




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


__SCREAMING_SNAKE_CASE			:      Tuple      			=torch.cuda.is_available()
__SCREAMING_SNAKE_CASE			:      str      			=[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
__SCREAMING_SNAKE_CASE			:      List[str]      			=["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
__SCREAMING_SNAKE_CASE			:      Optional[int]      			={
    """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-0_8,
    """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 	lowerCAmelCase_	(     lowerCamelCase:				Path   , lowerCamelCase:				list  )    ->	int:


      '''simple docstring'''




      lowerCamelCase__					     = '\n'.join(lowerCamelCase  )
      Path(lowerCamelCase  ).open('w'  ).writelines(lowerCamelCase  )

def 	lowerCAmelCase_	(     lowerCamelCase:				Dict  )    ->	Dict:


      '''simple docstring'''




      for split in ["train", "val", "test"]:
            _dump_articles(os.path.join(lowerCamelCase   , f"""{split}.source"""  )   , lowerCamelCase  )
            _dump_articles(os.path.join(lowerCamelCase   , f"""{split}.target"""  )   , lowerCamelCase  )
      return tmp_dir






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     @classmethod
     def        __snake_case      (			cls     :      List[str]	) ->				Dict:
           return cls




     @require_torch_multi_gpu
     def        __snake_case      (			self     :      Optional[Any]	) ->				Tuple:
           lowerCamelCase__					     = {
               'no_teacher': True,
               'freeze_encoder': True,
               'gpus': 2,
               'overwrite_output_dir': True,
               'sortish_sampler': True,
           }
           self._test_distiller_cli_fork(A__		,     check_contents=A__	)




     def        __snake_case      (			self     :      Optional[int]		,     A__     :      List[Any]		,     A__     :      List[str]=True	) ->				List[Any]:
           lowerCamelCase__					     = {
               '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(A__	)
           lowerCamelCase__					     = CHEAP_ARGS.copy()
           lowerCamelCase__					     = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
           lowerCamelCase__					     = self.get_auto_remove_tmp_dir()
           args_d.update(data_dir=A__		,     output_dir=A__		,     **A__	)

           def convert(A__     :      Any		,     A__     :      Any	):
                 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}"""

           lowerCamelCase__					     = [x for x in (convert(A__		,     A__	) for k, v in args_d.items()) if len(A__	)]
           lowerCamelCase__					     = [sys.executable, F"""{self.test_file_dir}/distillation.py"""] + cli_args
           execute_subprocess_async(A__		,     env=self.get_env()	)

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

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

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

           lowerCamelCase__					     = load_json(A__	)
           # {'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(A__	)
           lowerCamelCase__					     = metrics['val'][-1]
           self.assertGreaterEqual(last_step_stats['val_avg_gen_time']		,     0.01	)
           self.assertIsInstance(last_step_stats[F"""val_avg_{val_metric}"""]		,     A__	)
           self.assertEqual(len(metrics['test']	)		,     1	)
           lowerCamelCase__					     = int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1	)
           self.assertEqual(len(metrics['val']	)		,     A__	)





import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
      from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def        __snake_case      (			self     :      Optional[Any]	) ->				List[Any]:
           lowerCamelCase__					     = self.config_class(**self.inputs_dict	)
           self.parent.assertTrue(hasattr(A__		,     'hidden_sizes'	)	)
           self.parent.assertTrue(hasattr(A__		,     'num_encoder_blocks'	)	)






class 	A	:
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int=1_3		,     A__     :      Optional[Any]=6_4		,     A__     :      List[Any]=3		,     A__     :      Dict=4		,     A__     :      int=[2, 2, 2, 2]		,     A__     :      int=[8, 4, 2, 1]		,     A__     :      Any=[1_6, 3_2, 6_4, 1_2_8]		,     A__     :      List[str]=[1, 4, 8, 1_6]		,     A__     :      List[str]=False		,     A__     :      Optional[int]=True		,     A__     :      Any="gelu"		,     A__     :      List[str]=0.1		,     A__     :      Union[str, Any]=0.02		,     A__     :      Any=3		,     A__     :      Dict=None		,     ) ->				List[str]:
           lowerCamelCase__					     = parent
           lowerCamelCase__					     = batch_size
           lowerCamelCase__					     = image_size
           lowerCamelCase__					     = num_channels
           lowerCamelCase__					     = num_encoder_blocks
           lowerCamelCase__					     = sr_ratios
           lowerCamelCase__					     = depths
           lowerCamelCase__					     = hidden_sizes
           lowerCamelCase__					     = downsampling_rates
           lowerCamelCase__					     = is_training
           lowerCamelCase__					     = use_labels
           lowerCamelCase__					     = hidden_act
           lowerCamelCase__					     = hidden_dropout_prob
           lowerCamelCase__					     = initializer_range
           lowerCamelCase__					     = num_labels
           lowerCamelCase__					     = scope




     def        __snake_case      (			self     :      Any	) ->				str:
           lowerCamelCase__					     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           lowerCamelCase__					     = None
           if self.use_labels:
                 lowerCamelCase__					     = ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels	)

           lowerCamelCase__					     = PoolFormerConfig(
               image_size=self.image_size		,     num_channels=self.num_channels		,     num_encoder_blocks=self.num_encoder_blocks		,     depths=self.depths		,     hidden_sizes=self.hidden_sizes		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     initializer_range=self.initializer_range		,     )

           return config, pixel_values, labels




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      Optional[int]		,     A__     :      List[Any]		,     A__     :      Optional[Any]	) ->				str:
           lowerCamelCase__					     = PoolFormerModel(config=A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(A__	)
           lowerCamelCase__					     = lowerCamelCase__					     = self.image_size // 32.0
           self.parent.assertEqual(
               result.last_hidden_state.shape		,     (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)	)




     def        __snake_case      (			self     :      List[str]	) ->				Any:
           lowerCamelCase__					     = self.prepare_config_and_inputs()
           lowerCamelCase__     ,						lowerCamelCase__     ,						lowerCamelCase__					     = config_and_inputs
           lowerCamelCase__					     = {'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class 	A	(						_lowerCAmelCase	,  _lowerCAmelCase	,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		Dict   =(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
     _snake_case:		Dict   =(
         {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     _snake_case:		str   =False
     _snake_case:		Tuple   =False
     _snake_case:		Optional[int]   =False
     _snake_case:		Dict   =False
     _snake_case:		Tuple   =False




     def        __snake_case      (			self     :      List[Any]	) ->				Optional[Any]:
           lowerCamelCase__					     = PoolFormerModelTester(self	)
           lowerCamelCase__					     = PoolFormerConfigTester(self		,     config_class=A__	)




     def        __snake_case      (			self     :      str	) ->				List[str]:
           self.config_tester.run_common_tests()




     def        __snake_case      (			self     :      int	) ->				List[str]:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A__	)




     @unittest.skip('PoolFormer does not use inputs_embeds'	)
     def        __snake_case      (			self     :      Dict	) ->				Tuple:
           pass




     @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'	)
     def        __snake_case      (			self     :      List[str]	) ->				str:
           pass




     def        __snake_case      (			self     :      Dict	) ->				Optional[int]:
           def check_hidden_states_output(A__     :      str		,     A__     :      List[Any]		,     A__     :      List[Any]	):
                 lowerCamelCase__					     = model_class(A__	)
                 model.to(A__	)
                 model.eval()

                 with torch.no_grad():
                       lowerCamelCase__					     = model(**self._prepare_for_class(A__		,     A__	)	)

                 lowerCamelCase__					     = outputs.hidden_states

                 lowerCamelCase__					     = self.model_tester.num_encoder_blocks
                 self.assertEqual(len(A__	)		,     A__	)

                 # verify the first hidden states (first block)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-3:]	)		,     [
                         self.model_tester.hidden_sizes[0],
                         self.model_tester.image_size // 4,
                         self.model_tester.image_size // 4,
                     ]		,     )

           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase__					     = True
                 check_hidden_states_output(A__		,     A__		,     A__	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCamelCase__					     = True

                 check_hidden_states_output(A__		,     A__		,     A__	)




     def        __snake_case      (			self     :      Any	) ->				Tuple:
           if not self.model_tester.is_training:
                 return

           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase__					     = True

           for model_class in self.all_model_classes:
                 if model_class in get_values(A__	):
                       continue
                 lowerCamelCase__					     = model_class(A__	)
                 model.to(A__	)
                 model.train()
                 lowerCamelCase__					     = self._prepare_for_class(A__		,     A__		,     return_labels=A__	)
                 lowerCamelCase__					     = model(**A__	).loss
                 loss.backward()




     def        __snake_case      (			self     :      Tuple	) ->				Any:
           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase__					     = model_class(A__	)
                 lowerCamelCase__					     = inspect.signature(model.forward	)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCamelCase__					     = [*signature.parameters.keys()]

                 lowerCamelCase__					     = ['pixel_values']
                 self.assertListEqual(arg_names[:1]		,     A__	)




     @slow
     def        __snake_case      (			self     :      Dict	) ->				int:
           for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase__					     = PoolFormerModel.from_pretrained(A__	)
                 self.assertIsNotNone(A__	)

def 	lowerCAmelCase_	(     )    ->	Dict:


      '''simple docstring'''




      lowerCamelCase__					     = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
      return image






@require_torch
class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @slow
     def        __snake_case      (			self     :      Any	) ->				Dict:
           lowerCamelCase__					     = PoolFormerImageProcessor()
           lowerCamelCase__					     = PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'	).to(A__	)

           lowerCamelCase__					     = image_processor(images=prepare_img()		,     return_tensors='pt'	).to(A__	)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase__					     = model(**A__	)

           # verify the logits
           lowerCamelCase__					     = torch.Size((1, 1_0_0_0)	)
           self.assertEqual(outputs.logits.shape		,     A__	)

           lowerCamelCase__					     = torch.tensor([-0.6113, 0.1685, -0.0492]	).to(A__	)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]		,     A__		,     atol=1E-4	)	)