


import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


__snake_case            =   {
    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
    "gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Tuple	):





			"""simple docstring"""




			assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
			assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
			if args.mlm:
						assert os.path.isfile(args.token_counts	)
						assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
			else:
						assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

			assert args.teacher_type == args.student_type or (
			    args.student_type == "distilbert" and args.teacher_type == "bert"
			)
			assert os.path.isfile(args.student_config	)
			if args.student_pretrained_weights is not None:
						assert os.path.isfile(args.student_pretrained_weights	)

			if args.freeze_token_type_embds:
						assert args.student_type in ["roberta"]

			assert args.alpha_ce >= 0.0
			assert args.alpha_mlm >= 0.0
			assert args.alpha_clm >= 0.0
			assert args.alpha_mse >= 0.0
			assert args.alpha_cos >= 0.0
			assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




			if args.student_type == "roberta":
						UpperCamelCase         =					False
			elif args.student_type == "gpt2":
						UpperCamelCase         =					False






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		List[str]	):





			"""simple docstring"""




			if args.student_type == "roberta":
						UpperCamelCase         =					False






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					argparse.ArgumentParser(description="""Training"""	)
			parser.add_argument("""--force"""       ,  action="""store_true"""       ,  help="""Overwrite dump_path if it already exists."""	)

			parser.add_argument(
			    """--dump_path"""       ,  type=SCREAMING_SNAKE_CASE_       ,  required=SCREAMING_SNAKE_CASE_       ,  help="""The output directory (log, checkpoints, parameters, etc.)"""	)
			parser.add_argument(
			    """--data_file"""       ,  type=SCREAMING_SNAKE_CASE_       ,  required=SCREAMING_SNAKE_CASE_       ,  help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""       ,  )

			parser.add_argument(
			    """--student_type"""       ,  type=SCREAMING_SNAKE_CASE_       ,  choices=["""distilbert""", """roberta""", """gpt2"""]       ,  required=SCREAMING_SNAKE_CASE_       ,  help="""The student type (DistilBERT, RoBERTa)."""       ,  )
			parser.add_argument("""--student_config"""       ,  type=SCREAMING_SNAKE_CASE_       ,  required=SCREAMING_SNAKE_CASE_       ,  help="""Path to the student configuration."""	)
			parser.add_argument(
			    """--student_pretrained_weights"""       ,  default=SCREAMING_SNAKE_CASE_       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Load student initialization checkpoint."""	)

			parser.add_argument(
			    """--teacher_type"""       ,  choices=["""bert""", """roberta""", """gpt2"""]       ,  required=SCREAMING_SNAKE_CASE_       ,  help="""Teacher type (BERT, RoBERTa)."""	)
			parser.add_argument("""--teacher_name"""       ,  type=SCREAMING_SNAKE_CASE_       ,  required=SCREAMING_SNAKE_CASE_       ,  help="""The teacher model."""	)

			parser.add_argument("""--temperature"""       ,  default=2.0       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Temperature for the softmax temperature."""	)
			parser.add_argument(
			    """--alpha_ce"""       ,  default=0.5       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Linear weight for the distillation loss. Must be >=0."""	)
			parser.add_argument(
			    """--alpha_mlm"""       ,  default=0.0       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."""       ,  )
			parser.add_argument("""--alpha_clm"""       ,  default=0.5       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Linear weight for the CLM loss. Must be >=0."""	)
			parser.add_argument("""--alpha_mse"""       ,  default=0.0       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Linear weight of the MSE loss. Must be >=0."""	)
			parser.add_argument(
			    """--alpha_cos"""       ,  default=0.0       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Linear weight of the cosine embedding loss. Must be >=0."""	)

			parser.add_argument(
			    """--mlm"""       ,  action="""store_true"""       ,  help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""	)
			parser.add_argument(
			    """--mlm_mask_prop"""       ,  default=0.15       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Proportion of tokens for which we need to make a prediction."""       ,  )
			parser.add_argument("""--word_mask"""       ,  default=0.8       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Proportion of tokens to mask out."""	)
			parser.add_argument("""--word_keep"""       ,  default=0.1       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Proportion of tokens to keep."""	)
			parser.add_argument("""--word_rand"""       ,  default=0.1       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Proportion of tokens to randomly replace."""	)
			parser.add_argument(
			    """--mlm_smoothing"""       ,  default=0.7       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."""       ,  )
			parser.add_argument("""--token_counts"""       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""The token counts in the data_file for MLM."""	)

			parser.add_argument(
			    """--restrict_ce_to_mask"""       ,  action="""store_true"""       ,  help="""If true, compute the distillation loss only the [MLM] prediction distribution."""       ,  )
			parser.add_argument(
			    """--freeze_pos_embs"""       ,  action="""store_true"""       ,  help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."""       ,  )
			parser.add_argument(
			    """--freeze_token_type_embds"""       ,  action="""store_true"""       ,  help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."""       ,  )

			parser.add_argument("""--n_epoch"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=3       ,  help="""Number of pass on the whole dataset."""	)
			parser.add_argument("""--batch_size"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=5       ,  help="""Batch size (for each process)."""	)
			parser.add_argument(
			    """--group_by_size"""       ,  action="""store_false"""       ,  help="""If true, group sequences that have similar length into the same batch. Default is true."""       ,  )

			parser.add_argument(
			    """--gradient_accumulation_steps"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=50       ,  help="""Gradient accumulation for larger training batches."""       ,  )
			parser.add_argument("""--warmup_prop"""       ,  default=0.05       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Linear warmup proportion."""	)
			parser.add_argument("""--weight_decay"""       ,  default=0.0       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Weight decay if we apply some."""	)
			parser.add_argument("""--learning_rate"""       ,  default=5e-4       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""The initial learning rate for Adam."""	)
			parser.add_argument("""--adam_epsilon"""       ,  default=1e-6       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Epsilon for Adam optimizer."""	)
			parser.add_argument("""--max_grad_norm"""       ,  default=5.0       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Max gradient norm."""	)
			parser.add_argument("""--initializer_range"""       ,  default=0.02       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Random initialization range."""	)

			parser.add_argument(
			    """--fp16"""       ,  action="""store_true"""       ,  help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""       ,  )
			parser.add_argument(
			    """--fp16_opt_level"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default="""O1"""       ,  help=(
			        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
			        """See details at https://nvidia.github.io/apex/amp.html"""
			    )       ,  )
			parser.add_argument("""--n_gpu"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=1       ,  help="""Number of GPUs in the node."""	)
			parser.add_argument("""--local_rank"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=-1       ,  help="""Distributed training - Local rank"""	)
			parser.add_argument("""--seed"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=56       ,  help="""Random seed"""	)

			parser.add_argument("""--log_interval"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=500       ,  help="""Tensorboard logging interval."""	)
			parser.add_argument("""--checkpoint_interval"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=4_000       ,  help="""Checkpoint interval."""	)
			UpperCamelCase         =					parser.parse_args()
			sanity_checks(SCREAMING_SNAKE_CASE_	)

			# ARGS #
			init_gpu_params(SCREAMING_SNAKE_CASE_	)
			set_seed(SCREAMING_SNAKE_CASE_	)
			if args.is_master:
						if os.path.exists(args.dump_path	):
									if not args.force:
												raise ValueError(
												    f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
												    """ itUse `--force` if you want to overwrite it"""	)
									else:
												shutil.rmtree(args.dump_path	)

						if not os.path.exists(args.dump_path	):
									os.makedirs(args.dump_path	)
						logger.info(f'Experiment will be dumped and logged in {args.dump_path}'	)

						# SAVE PARAMS #
						logger.info(f'Param: {args}'	)
						with open(os.path.join(args.dump_path       ,  """parameters.json"""	)       ,  """w"""	) as f:
									json.dump(vars(SCREAMING_SNAKE_CASE_	)       ,  SCREAMING_SNAKE_CASE_       ,  indent=4	)
						git_log(args.dump_path	)

			UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					MODEL_CLASSES[args.student_type]
			UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					MODEL_CLASSES[args.teacher_type]

			# TOKENIZER #
			UpperCamelCase         =					teacher_tokenizer_class.from_pretrained(args.teacher_name	)
			UpperCamelCase         =					{}
			for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
						UpperCamelCase         =					tokenizer.all_special_tokens.index(SCREAMING_SNAKE_CASE_	)
						UpperCamelCase         =					tokenizer.all_special_ids[idx]
			logger.info(f'Special tokens {special_tok_ids}'	)
			UpperCamelCase         =					special_tok_ids
			UpperCamelCase         =					tokenizer.max_model_input_sizes[args.teacher_name]

			# DATA LOADER #
			logger.info(f'Loading data from {args.data_file}'	)
			with open(args.data_file       ,  """rb"""	) as fp:
						UpperCamelCase         =					pickle.load(SCREAMING_SNAKE_CASE_	)

			if args.mlm:
						logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)'	)
						with open(args.token_counts       ,  """rb"""	) as fp:
									UpperCamelCase         =					pickle.load(SCREAMING_SNAKE_CASE_	)

						UpperCamelCase         =					np.maximum(SCREAMING_SNAKE_CASE_       ,  1	) ** -args.mlm_smoothing
						for idx in special_tok_ids.values():
									UpperCamelCase         =					0.0  # do not predict special tokens
						UpperCamelCase         =					torch.from_numpy(SCREAMING_SNAKE_CASE_	)
			else:
						UpperCamelCase         =					None

			UpperCamelCase         =					LmSeqsDataset(params=SCREAMING_SNAKE_CASE_       ,  data=SCREAMING_SNAKE_CASE_	)
			logger.info("""Data loader created."""	)

			# STUDENT #
			logger.info(f'Loading student config from {args.student_config}'	)
			UpperCamelCase         =					student_config_class.from_pretrained(args.student_config	)
			UpperCamelCase         =					True

			if args.student_pretrained_weights is not None:
						logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}'	)
						UpperCamelCase         =					student_model_class.from_pretrained(args.student_pretrained_weights       ,  config=SCREAMING_SNAKE_CASE_	)
			else:
						UpperCamelCase         =					student_model_class(SCREAMING_SNAKE_CASE_	)

			if args.n_gpu > 0:
						student.to(f'cuda:{args.local_rank}'	)
			logger.info("""Student loaded."""	)

			# TEACHER #
			UpperCamelCase         =					teacher_model_class.from_pretrained(args.teacher_name       ,  output_hidden_states=SCREAMING_SNAKE_CASE_	)
			if args.n_gpu > 0:
						teacher.to(f'cuda:{args.local_rank}'	)
			logger.info(f'Teacher loaded from {args.teacher_name}.'	)

			# FREEZING #
			if args.freeze_pos_embs:
						freeze_pos_embeddings(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)
			if args.freeze_token_type_embds:
						freeze_token_type_embeddings(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			# SANITY CHECKS #
			assert student.config.vocab_size == teacher.config.vocab_size
			assert student.config.hidden_size == teacher.config.hidden_size
			assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
			if args.mlm:
						assert token_probs.size(0	) == stu_architecture_config.vocab_size

			# DISTILLER #
			torch.cuda.empty_cache()
			UpperCamelCase         =					Distiller(
			    params=SCREAMING_SNAKE_CASE_       ,  dataset=SCREAMING_SNAKE_CASE_       ,  token_probs=SCREAMING_SNAKE_CASE_       ,  student=SCREAMING_SNAKE_CASE_       ,  teacher=SCREAMING_SNAKE_CASE_	)
			distiller.train()
			logger.info("""Let's go get some drinks."""	)


if __name__ == "__main__":
			main()








import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import OwlViTImageProcessor, OwlViTProcessor




@require_vision
class    UpperCAmelCase					(       unittest.TestCase    ):




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					tempfile.mkdtemp()

						# fmt: off
						UpperCamelCase         =					["""""", """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
						# fmt: on
						UpperCamelCase         =					dict(zip(__magic_name__       ,       range(len(__magic_name__						)						)						)						)
						UpperCamelCase         =					["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
						UpperCamelCase         =					{"""unk_token""": """<unk>"""}

						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""vocab_file"""]						)
						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""merges_file"""]						)
						with open(self.vocab_file       ,       """w"""       ,       encoding="""utf-8"""						) as fp:
									fp.write(json.dumps(__magic_name__						) + """\n"""						)
						with open(self.merges_file       ,       """w"""       ,       encoding="""utf-8"""						) as fp:
									fp.write("""\n""".join(__magic_name__						)						)

						UpperCamelCase         =					{
						    """do_resize""": True,
						    """size""": 2_0,
						    """do_center_crop""": True,
						    """crop_size""": 1_8,
						    """do_normalize""": True,
						    """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
						    """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
						}
						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       __magic_name__						)
						with open(self.image_processor_file       ,       """w"""       ,       encoding="""utf-8"""						) as fp:
									json.dump(__magic_name__       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		str       ,       **__magic_name__							:		Tuple						):







						"""simple docstring"""







						return CLIPTokenizer.from_pretrained(self.tmpdirname       ,       pad_token="""!"""       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		str						):







						"""simple docstring"""







						return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,       pad_token="""!"""       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		Optional[Any]						):







						"""simple docstring"""







						return OwlViTImageProcessor.from_pretrained(self.tmpdirname       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						shutil.rmtree(self.tmpdirname						)




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					[np.random.randint(2_5_5       ,       size=(3, 3_0, 4_0_0)       ,       dtype=np.uinta						)]

						UpperCamelCase         =					[Image.fromarray(np.moveaxis(__magic_name__       ,       0       ,       -1						)						) for x in image_inputs]

						return image_inputs




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_tokenizer()
						UpperCamelCase         =					self.get_rust_tokenizer()
						UpperCamelCase         =					self.get_image_processor()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)
						processor_slow.save_pretrained(self.tmpdirname						)
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(self.tmpdirname       ,       use_fast=__magic_name__						)

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)
						processor_fast.save_pretrained(self.tmpdirname						)
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(self.tmpdirname						)

						self.assertEqual(processor_slow.tokenizer.get_vocab()       ,       tokenizer_slow.get_vocab()						)
						self.assertEqual(processor_fast.tokenizer.get_vocab()       ,       tokenizer_fast.get_vocab()						)
						self.assertEqual(tokenizer_slow.get_vocab()       ,       tokenizer_fast.get_vocab()						)
						self.assertIsInstance(processor_slow.tokenizer       ,       __magic_name__						)
						self.assertIsInstance(processor_fast.tokenizer       ,       __magic_name__						)

						self.assertEqual(processor_slow.image_processor.to_json_string()       ,       image_processor.to_json_string()						)
						self.assertEqual(processor_fast.image_processor.to_json_string()       ,       image_processor.to_json_string()						)
						self.assertIsInstance(processor_slow.image_processor       ,       __magic_name__						)
						self.assertIsInstance(processor_fast.image_processor       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					OwlViTProcessor(tokenizer=self.get_tokenizer()       ,       image_processor=self.get_image_processor()						)
						processor.save_pretrained(self.tmpdirname						)

						UpperCamelCase         =					self.get_tokenizer(bos_token="""(BOS)"""       ,       eos_token="""(EOS)"""						)
						UpperCamelCase         =					self.get_image_processor(do_normalize=__magic_name__						)

						UpperCamelCase         =					OwlViTProcessor.from_pretrained(
						    self.tmpdirname       ,       bos_token="""(BOS)"""       ,       eos_token="""(EOS)"""       ,       do_normalize=__magic_name__						)

						self.assertEqual(processor.tokenizer.get_vocab()       ,       tokenizer_add_kwargs.get_vocab()						)
						self.assertIsInstance(processor.tokenizer       ,       __magic_name__						)

						self.assertEqual(processor.image_processor.to_json_string()       ,       image_processor_add_kwargs.to_json_string()						)
						self.assertIsInstance(processor.image_processor       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					self.prepare_image_inputs()

						UpperCamelCase         =					image_processor(__magic_name__       ,       return_tensors="""np"""						)
						UpperCamelCase         =					processor(images=__magic_name__       ,       return_tensors="""np"""						)

						for key in input_image_proc.keys():
									self.assertAlmostEqual(input_image_proc[key].sum()       ,       input_processor[key].sum()       ,       delta=1e-2						)




			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					"""lower newer"""

						UpperCamelCase         =					processor(text=__magic_name__       ,       return_tensors="""np"""						)

						UpperCamelCase         =					tokenizer(__magic_name__       ,       return_tensors="""np"""						)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key][0].tolist()       ,       encoded_processor[key][0].tolist()						)




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					"""lower newer"""
						UpperCamelCase         =					self.prepare_image_inputs()

						UpperCamelCase         =					processor(text=__magic_name__       ,       images=__magic_name__						)

						self.assertListEqual(list(inputs.keys()						)       ,       ["""input_ids""", """attention_mask""", """pixel_values"""]						)

						# test if it raises when no input is passed
						with pytest.raises(__magic_name__						):
									processor()




			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					"""google/owlvit-base-patch32"""
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(__magic_name__						)

						UpperCamelCase         =					["""cat""", """nasa badge"""]
						UpperCamelCase         =					processor(text=__magic_name__						)

						UpperCamelCase         =					1_6
						self.assertListEqual(list(inputs.keys()						)       ,       ["""input_ids""", """attention_mask"""]						)
						self.assertEqual(inputs["""input_ids"""].shape       ,       (2, seq_length)						)

						# test if it raises when no input is passed
						with pytest.raises(__magic_name__						):
									processor()




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					"""google/owlvit-base-patch32"""
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(__magic_name__						)

						UpperCamelCase         =					[["""cat""", """nasa badge"""], ["""person"""]]
						UpperCamelCase         =					processor(text=__magic_name__						)

						UpperCamelCase         =					1_6
						UpperCamelCase         =					len(__magic_name__						)
						UpperCamelCase         =					max([len(__magic_name__						) for texts in input_texts]						)

						self.assertListEqual(list(inputs.keys()						)       ,       ["""input_ids""", """attention_mask"""]						)
						self.assertEqual(inputs["""input_ids"""].shape       ,       (batch_size * num_max_text_queries, seq_length)						)

						# test if it raises when no input is passed
						with pytest.raises(__magic_name__						):
									processor()




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					"""google/owlvit-base-patch32"""
						UpperCamelCase         =					OwlViTProcessor.from_pretrained(__magic_name__						)

						UpperCamelCase         =					["""cat""", """nasa badge"""]
						UpperCamelCase         =					processor(text=__magic_name__						)

						UpperCamelCase         =					1_6
						UpperCamelCase         =					inputs["""input_ids"""]
						UpperCamelCase         =					[
						    [4_9_4_0_6, 2_3_6_8, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
						    [4_9_4_0_6, 6_8_4_1, 1_1_3_0_1, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
						]

						self.assertListEqual(list(inputs.keys()						)       ,       ["""input_ids""", """attention_mask"""]						)
						self.assertEqual(inputs["""input_ids"""].shape       ,       (2, seq_length)						)
						self.assertListEqual(list(input_ids[0]						)       ,       predicted_ids[0]						)
						self.assertListEqual(list(input_ids[1]						)       ,       predicted_ids[1]						)




			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					self.prepare_image_inputs()
						UpperCamelCase         =					self.prepare_image_inputs()

						UpperCamelCase         =					processor(images=__magic_name__       ,       query_images=__magic_name__						)

						self.assertListEqual(list(inputs.keys()						)       ,       ["""query_pixel_values""", """pixel_values"""]						)

						# test if it raises when no input is passed
						with pytest.raises(__magic_name__						):
									processor()





			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_image_processor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					OwlViTProcessor(tokenizer=__magic_name__       ,       image_processor=__magic_name__						)

						UpperCamelCase         =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						UpperCamelCase         =					processor.batch_decode(__magic_name__						)
						UpperCamelCase         =					tokenizer.batch_decode(__magic_name__						)

						self.assertListEqual(__magic_name__       ,       __magic_name__						)




