





'''simple docstring'''






from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
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 TFViTForImageClassification, TFViTModel


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor




class 					UpperCAmelCase	:




      def __init__(     self     :					Optional[int]     ,     __snake_case     :					Tuple     ,     __snake_case     :					Union[str, Any]=13     ,     __snake_case     :					Dict=30     ,     __snake_case     :					int=2     ,     __snake_case     :					List[Any]=3     ,     __snake_case     :					int=True     ,     __snake_case     :					str=True     ,     __snake_case     :					List[str]=32     ,     __snake_case     :					List[str]=2     ,     __snake_case     :					Optional[int]=4     ,     __snake_case     :					Tuple=37     ,     __snake_case     :					str="gelu"     ,     __snake_case     :					Tuple=0.1     ,     __snake_case     :					List[str]=0.1     ,     __snake_case     :					int=10     ,     __snake_case     :					Any=0.02     ,     __snake_case     :					Optional[int]=3     ,     __snake_case     :					Optional[Any]=None     ,     )							-> int:
            _lowerCAmelCase					=				parent
            _lowerCAmelCase					=				batch_size
            _lowerCAmelCase					=				image_size
            _lowerCAmelCase					=				patch_size
            _lowerCAmelCase					=				num_channels
            _lowerCAmelCase					=				is_training
            _lowerCAmelCase					=				use_labels
            _lowerCAmelCase					=				hidden_size
            _lowerCAmelCase					=				num_hidden_layers
            _lowerCAmelCase					=				num_attention_heads
            _lowerCAmelCase					=				intermediate_size
            _lowerCAmelCase					=				hidden_act
            _lowerCAmelCase					=				hidden_dropout_prob
            _lowerCAmelCase					=				attention_probs_dropout_prob
            _lowerCAmelCase					=				type_sequence_label_size
            _lowerCAmelCase					=				initializer_range
            _lowerCAmelCase					=				scope

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




      def 							lowercase__       (     self     :					List[Any]    )							-> Optional[int]:
            _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.type_sequence_label_size    )

            _lowerCAmelCase					=				self.get_config()

            return config, pixel_values, labels




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Any:
            return 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     ,     )




      def 							lowercase__       (     self     :					str     ,     __snake_case     :					Tuple     ,     __snake_case     :					str     ,     __snake_case     :					str    )							-> List[Any]:
            _lowerCAmelCase					=				TFViTModel(config=__snake_case    )
            _lowerCAmelCase					=				model(__snake_case     ,     training=__snake_case    )
            self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, self.seq_length, self.hidden_size)    )

            # Test with an image with different size than the one specified in config.
            _lowerCAmelCase					=				self.image_size // 2
            _lowerCAmelCase					=				pixel_values[:, :, :image_size, :image_size]
            _lowerCAmelCase					=				model(__snake_case     ,     interpolate_pos_encoding=__snake_case     ,     training=__snake_case    )
            _lowerCAmelCase					=				(image_size // self.patch_size) ** 2 + 1
            self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, seq_length, self.hidden_size)    )




      def 							lowercase__       (     self     :					List[str]     ,     __snake_case     :					Union[str, Any]     ,     __snake_case     :					Optional[int]     ,     __snake_case     :					int    )							-> str:
            _lowerCAmelCase					=				self.type_sequence_label_size
            _lowerCAmelCase					=				TFViTForImageClassification(__snake_case    )
            _lowerCAmelCase					=				model(__snake_case     ,     labels=__snake_case     ,     training=__snake_case    )
            self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.type_sequence_label_size)    )

            # Test with an image with different size than the one specified in config.
            _lowerCAmelCase					=				self.image_size // 2
            _lowerCAmelCase					=				pixel_values[:, :, :image_size, :image_size]
            _lowerCAmelCase					=				model(__snake_case     ,     interpolate_pos_encoding=__snake_case     ,     training=__snake_case    )
            self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.type_sequence_label_size)    )

            # test greyscale images
            _lowerCAmelCase					=				1
            _lowerCAmelCase					=				TFViTForImageClassification(__snake_case    )
            _lowerCAmelCase					=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
            _lowerCAmelCase					=				model(__snake_case    )
            self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.type_sequence_label_size)    )




      def 							lowercase__       (     self     :					List[Any]    )							-> Any:
            _lowerCAmelCase					=				self.prepare_config_and_inputs()
            _lowerCAmelCase	,				_lowerCAmelCase	,				_lowerCAmelCase					=				config_and_inputs
            _lowerCAmelCase					=				{"""pixel_values""": pixel_values}
            return config, inputs_dict




@require_tf
class 					UpperCAmelCase	(     snake_case_				,						snake_case_				,						unittest.TestCase  ):
      _lowercase:     Dict			=  (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
      _lowercase:     str			=  (
          {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
          if is_tf_available()
          else {}
      )

      _lowercase:     Optional[Any]			=  False
      _lowercase:     List[Any]			=  False
      _lowercase:     Any			=  False




      def 							lowercase__       (     self     :					str    )							-> str:
            _lowerCAmelCase					=				TFViTModelTester(self    )
            _lowerCAmelCase					=				ConfigTester(self     ,     config_class=__snake_case     ,     has_text_modality=__snake_case     ,     hidden_size=37    )




      def 							lowercase__       (     self     :					str    )							-> Optional[Any]:
            self.config_tester.run_common_tests()




      @unittest.skip(reason="""ViT does not use inputs_embeds"""    )
      def 							lowercase__       (     self     :					List[str]    )							-> Dict:
            pass




      @unittest.skip(reason="""ViT does not use inputs_embeds"""    )
      def 							lowercase__       (     self     :					Any    )							-> Any:
            pass




      def 							lowercase__       (     self     :					Any    )							-> List[str]:
            _lowerCAmelCase	,				_lowerCAmelCase					=				self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  _lowerCAmelCase					=				model_class(__snake_case    )
                  self.assertIsInstance(model.get_input_embeddings()     ,     (tf.keras.layers.Layer)    )
                  _lowerCAmelCase					=				model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(__snake_case     ,     tf.keras.layers.Layer    )    )




      def 							lowercase__       (     self     :					str    )							-> List[str]:
            _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.call    )
                  # 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 							lowercase__       (     self     :					Optional[int]    )							-> int:
            _lowerCAmelCase					=				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__snake_case    )




      def 							lowercase__       (     self     :					Optional[int]    )							-> List[str]:
            _lowerCAmelCase					=				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__snake_case    )




      @slow
      def 							lowercase__       (     self     :					Union[str, Any]    )							-> int:
            _lowerCAmelCase					=				TFViTModel.from_pretrained("""google/vit-base-patch16-224"""    )
            self.assertIsNotNone(__snake_case    )




def   UpperCamelCase__ (						):





      """simple docstring"""


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





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




      @cached_property
      def 							lowercase__       (     self     :					Union[str, Any]    )							-> List[str]:
            return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""    ) if is_vision_available() else None




      @slow
      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Optional[int]:
            _lowerCAmelCase					=				TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""    )

            _lowerCAmelCase					=				self.default_image_processor
            _lowerCAmelCase					=				prepare_img()
            _lowerCAmelCase					=				image_processor(images=__snake_case     ,     return_tensors="""tf"""    )

            # forward pass
            _lowerCAmelCase					=				model(**__snake_case    )

            # verify the logits
            _lowerCAmelCase					=				tf.TensorShape((1, 10_00)    )
            self.assertEqual(outputs.logits.shape     ,     __snake_case    )

            _lowerCAmelCase					=				tf.constant([-0.27_44, 0.82_15, -0.08_36]    )

            tf.debugging.assert_near(outputs.logits[0, :3]     ,     __snake_case     ,     atol=1E-4    )













'''simple docstring'''






import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
A__							:  List[str]    =logging.getLogger(__name__)
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				argparse.ArgumentParser(
          description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."""    )
      parser.add_argument("""--file_path"""				,      type=lowerCAmelCase				,      default="""data/dump.txt"""				,      help="""The path to the data."""    )
      parser.add_argument("""--tokenizer_type"""				,      type=lowerCAmelCase				,      default="""bert"""				,      choices=["""bert""", """roberta""", """gpt2"""]    )
      parser.add_argument("""--tokenizer_name"""				,      type=lowerCAmelCase				,      default="""bert-base-uncased"""				,      help="""The tokenizer to use."""    )
      parser.add_argument("""--dump_file"""				,      type=lowerCAmelCase				,      default="""data/dump"""				,      help="""The dump file prefix."""    )
      _lowerCAmelCase					=				parser.parse_args()

      logger.info(f"Loading Tokenizer ({args.tokenizer_name})"    )
      if args.tokenizer_type == "bert":
            _lowerCAmelCase					=				BertTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            _lowerCAmelCase					=				RobertaTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
      elif args.tokenizer_type == "gpt2":
            _lowerCAmelCase					=				GPTaTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""eos_token"""]  # `<|endoftext|>`

      logger.info(f"Loading text from {args.file_path}"    )
      with open(args.file_path				,      """r"""				,      encoding="""utf8"""    ) as fp:
            _lowerCAmelCase					=				fp.readlines()

      logger.info("""Start encoding"""    )
      logger.info(f"{len(lowerCAmelCase    )} examples to process."    )

      _lowerCAmelCase					=				[]
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				1_00_00
      _lowerCAmelCase					=				time.time()
      for text in data:
            _lowerCAmelCase					=				f"{bos} {text.strip()} {sep}"
            _lowerCAmelCase					=				tokenizer.encode(lowerCAmelCase				,      add_special_tokens=lowerCAmelCase    )
            rslt.append(lowerCAmelCase    )

            iter += 1
            if iter % interval == 0:
                  _lowerCAmelCase					=				time.time()
                  logger.info(f"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"    )
                  _lowerCAmelCase					=				time.time()
      logger.info("""Finished binarization"""    )
      logger.info(f"{len(lowerCAmelCase    )} examples processed."    )

      _lowerCAmelCase					=				f"{args.dump_file}.{args.tokenizer_name}.pickle"
      _lowerCAmelCase					=				tokenizer.vocab_size
      if vocab_size < (1 << 16):
            _lowerCAmelCase					=				[np.uintaa(lowerCAmelCase    ) for d in rslt]
      else:
            _lowerCAmelCase					=				[np.intaa(lowerCAmelCase    ) for d in rslt]
      random.shuffle(rslt_    )
      logger.info(f"Dump to {dp_file}"    )
      with open(lowerCAmelCase				,      """wb"""    ) as handle:
            pickle.dump(rslt_				,      lowerCAmelCase				,      protocol=pickle.HIGHEST_PROTOCOL    )


if __name__ == "__main__":
      main()






