str								=							num_heads
									lowerCAmelCase					:					List[Any]								=							kernel_size
									lowerCAmelCase					:					List[Any]								=							dilations
									lowerCAmelCase					:					Any								=							mlp_ratio
									lowerCAmelCase					:					Dict								=							qkv_bias
									lowerCAmelCase					:					int								=							hidden_dropout_prob
									lowerCAmelCase					:					Dict								=							attention_probs_dropout_prob
									lowerCAmelCase					:					Any								=							drop_path_rate
									lowerCAmelCase					:					Tuple								=							hidden_act
									lowerCAmelCase					:					Union[str, Any]								=							layer_norm_eps
									lowerCAmelCase					:					Optional[int]								=							initializer_range
									# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									lowerCAmelCase					:					str								=							int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE						) - 1)						)
									lowerCAmelCase					:					Optional[int]								=							layer_scale_init_value
									lowerCAmelCase					:					Optional[int]								=							["stem"] + [f"""stage{idx}""" for idx in range(1		,					len(_SCREAMING_SNAKE_CASE						) + 1						)]
									lowerCAmelCase					:					Any								=							get_aligned_output_features_output_indices(
									    out_features=_SCREAMING_SNAKE_CASE		,					out_indices=_SCREAMING_SNAKE_CASE		,					stage_names=self.stage_names						)









"""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"						)						)						)





