
import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
  import numpy as np
  import torch

  from . import (
      AlbertForPreTraining,
      BartForConditionalGeneration,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      CamembertForMaskedLM,
      CTRLLMHeadModel,
      DistilBertForMaskedLM,
      DistilBertForQuestionAnswering,
      DPRContextEncoder,
      DPRQuestionEncoder,
      DPRReader,
      ElectraForPreTraining,
      FlaubertWithLMHeadModel,
      GPTaLMHeadModel,
      LayoutLMForMaskedLM,
      LxmertForPreTraining,
      LxmertVisualFeatureEncoder,
      OpenAIGPTLMHeadModel,
      RobertaForMaskedLM,
      RobertaForSequenceClassification,
      TaForConditionalGeneration,
      TransfoXLLMHeadModel,
      XLMRobertaForMaskedLM,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
  )


logging.set_verbosity_info()

lowercase        =  {
    """bart""": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """bert""": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-base-cased-finetuned-mrpc""": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """dpr""": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """gpt2""": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlnet""": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm""": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm-roberta""": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """transfo-xl""": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """openai-gpt""": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """roberta""": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """layoutlm""": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """roberta-large-mnli""": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """camembert""": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """flaubert""": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert""": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert-base-distilled-squad""": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert""": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert-visual-feature-encoder""": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """ctrl""": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """albert""": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """t5""": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """electra""": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """wav2vec2""": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Tuple			,				UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      Tuple			,				UpperCAmelCase__				:      Tuple=False			,				UpperCAmelCase__				:      Dict=True       )  ->    Union[str, Any]:



       if model_type not in MODEL_CLASSES:
              raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()       )}.'''       )

       lowerCamelCase_							  =					MODEL_CLASSES[model_type]

       # Initialise TF model
       if config_file in aws_config_map:
              lowerCamelCase_							  =					cached_file(UpperCAmelCase__			,				UpperCAmelCase__			,				force_download=not use_cached_models       )
       lowerCamelCase_							  =					config_class.from_json_file(UpperCAmelCase__       )
       lowerCamelCase_							  =					True
       lowerCamelCase_							  =					True
       print(F'''Building TensorFlow model from configuration: {config}'''       )
       lowerCamelCase_							  =					model_class(UpperCAmelCase__       )

       # Load weights from tf checkpoint
       if pytorch_checkpoint_path in aws_config_map.keys():
              lowerCamelCase_							  =					cached_file(
                  UpperCAmelCase__			,				UpperCAmelCase__			,				force_download=not use_cached_models       )
       # Load PyTorch checkpoint in tf2 model:
       lowerCamelCase_							  =					load_pytorch_checkpoint_in_tfa_model(UpperCAmelCase__			,				UpperCAmelCase__       )

       if compare_with_pt_model:
              lowerCamelCase_							  =					tf_model(tf_model.dummy_inputs			,				training=UpperCAmelCase__       )  # build the network

              lowerCamelCase_							  =					torch.load(UpperCAmelCase__			,				map_location="""cpu"""       )
              lowerCamelCase_							  =					pt_model_class.from_pretrained(
                  pretrained_model_name_or_path=UpperCAmelCase__			,				config=UpperCAmelCase__			,				state_dict=UpperCAmelCase__       )

              with torch.no_grad():
                     lowerCamelCase_							  =					pt_model(**pt_model.dummy_inputs       )

              lowerCamelCase_							  =					pto[0].numpy()
              lowerCamelCase_							  =					tfo[0].numpy()
              lowerCamelCase_							  =					np.amax(np.abs(np_pt - np_tf       )       )
              print(F'''Max absolute difference between models outputs {diff}'''       )
              assert diff <= 2e-2, F'''Error, model absolute difference is >2e-2: {diff}'''

       # Save pytorch-model
       print(F'''Save TensorFlow model to {tf_dump_path}'''       )
       tf_model.save_weights(UpperCAmelCase__			,				save_format="""h5"""       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Tuple			,				UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      List[str]=None			,				UpperCAmelCase__				:      int=None			,				UpperCAmelCase__				:      Union[str, Any]=False			,				UpperCAmelCase__				:      str=False			,				UpperCAmelCase__				:      str=False			,				UpperCAmelCase__				:      Optional[int]=False			,				)  ->    List[Any]:



       if args_model_type is None:
              lowerCamelCase_							  =					list(MODEL_CLASSES.keys()       )
       else:
              lowerCamelCase_							  =					[args_model_type]

       for j, model_type in enumerate(UpperCAmelCase__			,				start=1       ):
              print("""=""" * 1_0_0       )
              print(F''' Converting model type {j}/{len(UpperCAmelCase__       )}: {model_type}'''       )
              print("""=""" * 1_0_0       )
              if model_type not in MODEL_CLASSES:
                     raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()       )}.'''       )

              lowerCamelCase_							  =					MODEL_CLASSES[model_type]

              if model_shortcut_names_or_path is None:
                     lowerCamelCase_							  =					list(aws_model_maps.keys()       )
              if config_shortcut_names_or_path is None:
                     lowerCamelCase_							  =					model_shortcut_names_or_path

              for i, (model_shortcut_name, config_shortcut_name) in enumerate(
                  zip(UpperCAmelCase__			,				UpperCAmelCase__       )			,				start=1       ):
                     print("""-""" * 1_0_0       )
                     if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                            if not only_convert_finetuned_models:
                                   print(F'''    Skipping finetuned checkpoint {model_shortcut_name}'''       )
                                   continue
                            lowerCamelCase_							  =					model_shortcut_name
                     elif only_convert_finetuned_models:
                            print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''       )
                            continue
                     print(
                         F'''    Converting checkpoint {i}/{len(UpperCAmelCase__       )}: {model_shortcut_name} - model_type {model_type}'''       )
                     print("""-""" * 1_0_0       )

                     if config_shortcut_name in aws_config_map:
                            lowerCamelCase_							  =					cached_file(UpperCAmelCase__			,				UpperCAmelCase__			,				force_download=not use_cached_models       )
                     else:
                            lowerCamelCase_							  =					config_shortcut_name

                     if model_shortcut_name in aws_model_maps:
                            lowerCamelCase_							  =					cached_file(UpperCAmelCase__			,				UpperCAmelCase__			,				force_download=not use_cached_models       )
                     else:
                            lowerCamelCase_							  =					model_shortcut_name

                     if os.path.isfile(UpperCAmelCase__       ):
                            lowerCamelCase_							  =					"converted_model"

                     convert_pt_checkpoint_to_tf(
                         model_type=UpperCAmelCase__			,				pytorch_checkpoint_path=UpperCAmelCase__			,				config_file=UpperCAmelCase__			,				tf_dump_path=os.path.join(UpperCAmelCase__			,				model_shortcut_name + """-tf_model.h5"""       )			,				compare_with_pt_model=UpperCAmelCase__			,				)
                     if remove_cached_files:
                            os.remove(UpperCAmelCase__       )
                            os.remove(UpperCAmelCase__       )


if __name__ == "__main__":
  lowercase        =  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
  )
  parser.add_argument(
      '''--model_type''',
      default=None,
      type=str,
      help=(
          F"""Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and """
          '''convert all the models from AWS.'''
      ),
  )
  parser.add_argument(
      '''--pytorch_checkpoint_path''',
      default=None,
      type=str,
      help=(
          '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
          '''If not given, will download and convert all the checkpoints from AWS.'''
      ),
  )
  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      help=(
          '''The config json file corresponding to the pre-trained model. \n'''
          '''This specifies the model architecture. If not given and '''
          '''--pytorch_checkpoint_path is not given or is a shortcut name '''
          '''use the configuration associated to the shortcut name on the AWS'''
      ),
  )
  parser.add_argument(
      '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
  )
  parser.add_argument(
      '''--use_cached_models''',
      action='''store_true''',
      help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
  )
  parser.add_argument(
      '''--remove_cached_files''',
      action='''store_true''',
      help='''Remove pytorch models after conversion (save memory when converting in batches).''',
  )
  parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
  lowercase        =  parser.parse_args()

  # if args.pytorch_checkpoint_path is not None:
  #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
  #                                 args.pytorch_checkpoint_path,
  #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
  #                                 args.tf_dump_path,
  #                                 compare_with_pt_model=args.compare_with_pt_model,
  #                                 use_cached_models=args.use_cached_models)
  # else:
  convert_all_pt_checkpoints_to_tf(
      args.model_type.lower() if args.model_type is not None else None,
      args.tf_dump_path,
      model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
      if args.pytorch_checkpoint_path is not None
      else None,
      config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
      compare_with_pt_model=args.compare_with_pt_model,
      use_cached_models=args.use_cached_models,
      remove_cached_files=args.remove_cached_files,
      only_convert_finetuned_models=args.only_convert_finetuned_models,
  )







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       __A(				unittest.TestCase       ):

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					tempfile.mkdtemp()

												# fmt: off
												lowerCamelCase_							  =					["""""", """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
												lowerCamelCase_							  =					dict(zip(__UpperCamelCase   ,					range(len(__UpperCamelCase   )   )   )   )
												lowerCamelCase_							  =					["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
												lowerCamelCase_							  =					{"""unk_token""": """<unk>"""}

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

												lowerCamelCase_							  =					{
												    """do_resize""": True,
												    """size""": 2_0,
												    """do_center_crop""": True,
												    """crop_size""": 1_8,
												    """do_normalize""": True,
												    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
												    """image_std""": [0.26862954, 0.26130258, 0.27577711],
												}
												lowerCamelCase_							  =					os.path.join(self.tmpdirname   ,					__UpperCamelCase   )
												with open(self.image_processor_file   ,					"""w"""   ,					encoding="""utf-8"""   ) as fp:
																			json.dump(__UpperCamelCase   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     str   ,					**__UpperCamelCase					:     Optional[Any]   ):
												return CLIPTokenizer.from_pretrained(self.tmpdirname   ,					pad_token="""!"""   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ,					**__UpperCamelCase					:     List[Any]   ):
												return CLIPTokenizerFast.from_pretrained(self.tmpdirname   ,					pad_token="""!"""   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     List[Any]   ,					**__UpperCamelCase					:     Optional[int]   ):
												return OwlViTImageProcessor.from_pretrained(self.tmpdirname   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												shutil.rmtree(self.tmpdirname   )

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					[np.random.randint(2_5_5   ,					size=(3, 3_0, 4_0_0)   ,					dtype=np.uinta   )]

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

												return image_inputs

					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					self.get_tokenizer()
												lowerCamelCase_							  =					self.get_rust_tokenizer()
												lowerCamelCase_							  =					self.get_image_processor()

												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )
												processor_slow.save_pretrained(self.tmpdirname   )
												lowerCamelCase_							  =					OwlViTProcessor.from_pretrained(self.tmpdirname   ,					use_fast=__UpperCamelCase   )

												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )
												processor_fast.save_pretrained(self.tmpdirname   )
												lowerCamelCase_							  =					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   ,					__UpperCamelCase   )
												self.assertIsInstance(processor_fast.tokenizer   ,					__UpperCamelCase   )

												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   ,					__UpperCamelCase   )
												self.assertIsInstance(processor_fast.image_processor   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=self.get_tokenizer()   ,					image_processor=self.get_image_processor()   )
												processor.save_pretrained(self.tmpdirname   )

												lowerCamelCase_							  =					self.get_tokenizer(bos_token="""(BOS)"""   ,					eos_token="""(EOS)"""   )
												lowerCamelCase_							  =					self.get_image_processor(do_normalize=__UpperCamelCase   )

												lowerCamelCase_							  =					OwlViTProcessor.from_pretrained(
												    self.tmpdirname   ,					bos_token="""(BOS)"""   ,					eos_token="""(EOS)"""   ,					do_normalize=__UpperCamelCase   )

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

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

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					self.get_image_processor()
												lowerCamelCase_							  =					self.get_tokenizer()

												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )

												lowerCamelCase_							  =					self.prepare_image_inputs()

												lowerCamelCase_							  =					image_processor(__UpperCamelCase   ,					return_tensors="""np"""   )
												lowerCamelCase_							  =					processor(images=__UpperCamelCase   ,					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    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.get_image_processor()
												lowerCamelCase_							  =					self.get_tokenizer()

												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )

												lowerCamelCase_							  =					"""lower newer"""

												lowerCamelCase_							  =					processor(text=__UpperCamelCase   ,					return_tensors="""np"""   )

												lowerCamelCase_							  =					tokenizer(__UpperCamelCase   ,					return_tensors="""np"""   )

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

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					self.get_image_processor()
												lowerCamelCase_							  =					self.get_tokenizer()

												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )

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

												lowerCamelCase_							  =					processor(text=__UpperCamelCase   ,					images=__UpperCamelCase   )

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

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

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					"""google/owlvit-base-patch32"""
												lowerCamelCase_							  =					OwlViTProcessor.from_pretrained(__UpperCamelCase   )

												lowerCamelCase_							  =					["""cat""", """nasa badge"""]
												lowerCamelCase_							  =					processor(text=__UpperCamelCase   )

												lowerCamelCase_							  =					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(__UpperCamelCase   ):
																			processor()

					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					"""google/owlvit-base-patch32"""
												lowerCamelCase_							  =					OwlViTProcessor.from_pretrained(__UpperCamelCase   )

												lowerCamelCase_							  =					[["""cat""", """nasa badge"""], ["""person"""]]
												lowerCamelCase_							  =					processor(text=__UpperCamelCase   )

												lowerCamelCase_							  =					1_6
												lowerCamelCase_							  =					len(__UpperCamelCase   )
												lowerCamelCase_							  =					max([len(__UpperCamelCase   ) 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(__UpperCamelCase   ):
																			processor()

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					"""google/owlvit-base-patch32"""
												lowerCamelCase_							  =					OwlViTProcessor.from_pretrained(__UpperCamelCase   )

												lowerCamelCase_							  =					["""cat""", """nasa badge"""]
												lowerCamelCase_							  =					processor(text=__UpperCamelCase   )

												lowerCamelCase_							  =					1_6
												lowerCamelCase_							  =					inputs["""input_ids"""]
												lowerCamelCase_							  =					[
												    [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    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					self.get_image_processor()
												lowerCamelCase_							  =					self.get_tokenizer()

												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )

												lowerCamelCase_							  =					self.prepare_image_inputs()
												lowerCamelCase_							  =					self.prepare_image_inputs()

												lowerCamelCase_							  =					processor(images=__UpperCamelCase   ,					query_images=__UpperCamelCase   )

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

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



					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_							  =					self.get_image_processor()
												lowerCamelCase_							  =					self.get_tokenizer()

												lowerCamelCase_							  =					OwlViTProcessor(tokenizer=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )

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

												lowerCamelCase_							  =					processor.batch_decode(__UpperCamelCase   )
												lowerCamelCase_							  =					tokenizer.batch_decode(__UpperCamelCase   )

												self.assertListEqual(__UpperCamelCase   ,					__UpperCamelCase   )





