



"""simple docstring"""





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFResNetForImageClassification, TFResNetModel
					from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor



class  _lowerCAmelCase     :





		"""simple docstring"""






		def __init__(       self				,     _lowercase				,     _lowercase=3				,     _lowercase=3_2				,     _lowercase=3				,     _lowercase=1_0				,     _lowercase=[1_0, 2_0, 3_0, 4_0]				,     _lowercase=[1, 1, 2, 1]				,     _lowercase=True				,     _lowercase=True				,     _lowercase="relu"				,     _lowercase=3				,     _lowercase=None				,     )			->    Optional[int]:







								'''simple docstring'''

								snake_case_ :			Tuple            =      parent
								snake_case_ :			str            =      batch_size
								snake_case_ :			Union[str, Any]            =      image_size
								snake_case_ :			str            =      num_channels
								snake_case_ :			Union[str, Any]            =      embeddings_size
								snake_case_ :			Tuple            =      hidden_sizes
								snake_case_ :			int            =      depths
								snake_case_ :			int            =      is_training
								snake_case_ :			Dict            =      use_labels
								snake_case_ :			List[str]            =      hidden_act
								snake_case_ :			List[str]            =      num_labels
								snake_case_ :			Optional[Any]            =      scope
								snake_case_ :			Tuple            =      len(_lowercase )





		def  UpperCAmelCase__				(       self )			->    List[str]:







								'''simple docstring'''

								snake_case_ :			Union[str, Any]            =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

								snake_case_ :			Tuple            =      None
								if self.use_labels:
														snake_case_ :			Optional[Any]            =      ids_tensor([self.batch_size]				,     self.num_labels )

								snake_case_ :			Optional[Any]            =      self.get_config()

								return config, pixel_values, labels





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								return ResNetConfig(
								    num_channels=self.num_channels				,     embeddings_size=self.embeddings_size				,     hidden_sizes=self.hidden_sizes				,     depths=self.depths				,     hidden_act=self.hidden_act				,     num_labels=self.num_labels				,     image_size=self.image_size				,     )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase )			->    Union[str, Any]:







								'''simple docstring'''

								snake_case_ :			Any            =      TFResNetModel(config=_lowercase )
								snake_case_ :			Tuple            =      model(_lowercase )
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape				,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)				,     )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase )			->    Dict:







								'''simple docstring'''

								snake_case_ :			Union[str, Any]            =      self.num_labels
								snake_case_ :			Optional[Any]            =      TFResNetForImageClassification(_lowercase )
								snake_case_ :			Optional[int]            =      model(_lowercase				,     labels=_lowercase )
								self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels) )





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								snake_case_ :			str            =      self.prepare_config_and_inputs()
								snake_case_      ,		snake_case_      ,		snake_case_ :			Dict            =      config_and_inputs
								snake_case_ :			List[str]            =      {"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_tf
class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__	,					unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase      	=							(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
		_lowerCamelCase      	=							(
		    {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
		    if is_tf_available()
		    else {}
		)

		_lowerCamelCase      	=							False
		_lowerCamelCase      	=							False
		_lowerCamelCase      	=							False
		_lowerCamelCase      	=							False
		_lowerCamelCase      	=							False





		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			List[str]            =      TFResNetModelTester(self )
								snake_case_ :			List[Any]            =      ConfigTester(self				,     config_class=_lowercase				,     has_text_modality=_lowercase )





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								self.create_and_test_config_common_properties()
								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 )			->    Optional[int]:







								'''simple docstring'''

								return





		@unittest.skip(reason="""ResNet does not use inputs_embeds""" )
		def  UpperCAmelCase__				(       self )			->    Optional[Any]:







								'''simple docstring'''

								pass





		@unittest.skip(reason="""ResNet does not support input and output embeddings""" )
		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								pass





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								snake_case_      ,		snake_case_ :			List[Any]            =      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														snake_case_ :			Optional[Any]            =      model_class(_lowercase )
														snake_case_ :			List[Any]            =      inspect.signature(model.call )
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														snake_case_ :			Any            =      [*signature.parameters.keys()]

														snake_case_ :			str            =      ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]				,     _lowercase )





		def  UpperCAmelCase__				(       self )			->    Optional[int]:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase )





		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								def check_hidden_states_output(_lowercase				,     _lowercase				,     _lowercase ):
														snake_case_ :			str            =      model_class(_lowercase )
														snake_case_ :			Tuple            =      model(**self._prepare_for_class(_lowercase				,     _lowercase ) )

														snake_case_ :			Optional[Any]            =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														snake_case_ :			Any            =      self.model_tester.num_stages
														self.assertEqual(len(_lowercase )				,     expected_num_stages + 1 )

														# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
														self.assertListEqual(
														    list(hidden_states[0].shape[-2:] )				,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,     )

								snake_case_      ,		snake_case_ :			Union[str, Any]            =      self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_ :			Optional[Any]            =      ["""basic""", """bottleneck"""]
								for model_class in self.all_model_classes:
														for layer_type in layers_type:
																				snake_case_ :			List[str]            =      layer_type
																				snake_case_ :			Any            =      True
																				check_hidden_states_output(_lowercase				,     _lowercase				,     _lowercase )

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				snake_case_ :			Tuple            =      True

																				check_hidden_states_output(_lowercase				,     _lowercase				,     _lowercase )





		def  UpperCAmelCase__				(       self )			->    List[str]:







								'''simple docstring'''

								snake_case_ :			List[str]            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowercase )





		@slow
		def  UpperCAmelCase__				(       self )			->    List[str]:







								'''simple docstring'''

								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_ :			str            =      TFResNetModel.from_pretrained(_lowercase )
														self.assertIsNotNone(_lowercase )




def 						__lowerCAmelCase    (		):


						'''simple docstring'''






						snake_case_ :			str            =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
						return image






@require_tf
@require_vision
class  _lowerCAmelCase     (					unittest.TestCase		):





		"""simple docstring"""






		@cached_property
		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
								    if is_vision_available()
								    else None
								)





		@slow
		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

								snake_case_ :			str            =      self.default_image_processor
								snake_case_ :			Optional[Any]            =      prepare_img()
								snake_case_ :			Dict            =      image_processor(images=_lowercase				,     return_tensors="""tf""" )

								# forward pass
								snake_case_ :			Dict            =      model(**_lowercase )

								# verify the logits
								snake_case_ :			str            =      tf.TensorShape((1, 1_0_0_0) )
								self.assertEqual(outputs.logits.shape				,     _lowercase )

								snake_case_ :			Optional[Any]            =      tf.constant([-11.1069, -9.7877, -8.3777] )

								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()				,     _lowercase				,     atol=1E-4 ) )








import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   __lowerCamelCase   (				__a  :Optional[int]       )						->							str:






		"""simple docstring"""

		A__     =  {}
		A__     =  tokenizer(example["""content"""]       ,	truncation=__a       )["""input_ids"""]
		A__     =  len(example["""content"""]       ) / len(output["""input_ids"""]       )
		return output


A       :   Union[str, Any]					= HfArgumentParser(PretokenizationArguments)
A       :   str					= parser.parse_args()
if args.num_workers is None:
					A       :   Any					= multiprocessing.cpu_count()
A       :   int					= AutoTokenizer.from_pretrained(args.tokenizer_dir)

A       :   Optional[int]					= time.time()
A       :   int					= load_dataset(args.dataset_name, split='''train''')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

A       :   Optional[Any]					= time.time()
A       :   int					= 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       :   Tuple					= time.time()
ds.push_to_hub(args.tokenized_data_repo)






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



