

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
					import jax

					from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel







class  lowercase				(       unittest.TestCase			):




				def __init__(					self      ,   snake_case      ,   snake_case=13      ,   snake_case=30      ,   snake_case=2      ,   snake_case=3      ,   snake_case=True      ,   snake_case=True      ,   snake_case=32      ,   snake_case=5      ,   snake_case=4      ,   snake_case=37      ,   snake_case="gelu"      ,   snake_case=0.1      ,   snake_case=0.1      ,   snake_case=10      ,   snake_case=0.02      ,   ):
											snake_case_							=    parent
											snake_case_							=    batch_size
											snake_case_							=    image_size
											snake_case_							=    patch_size
											snake_case_							=    num_channels
											snake_case_							=    is_training
											snake_case_							=    use_labels
											snake_case_							=    hidden_size
											snake_case_							=    num_hidden_layers
											snake_case_							=    num_attention_heads
											snake_case_							=    intermediate_size
											snake_case_							=    hidden_act
											snake_case_							=    hidden_dropout_prob
											snake_case_							=    attention_probs_dropout_prob
											snake_case_							=    type_sequence_label_size
											snake_case_							=    initializer_range

											# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
											snake_case_							=    (image_size // patch_size) ** 2
											snake_case_							=    num_patches + 1




				def     a							(					self       ):
											snake_case_							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

											snake_case_							=    ViTConfig(
											    image_size=self.image_size      ,   patch_size=self.patch_size      ,   num_channels=self.num_channels      ,   hidden_size=self.hidden_size      ,   num_hidden_layers=self.num_hidden_layers      ,   num_attention_heads=self.num_attention_heads      ,   intermediate_size=self.intermediate_size      ,   hidden_act=self.hidden_act      ,   hidden_dropout_prob=self.hidden_dropout_prob      ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,   is_decoder=snake_case      ,   initializer_range=self.initializer_range      ,   )

											return config, pixel_values




				def     a							(					self      ,   snake_case      ,   snake_case       ):
											snake_case_							=    FlaxViTModel(config=snake_case       )
											snake_case_							=    model(snake_case       )
											# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
											snake_case_							=    (self.image_size, self.image_size)
											snake_case_							=    (self.patch_size, self.patch_size)
											snake_case_							=    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
											self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, num_patches + 1, self.hidden_size)       )




				def     a							(					self      ,   snake_case      ,   snake_case       ):
											snake_case_							=    self.type_sequence_label_size
											snake_case_							=    FlaxViTForImageClassification(config=snake_case       )
											snake_case_							=    model(snake_case       )
											self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size)       )

											# test greyscale images
											snake_case_							=    1
											snake_case_							=    FlaxViTForImageClassification(snake_case       )

											snake_case_							=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
											snake_case_							=    model(snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.prepare_config_and_inputs()
											(
											    (
											    snake_case_
											)				,    (
											    snake_case_
											)				,    
											)							=    config_and_inputs
											snake_case_							=    {'pixel_values': pixel_values}
											return config, inputs_dict







@require_flax
class  lowercase				(       lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()




				def     a							(					self       ):
											snake_case_							=    FlaxViTModelTester(self       )
											snake_case_							=    ConfigTester(self      ,   config_class=snake_case      ,   has_text_modality=snake_case      ,   hidden_size=37       )




				def     a							(					self       ):
											self.config_tester.run_common_tests()




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*snake_case       )




				def     a							(					self       ):
											snake_case_				,    snake_case_							=    self.model_tester.prepare_config_and_inputs_for_common()

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

																		snake_case_							=    ['pixel_values']
																		self.assertListEqual(arg_names[:1]      ,   snake_case       )




				def     a							(					self       ):
											snake_case_				,    snake_case_							=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__       ):
																									snake_case_							=    self._prepare_for_class(snake_case      ,   snake_case       )
																									snake_case_							=    model_class(snake_case       )

																									@jax.jit
																									def model_jitted(snake_case      ,   **snake_case       ):
																																return model(pixel_values=snake_case      ,   **snake_case       )

																									with self.subTest('JIT Enabled'       ):
																																snake_case_							=    model_jitted(**snake_case       ).to_tuple()

																									with self.subTest('JIT Disabled'       ):
																																with jax.disable_jit():
																																							snake_case_							=    model_jitted(**snake_case       ).to_tuple()

																									self.assertEqual(len(snake_case       )      ,   len(snake_case       )       )
																									for jitted_output, output in zip(snake_case      ,   snake_case       ):
																																self.assertEqual(jitted_output.shape      ,   output.shape       )




				@slow
				def     a							(					self       ):
											for model_class_name in self.all_model_classes:
																		snake_case_							=    model_class_name.from_pretrained('google/vit-base-patch16-224'       )
																		snake_case_							=    model(np.ones((1, 3, 224, 224)       )       )
																		self.assertIsNotNone(snake_case       )







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    {}
							snake_case_							=    tokenizer(example['content']   ,							truncation=UpperCamelCase__      )['input_ids']
							snake_case_							=    len(example['content']      ) / len(output['input_ids']      )
							return output


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

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

_UpperCAmelCase		:   Tuple										=       time.time()
_UpperCAmelCase		:   Union[str, 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''')

_UpperCAmelCase		:   Dict										=       time.time()
ds.push_to_hub(args.tokenized_data_repo)


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




