


'''simple docstring'''



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any]				)	->						Optional[int]:
     __lowerCamelCase  :      int														=						{}
     __lowerCamelCase  :      int														=						tokenizer(example['content'] ,       truncation=UpperCAmelCase_				)['input_ids']
     __lowerCamelCase  :      List[str]														=						len(example['content']				) / len(output['input_ids']				)
     return output


A__						:   Tuple				=    HfArgumentParser(PretokenizationArguments)
A__						:   Dict				=    parser.parse_args()
if args.num_workers is None:
      A__						:   Any				=    multiprocessing.cpu_count()
A__						:   Optional[Any]				=    AutoTokenizer.from_pretrained(args.tokenizer_dir)

A__						:   Union[str, Any]				=    time.time()
A__						:   List[Any]				=    load_dataset(args.dataset_name, split="""train""")
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')

A__						:   Optional[Any]				=    time.time()
A__						:   Optional[Any]				=    ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(f'''Dataset tokenized in {time.time()-t_start:.2f}s''')

A__						:   Optional[int]				=    time.time()
ds.push_to_hub(args.tokenized_data_repo)

print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')











import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
				import torch

				from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class     __a			:



	def __init__(    self				:     Union[str, Any]					,						snake_case_				:     Dict					,						snake_case_				:     Tuple=None					,						snake_case_				:     List[str]=None					,						snake_case_				:     Union[str, Any]=None					,						snake_case_				:     str="resnet50"					,						snake_case_				:     List[Any]=3					,						snake_case_				:     Optional[int]=32					,						snake_case_				:     Union[str, Any]=3					,						snake_case_				:     Tuple=True					,						snake_case_				:     List[str]=True					,						)->				Optional[Any]:
						__lowerCAmelCase						 =parent
						__lowerCAmelCase						 =out_indices if out_indices is not None else [4]
						__lowerCAmelCase						 =stage_names
						__lowerCAmelCase						 =out_features
						__lowerCAmelCase						 =backbone
						__lowerCAmelCase						 =batch_size
						__lowerCAmelCase						 =image_size
						__lowerCAmelCase						 =num_channels
						__lowerCAmelCase						 =use_pretrained_backbone
						__lowerCAmelCase						 =is_training



	def 		UpperCamelCase				(    self				:     int)->				Dict:
						__lowerCAmelCase						 =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
						__lowerCAmelCase						 =self.get_config()

						return config, pixel_values



	def 		UpperCamelCase				(    self				:     Optional[int])->				Optional[int]:
						return TimmBackboneConfig(
						    image_size=self.image_size					,						num_channels=self.num_channels					,						out_features=self.out_features					,						out_indices=self.out_indices					,						stage_names=self.stage_names					,						use_pretrained_backbone=self.use_pretrained_backbone					,						backbone=self.backbone					,						)



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     int					,						snake_case_				:     Union[str, Any])->				str:
						__lowerCAmelCase						 =TimmBackbone(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						with torch.no_grad():
											__lowerCAmelCase						 =model(snake_case_)
						self.parent.assertEqual(
						    result.feature_map[-1].shape					,						(self.batch_size, model.channels[-1], 14, 14)					,						)



	def 		UpperCamelCase				(    self				:     List[str])->				Union[str, Any]:
						__lowerCAmelCase						 =self.prepare_config_and_inputs()
						__lowerCAmelCase ,						__lowerCAmelCase						 =config_and_inputs
						__lowerCAmelCase						 ={"""pixel_values""": pixel_values}
						return config, inputs_dict






@require_torch
@require_timm
class     __a			(  SCREAMING_SNAKE_CASE						,     SCREAMING_SNAKE_CASE						,     SCREAMING_SNAKE_CASE						,     unittest.TestCase					):
	SCREAMING_SNAKE_CASE  	=							(TimmBackbone,) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE  	=							{"feature-extraction": TimmBackbone} if is_torch_available() else {}
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False
	SCREAMING_SNAKE_CASE  	=							False



	def 		UpperCamelCase				(    self				:     Union[str, Any])->				str:
						__lowerCAmelCase						 =TimmBackboneModelTester(self)
						__lowerCAmelCase						 =ConfigTester(self					,						config_class=snake_case_					,						has_text_modality=snake_case_)



	def 		UpperCamelCase				(    self				:     Tuple)->				Optional[Any]:
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()



	def 		UpperCamelCase				(    self				:     Any)->				Dict:
						__lowerCAmelCase						 ="""resnet18"""
						__lowerCAmelCase						 ="""microsoft/resnet-18"""

						__lowerCAmelCase						 =AutoBackbone.from_pretrained(snake_case_					,						use_timm_backbone=snake_case_)
						__lowerCAmelCase						 =AutoBackbone.from_pretrained(snake_case_)

						self.assertEqual(len(timm_model.out_features)					,						len(transformers_model.out_features))
						self.assertEqual(len(timm_model.stage_names)					,						len(transformers_model.stage_names))
						self.assertEqual(timm_model.channels					,						transformers_model.channels)
						# Out indices are set to the last layer by default. For timm models, we don't know
						# the number of layers in advance, so we set it to (-1,), whereas for transformers
						# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
						self.assertEqual(timm_model.out_indices					,						(-1,))
						self.assertEqual(transformers_model.out_indices					,						[len(timm_model.stage_names) - 1])

						__lowerCAmelCase						 =AutoBackbone.from_pretrained(snake_case_					,						use_timm_backbone=snake_case_					,						out_indices=[1, 2, 3])
						__lowerCAmelCase						 =AutoBackbone.from_pretrained(snake_case_					,						out_indices=[1, 2, 3])

						self.assertEqual(timm_model.out_indices					,						transformers_model.out_indices)
						self.assertEqual(len(timm_model.out_features)					,						len(transformers_model.out_features))
						self.assertEqual(timm_model.channels					,						transformers_model.channels)



	@unittest.skip("""TimmBackbone doesn't support feed forward chunking""")
	def 		UpperCamelCase				(    self				:     Dict)->				Any:
						pass



	@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute""")
	def 		UpperCamelCase				(    self				:     Tuple)->				Dict:
						pass



	@unittest.skip("""TimmBackbone initialization is managed on the timm side""")
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				List[str]:
						pass



	@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""")
	def 		UpperCamelCase				(    self				:     List[str])->				List[str]:
						pass



	@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""")
	def 		UpperCamelCase				(    self				:     int)->				int:
						pass



	@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint""")
	def 		UpperCamelCase				(    self				:     Dict)->				int:
						pass



	@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
	def 		UpperCamelCase				(    self				:     Any)->				Dict:
						pass



	@unittest.skip("""model weights aren't tied in TimmBackbone.""")
	def 		UpperCamelCase				(    self				:     int)->				Tuple:
						pass



	@unittest.skip("""model weights aren't tied in TimmBackbone.""")
	def 		UpperCamelCase				(    self				:     Optional[int])->				Union[str, Any]:
						pass



	@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
	def 		UpperCamelCase				(    self				:     Optional[Any])->				Optional[Any]:
						pass



	@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
	def 		UpperCamelCase				(    self				:     Any)->				List[Any]:
						pass



	@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration.""")
	def 		UpperCamelCase				(    self				:     Any)->				Tuple:
						pass



	@unittest.skip("""TimmBackbone doesn't support output_attentions.""")
	def 		UpperCamelCase				(    self				:     List[Any])->				str:
						pass



	@unittest.skip("""Safetensors is not supported by timm.""")
	def 		UpperCamelCase				(    self				:     Dict)->				Optional[int]:
						pass



	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
	def 		UpperCamelCase				(    self				:     Optional[int])->				Tuple:
						pass



	def 		UpperCamelCase				(    self				:     int)->				Union[str, Any]:
						__lowerCAmelCase ,						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__lowerCAmelCase						 =model_class(snake_case_)
											__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]					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Dict)->				Optional[Any]:
						__lowerCAmelCase ,						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs_for_common()
						__lowerCAmelCase						 =True
						__lowerCAmelCase						 =self.has_attentions

						# no need to test all models as different heads yield the same functionality
						__lowerCAmelCase						 =self.all_model_classes[0]
						__lowerCAmelCase						 =model_class(snake_case_)
						model.to(snake_case_)

						__lowerCAmelCase						 =self._prepare_for_class(snake_case_					,						snake_case_)
						__lowerCAmelCase						 =model(**snake_case_)
						__lowerCAmelCase						 =outputs[0][-1]

						# Encoder-/Decoder-only models
						__lowerCAmelCase						 =outputs.hidden_states[0]
						hidden_states.retain_grad()

						if self.has_attentions:
											__lowerCAmelCase						 =outputs.attentions[0]
											attentions.retain_grad()

						output.flatten()[0].backward(retain_graph=snake_case_)

						self.assertIsNotNone(hidden_states.grad)

						if self.has_attentions:
											self.assertIsNotNone(attentions.grad)



	def 		UpperCamelCase				(    self				:     Tuple)->				List[Any]:
						__lowerCAmelCase ,						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__lowerCAmelCase						 =model_class(snake_case_)
											model.to(snake_case_)
											model.eval()
											__lowerCAmelCase						 =model(**snake_case_)

											self.assertEqual(len(result.feature_maps)					,						len(config.out_indices))
											self.assertEqual(len(model.channels)					,						len(config.out_indices))

											# Check output of last stage is taken if out_features=None, out_indices=None
											__lowerCAmelCase						 =copy.deepcopy(snake_case_)
											__lowerCAmelCase						 =None
											__lowerCAmelCase						 =model_class(snake_case_)
											model.to(snake_case_)
											model.eval()
											__lowerCAmelCase						 =model(**snake_case_)

											self.assertEqual(len(result.feature_maps)					,						1)
											self.assertEqual(len(model.channels)					,						1)

											# Check backbone can be initialized with fresh weights
											__lowerCAmelCase						 =copy.deepcopy(snake_case_)
											__lowerCAmelCase						 =False
											__lowerCAmelCase						 =model_class(snake_case_)
											model.to(snake_case_)
											model.eval()
											__lowerCAmelCase						 =model(**snake_case_)




