"""simple docstring"""


import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


__UpperCamelCase								=    torch.cuda.is_available()
__UpperCamelCase								=    [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__UpperCamelCase								=    ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__UpperCamelCase								=    {
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1E-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   Optional[Any]:
     snake_case_       =      '\n'.join(UpperCAmelCase      )
     Path(UpperCAmelCase      ).open('w'      ).writelines(UpperCAmelCase      )







def       UpperCAmelCase					(	UpperCAmelCase      )       ->   List[str]:
     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(UpperCAmelCase		,      f'{split}.source'      )		,      UpperCAmelCase      )
          _dump_articles(os.path.join(UpperCAmelCase		,      f'{split}.target'      )		,      UpperCAmelCase      )
     return tmp_dir




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       @classmethod
       def 	a_		(				cls)   ->   List[Any]:
            return cls







       @require_torch_multi_gpu
       def 	a_		(				self)   ->   Union[str, Any]:
            snake_case_       =      {
                'no_teacher': True,
                'freeze_encoder': True,
                'gpus': 2,
                'overwrite_output_dir': True,
                'sortish_sampler': True,
            }
            self._test_distiller_cli_fork(lowerCAmelCase__,							check_contents=lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__=True)   ->   str:
            snake_case_       =      {
                'label_smoothing': 0.0,
                'early_stopping_patience': -1,
                'train_batch_size': 1,
                'eval_batch_size': 2,
                'max_epochs': 2,
                'alpha_mlm': 0.2,
                'alpha_ce': 0.8,
                'do_predict': True,
                'model_name_or_path': 'sshleifer/tinier_bart',
                'teacher': CHEAP_ARGS['model_name_or_path'],
                'val_check_interval': 0.5,
            }
            default_updates.update(lowerCAmelCase__)
            snake_case_       =      CHEAP_ARGS.copy()
            snake_case_       =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
            snake_case_       =      self.get_auto_remove_tmp_dir()
            args_d.update(data_dir=lowerCAmelCase__,							output_dir=lowerCAmelCase__,							**lowerCAmelCase__)

            def convert(lowerCAmelCase__,							lowerCAmelCase__):
                 if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                      return ""
                 if v is False or v is None:
                      return ""
                 if v is True:  # or len(str(v))==0:
                      return f'--{k}'
                 return f'--{k}={v}'

            snake_case_       =      [x for x in (convert(lowerCAmelCase__,							lowerCAmelCase__) for k, v in args_d.items()) if len(lowerCAmelCase__)]
            snake_case_       =      [sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
            execute_subprocess_async(lowerCAmelCase__,							env=self.get_env())

            snake_case_       =      os.listdir(lowerCAmelCase__)
            snake_case_       =      {os.path.basename(lowerCAmelCase__) for p in contents}
            snake_case_       =      [p for p in contents if p.endswith('ckpt')]
            assert len(lowerCAmelCase__) > 0

            self.assertIn('test_generations.txt',							lowerCAmelCase__)
            self.assertIn('test_results.txt',							lowerCAmelCase__)

            # get the following from the module, (we don't have access to `model` here)
            snake_case_       =      os.path.join(lowerCAmelCase__,							'metrics.json')
            snake_case_       =      'rouge2'

            snake_case_       =      load_json(lowerCAmelCase__)
            # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
            print(lowerCAmelCase__)
            snake_case_       =      metrics['val'][-1]
            self.assertGreaterEqual(last_step_stats['val_avg_gen_time'],							0.01)
            self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}'],							lowerCAmelCase__)
            self.assertEqual(len(metrics['test']),							1)
            snake_case_       =      int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1)
            self.assertEqual(len(metrics['val']),							lowerCAmelCase__)


"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase								=    logging.get_logger(__name__)

__UpperCamelCase								=    {
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=256,							lowerCAmelCase__=1024,							lowerCAmelCase__=16,							lowerCAmelCase__=16,							lowerCAmelCase__=4,							lowerCAmelCase__="gelu",							lowerCAmelCase__=1e-6,							**lowerCAmelCase__,							)   ->   int:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      hidden_size
            snake_case_       =      image_size
            snake_case_       =      patch_size
            snake_case_       =      image_size // patch_size
            snake_case_       =      mask_input_channels
            snake_case_       =      num_point_embeddings
            snake_case_       =      hidden_act
            snake_case_       =      layer_norm_eps




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=256,							lowerCAmelCase__="relu",							lowerCAmelCase__=2048,							lowerCAmelCase__=2,							lowerCAmelCase__=8,							lowerCAmelCase__=2,							lowerCAmelCase__=3,							lowerCAmelCase__=3,							lowerCAmelCase__=256,							lowerCAmelCase__=1e-6,							**lowerCAmelCase__,							)   ->   List[str]:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      hidden_size
            snake_case_       =      hidden_act
            snake_case_       =      mlp_dim
            snake_case_       =      num_hidden_layers
            snake_case_       =      num_attention_heads
            snake_case_       =      attention_downsample_rate
            snake_case_       =      num_multimask_outputs
            snake_case_       =      iou_head_depth
            snake_case_       =      iou_head_hidden_dim
            snake_case_       =      layer_norm_eps




class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def __init__(				self,							lowerCAmelCase__=768,							lowerCAmelCase__=256,							lowerCAmelCase__=12,							lowerCAmelCase__=12,							lowerCAmelCase__=3,							lowerCAmelCase__=1024,							lowerCAmelCase__=16,							lowerCAmelCase__="gelu",							lowerCAmelCase__=1e-06,							lowerCAmelCase__=0.0,							lowerCAmelCase__=1e-10,							lowerCAmelCase__=True,							lowerCAmelCase__=4.0,							lowerCAmelCase__=True,							lowerCAmelCase__=True,							lowerCAmelCase__=14,							lowerCAmelCase__=[2, 5, 8, 11],							lowerCAmelCase__=128,							lowerCAmelCase__=None,							**lowerCAmelCase__,							)   ->   List[str]:
            super().__init__(**lowerCAmelCase__)

            snake_case_       =      hidden_size
            snake_case_       =      output_channels
            snake_case_       =      num_hidden_layers
            snake_case_       =      num_attention_heads
            snake_case_       =      num_channels
            snake_case_       =      image_size
            snake_case_       =      patch_size
            snake_case_       =      hidden_act
            snake_case_       =      layer_norm_eps
            snake_case_       =      attention_dropout
            snake_case_       =      initializer_range
            snake_case_       =      qkv_bias
            snake_case_       =      mlp_ratio
            snake_case_       =      use_abs_pos
            snake_case_       =      use_rel_pos
            snake_case_       =      window_size
            snake_case_       =      global_attn_indexes
            snake_case_       =      num_pos_feats
            snake_case_       =      int(hidden_size * mlp_ratio) if mlp_dim is None else mlp_dim




class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "sam"
       SCREAMING_SNAKE_CASE_				=   True







       def __init__(				self,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=0.02,							**lowerCAmelCase__,							)   ->   Any:
            super().__init__(**lowerCAmelCase__)
            snake_case_       =      vision_config if vision_config is not None else {}
            snake_case_       =      prompt_encoder_config if prompt_encoder_config is not None else {}
            snake_case_       =      mask_decoder_config if mask_decoder_config is not None else {}

            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      vision_config.to_dict()
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      prompt_encoder_config.to_dict()
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      mask_decoder_config.to_dict()

            snake_case_       =      SamVisionConfig(**lowerCAmelCase__)
            snake_case_       =      SamPromptEncoderConfig(**lowerCAmelCase__)
            snake_case_       =      SamMaskDecoderConfig(**lowerCAmelCase__)
            snake_case_       =      initializer_range







       def 	a_		(				self)   ->   Dict:
            snake_case_       =      copy.deepcopy(self.__dict__)
            snake_case_       =      self.vision_config.to_dict()
            snake_case_       =      self.prompt_encoder_config.to_dict()
            snake_case_       =      self.mask_decoder_config.to_dict()
            snake_case_       =      self.__class__.model_type
            return output

