

'''simple docstring'''
from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
					import tensorflow as tf

					from transformers import TFBlipTextModel
					from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class 					_snake_case		:






			def __init__(  self			,_snake_case			,_snake_case=12			,_snake_case=7			,_snake_case=True			,_snake_case=True			,_snake_case=True			,_snake_case=99			,_snake_case=32			,_snake_case=32			,_snake_case=2			,_snake_case=4			,_snake_case=37			,_snake_case=0.1			,_snake_case=0.1			,_snake_case=5_12			,_snake_case=0.02			,_snake_case=0			,_snake_case=None			,):
				UpperCAmelCase_     :      int						=  parent
				UpperCAmelCase_     :      Optional[Any]						=  batch_size
				UpperCAmelCase_     :      Tuple						=  seq_length
				UpperCAmelCase_     :      int						=  is_training
				UpperCAmelCase_     :      Dict						=  use_input_mask
				UpperCAmelCase_     :      List[str]						=  use_labels
				UpperCAmelCase_     :      int						=  vocab_size
				UpperCAmelCase_     :      int						=  hidden_size
				UpperCAmelCase_     :      Any						=  projection_dim
				UpperCAmelCase_     :      Dict						=  num_hidden_layers
				UpperCAmelCase_     :      int						=  num_attention_heads
				UpperCAmelCase_     :      Optional[Any]						=  intermediate_size
				UpperCAmelCase_     :      Optional[Any]						=  dropout
				UpperCAmelCase_     :      Any						=  attention_dropout
				UpperCAmelCase_     :      List[str]						=  max_position_embeddings
				UpperCAmelCase_     :      Dict						=  initializer_range
				UpperCAmelCase_     :      Tuple						=  scope
				UpperCAmelCase_     :      List[Any]						=  bos_token_id






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[str]						=  ids_tensor([self.batch_size, self.seq_length]			,self.vocab_size     )

				UpperCAmelCase_     :      Optional[Any]						=  None
				if self.use_input_mask:
					UpperCAmelCase_     :      str						=  random_attention_mask([self.batch_size, self.seq_length]     )

				if input_mask is not None:
					UpperCAmelCase_     :      Tuple						=  input_mask.numpy()
					UpperCAmelCase_					,					UpperCAmelCase_     :      str						=  input_mask.shape
					UpperCAmelCase_     :      Union[str, Any]						=  np.random.randint(1			,seq_length - 1			,size=(batch_size,)     )
					for batch_idx, start_index in enumerate(_snake_case     ):
						UpperCAmelCase_     :      Union[str, Any]						=  1
						UpperCAmelCase_     :      Optional[Any]						=  0

				UpperCAmelCase_     :      int						=  self.get_config()

				return config, input_ids, tf.convert_to_tensor(_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				return BlipTextConfig(
				    vocab_size=self.vocab_size			,hidden_size=self.hidden_size			,projection_dim=self.projection_dim			,num_hidden_layers=self.num_hidden_layers			,num_attention_heads=self.num_attention_heads			,intermediate_size=self.intermediate_size			,dropout=self.dropout			,attention_dropout=self.attention_dropout			,max_position_embeddings=self.max_position_embeddings			,initializer_range=self.initializer_range			,bos_token_id=self.bos_token_id			,)






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      Tuple						=  TFBlipTextModel(config=_snake_case     )
				UpperCAmelCase_     :      Tuple						=  model(_snake_case			,attention_mask=_snake_case			,training=_snake_case     )
				UpperCAmelCase_     :      int						=  model(_snake_case			,training=_snake_case     )
				self.parent.assertEqual(result.last_hidden_state.shape			,(self.batch_size, self.seq_length, self.hidden_size)     )
				self.parent.assertEqual(result.pooler_output.shape			,(self.batch_size, self.hidden_size)     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[Any]						=  self.prepare_config_and_inputs()
				UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      Tuple						=  config_and_inputs
				UpperCAmelCase_     :      List[str]						=  {"input_ids": input_ids, "attention_mask": input_mask}
				return config, inputs_dict




@require_tf
class 					_snake_case		(__SCREAMING_SNAKE_CASE			,			unittest.TestCase):
			__A  :	Optional[Any]        =(TFBlipTextModel,) if is_tf_available() else ()
			__A  :	Dict        =False
			__A  :	Optional[Any]        =False
			__A  :	Union[str, Any]        =False






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      str						=  BlipTextModelTester(self     )
				UpperCAmelCase_     :      Dict						=  ConfigTester(self			,config_class=_snake_case			,hidden_size=37     )






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






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Union[str, Any]						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				pass






			def 		UpperCamelCase__							(  self     ):
				pass






			@unittest.skip(reason="Blip does not use inputs_embeds"     )
			def 		UpperCamelCase__							(  self     ):
				pass






			@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"     )
			def 		UpperCamelCase__							(  self     ):
				pass






			@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"     )
			def 		UpperCamelCase__							(  self     ):
				pass






			@slow
			def 		UpperCamelCase__							(  self     ):
				for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCAmelCase_     :      Any						=  TFBlipTextModel.from_pretrained(_snake_case     )
					self.assertIsNotNone(_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case=True     ):
				super().test_pt_tf_model_equivalence(allow_missing_keys=_snake_case     )





'''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
)
UpperCamelCase__  :						Dict					=      logging.getLogger(__name__)







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[Any] 					=	argparse.ArgumentParser(
       description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''     )
   parser.add_argument('''--file_path'''	,      type=_A	,      default='''data/dump.txt'''	,      help='''The path to the data.'''     )
   parser.add_argument('''--tokenizer_type'''	,      type=_A	,      default='''bert'''	,      choices=['''bert''', '''roberta''', '''gpt2''']     )
   parser.add_argument('''--tokenizer_name'''	,      type=_A	,      default='''bert-base-uncased'''	,      help='''The tokenizer to use.'''     )
   parser.add_argument('''--dump_file'''	,      type=_A	,      default='''data/dump'''	,      help='''The dump file prefix.'''     )
   UpperCAmelCase__				:					Optional[int] 					=	parser.parse_args()

   logger.info(F'''Loading Tokenizer ({args.tokenizer_name})'''     )
   if args.tokenizer_type == "bert":
      UpperCAmelCase__				:					List[str] 					=	BertTokenizer.from_pretrained(args.tokenizer_name     )
      UpperCAmelCase__				:					Optional[Any] 					=	tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
      UpperCAmelCase__				:					Union[str, Any] 					=	tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
   elif args.tokenizer_type == "roberta":
      UpperCAmelCase__				:					Union[str, Any] 					=	RobertaTokenizer.from_pretrained(args.tokenizer_name     )
      UpperCAmelCase__				:					List[Any] 					=	tokenizer.special_tokens_map['''cls_token''']  # `<s>`
      UpperCAmelCase__				:					Dict 					=	tokenizer.special_tokens_map['''sep_token''']  # `</s>`
   elif args.tokenizer_type == "gpt2":
      UpperCAmelCase__				:					List[str] 					=	GPTaTokenizer.from_pretrained(args.tokenizer_name     )
      UpperCAmelCase__				:					List[Any] 					=	tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
      UpperCAmelCase__				:					Optional[Any] 					=	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:
      UpperCAmelCase__				:					List[Any] 					=	fp.readlines()

   logger.info('''Start encoding'''     )
   logger.info(F'''{len(_A     )} examples to process.'''     )

   UpperCAmelCase__				:					List[str] 					=	[]
   UpperCAmelCase__				:					Union[str, Any] 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	1_00_00
   UpperCAmelCase__				:					Tuple 					=	time.time()
   for text in data:
      UpperCAmelCase__				:					Any 					=	F'''{bos} {text.strip()} {sep}'''
      UpperCAmelCase__				:					int 					=	tokenizer.encode(_A	,      add_special_tokens=_A     )
      rslt.append(_A     )

      iter += 1
      if iter % interval == 0:
         UpperCAmelCase__				:					int 					=	time.time()
         logger.info(F'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''     )
         UpperCAmelCase__				:					Optional[Any] 					=	time.time()
   logger.info('''Finished binarization'''     )
   logger.info(F'''{len(_A     )} examples processed.'''     )

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


if __name__ == "__main__":
  main()




