



import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		{"vocab_file": "sentencepiece.model"}

_lowercase    	=		{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

_lowercase    	=		{
    "google/rembert": 256,
}



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					VOCAB_FILES_NAMES
     UpperCamelCase__		     =					PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase__		     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




     def __init__(					self			,    a__			,    a__=False			,    a__=True			,    a__=True			,    a__="[CLS]"			,    a__="[SEP]"			,    a__="[UNK]"			,    a__="[SEP]"			,    a__="[PAD]"			,    a__="[CLS]"			,    a__="[MASK]"			,    **a__			,    ):
         super().__init__(
             do_lower_case=a__			,    remove_space=a__			,    keep_accents=a__			,    bos_token=a__			,    eos_token=a__			,    unk_token=a__			,    sep_token=a__			,    pad_token=a__			,    cls_token=a__			,    mask_token=a__			,    **a__			,    )

         A__					=       do_lower_case
         A__					=       remove_space
         A__					=       keep_accents
         A__					=       vocab_file

         A__					=       spm.SentencePieceProcessor()
         self.sp_model.Load(a__)




     @property
     def 		snake_case_		(					self):
         return len(self.sp_model)




     def 		snake_case_		(					self):
         A__					=       {self.convert_ids_to_tokens(a__): i for i in range(self.vocab_size)}
         vocab.update(self.added_tokens_encoder)
         return vocab




     def __getstate__(					self):
         A__					=       self.__dict__.copy()
         A__					=       None
         return state




     def __setstate__(					self			,    a__):
         A__					=       d
         A__					=       spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file)




     def 		snake_case_		(					self			,    a__			,    a__=False):
         A__					=       self.sp_model.EncodeAsPieces(a__)
         return pieces




     def 		snake_case_		(					self			,    a__):
         return self.sp_model.PieceToId(a__)




     def 		snake_case_		(					self			,    a__):
         return self.sp_model.IdToPiece(a__)




     def 		snake_case_		(					self			,    a__):
         A__					=       self.sp_model.decode_pieces(a__)
         return out_string




     def 		snake_case_		(					self			,    a__			,    a__ = None):
         A__					=       [self.sep_token_id]
         A__					=       [self.cls_token_id]
         if token_ids_a is None:
             return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep




     def 		snake_case_		(					self			,    a__			,    a__ = None			,    a__ = False):

         if already_has_special_tokens:
             if token_ids_a is not None:
                 raise ValueError(
                     '''You should not supply a second sequence if the provided sequence of '''
                     '''ids is already formatted with special tokens for the model.''')
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
             return [1] + ([0] * len(a__)) + [1] + ([0] * len(a__)) + [1]
         return [1] + ([0] * len(a__)) + [1]




     def 		snake_case_		(					self			,    a__			,    a__ = None):
         A__					=       [self.sep_token_id]
         A__					=       [self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep) * [0]
         return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]




     def 		snake_case_		(					self			,    a__			,    a__ = None):
         if not os.path.isdir(a__):
             logger.error('''Vocabulary path ({}) should be a directory'''.format(a__))
             return
         A__					=       os.path.join(
             a__			,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

         if os.path.abspath(self.vocab_file) != os.path.abspath(a__):
             copyfile(self.vocab_file			,    a__)

         return (out_vocab_file,)





from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
   from transformers import (
       TFBertModel,
       TFCLIPVisionModel,
       TFDeiTModel,
       TFRobertaModel,
       TFVisionTextDualEncoderModel,
       TFViTModel,
       VisionTextDualEncoderConfig,
   )

if is_vision_available():
   from PIL import Image

   from transformers import VisionTextDualEncoderProcessor



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[Any]						)->	Dict:
    if isinstance(UpperCamelCase_			,							collections.abc.Iterable						):
        return x
    return (x, x)



@require_tf
class      _UpperCAmelCase      :




     def 		snake_case_		(					self			,    a__			,    a__):
         pass




     def 		snake_case_		(					self):
         pass




     def 		snake_case_		(					self):
         pass




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__=None			,    **a__):
         A__					=       VisionTextDualEncoderConfig.from_vision_text_configs(a__			,    a__)

         A__					=       TFVisionTextDualEncoderModel(a__)

         A__					=       model(input_ids=a__			,    pixel_values=a__			,    attention_mask=a__)

         self.assertEqual(output['''text_embeds'''].shape			,    (input_ids.shape[0], config.projection_dim))
         self.assertEqual(output['''image_embeds'''].shape			,    (pixel_values.shape[0], config.projection_dim))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__=None			,    **a__):
         A__			,						A__					=       self.get_vision_text_model(a__			,    a__)
         A__					=       TFVisionTextDualEncoderModel(vision_model=a__			,    text_model=a__)

         A__					=       model(input_ids=a__			,    pixel_values=a__			,    attention_mask=a__)

         self.assertEqual(output['''text_embeds'''].shape			,    (input_ids.shape[0], model.config.projection_dim))
         self.assertEqual(output['''image_embeds'''].shape			,    (pixel_values.shape[0], model.config.projection_dim))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__=None			,    **a__):
         A__			,						A__					=       self.get_vision_text_model(a__			,    a__)
         A__					=       {'''vision_model''': vision_model, '''text_model''': text_model}
         A__					=       TFVisionTextDualEncoderModel.from_vision_text_pretrained(**a__)

         A__					=       model(input_ids=a__			,    pixel_values=a__			,    attention_mask=a__)

         self.assertEqual(output['''text_embeds'''].shape			,    (input_ids.shape[0], model.config.projection_dim))
         self.assertEqual(output['''image_embeds'''].shape			,    (pixel_values.shape[0], model.config.projection_dim))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__=None			,    **a__):
         A__			,						A__					=       self.get_vision_text_model(a__			,    a__)
         A__					=       TFVisionTextDualEncoderModel(vision_model=a__			,    text_model=a__)

         A__					=       model(input_ids=a__			,    pixel_values=a__			,    attention_mask=a__)
         A__					=       output[0].numpy()

         with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_pretrained(a__)
             A__					=       TFVisionTextDualEncoderModel.from_pretrained(a__)

             A__					=       model(input_ids=a__			,    pixel_values=a__			,    attention_mask=a__)
             A__					=       after_output[0].numpy()
             A__					=       np.amax(np.abs(out_a - out_a))
             self.assertLessEqual(a__			,    1e-5)




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__=None			,    **a__):
         A__			,						A__					=       self.get_vision_text_model(a__			,    a__)
         A__					=       TFVisionTextDualEncoderModel(vision_model=a__			,    text_model=a__)

         A__					=       model(
             input_ids=a__			,    pixel_values=a__			,    attention_mask=a__			,    output_attentions=a__)

         A__					=       output.vision_model_output.attentions
         self.assertEqual(len(a__)			,    vision_config.num_hidden_layers)

         # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
         A__					=       to_atuple(vision_model.config.image_size)
         A__					=       to_atuple(vision_model.config.patch_size)
         A__					=       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         A__					=       num_patches + 1
         self.assertEqual(vision_attentions[0].shape[-3:]			,    (vision_config.num_attention_heads, seq_len, seq_len))

         A__					=       output.text_model_output.attentions
         self.assertEqual(len(a__)			,    text_config.num_hidden_layers)

         self.assertEqual(
             text_attentions[0].shape[-3:]			,    (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])			,    )




     def 		snake_case_		(					self			,    a__			,    a__			,    a__):
         A__					=       np.abs((a - b)).max()
         self.assertLessEqual(a__			,    a__			,    F"Difference between torch and flax is {diff} (>= {tol}).")




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_model(**a__)




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         self.check_model_from_pretrained_configs(**a__)




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_from_pretrained(**a__)




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         self.check_save_load(**a__)




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         self.check_vision_text_output_attention(**a__)




     @slow
     def 		snake_case_		(					self):
         A__			,						A__					=       self.get_pretrained_model_and_inputs()

         A__					=       model_a(**a__)
         A__					=       outputs[0].numpy()

         with tempfile.TemporaryDirectory() as tmp_dirname:
             model_a.save_pretrained(a__)
             A__					=       TFVisionTextDualEncoderModel.from_pretrained(a__)

             A__					=       model_a(**a__)
             A__					=       after_outputs[0].numpy()
             A__					=       np.amax(np.abs(out_a - out_a))
             self.assertLessEqual(a__			,    1e-5)







@require_tf
class      _UpperCAmelCase      (							A__      ,   unittest.TestCase  ):




     def 		snake_case_		(					self):
         A__					=       TFVisionTextDualEncoderModel.from_vision_text_pretrained(
             '''hf-internal-testing/tiny-random-vit'''			,    '''hf-internal-testing/tiny-random-bert''')
         A__					=       1_3
         A__					=       floats_tensor(
             [
                 batch_size,
                 model.vision_model.config.num_channels,
                 model.vision_model.config.image_size,
                 model.vision_model.config.image_size,
             ])
         A__					=       ids_tensor([batch_size, 4]			,    model.text_model.config.vocab_size)
         A__					=       random_attention_mask([batch_size, 4])
         A__					=       {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

         return model, inputs




     def 		snake_case_		(					self			,    a__			,    a__):
         A__					=       TFViTModel(a__			,    name='''vision_model''')
         A__					=       TFBertModel(a__			,    name='''text_model''')
         return vision_model, text_model




     def 		snake_case_		(					self):
         A__					=       TFViTModelTester(self)
         A__					=       TFBertModelTester(self)
         A__					=       vit_model_tester.prepare_config_and_inputs()
         A__					=       bert_model_tester.prepare_config_and_inputs()

         A__			,						A__			,						A__					=       vision_config_and_inputs

         (
             (
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						
         )					=       text_config_and_inputs

         return {
             "text_config": text_config,
             "vision_config": vision_config,
             "pixel_values": pixel_values,
             "attention_mask": input_mask,
             "input_ids": input_ids,
             "text_token_type_ids": token_type_ids,
             "text_sequence_labels": sequence_labels,
             "text_token_labels": token_labels,
             "text_choice_labels": choice_labels,
         }







@require_tf
class      _UpperCAmelCase      (							A__      ,   unittest.TestCase  ):




     def 		snake_case_		(					self):
         # DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
         # just reinitialize it.
         A__					=       TFVisionTextDualEncoderModel.from_vision_text_pretrained(
             '''Rocketknight1/tiny-random-deit-tf'''			,    '''hf-internal-testing/tiny-random-roberta''')
         A__					=       1_3
         A__					=       floats_tensor(
             [
                 batch_size,
                 model.vision_model.config.num_channels,
                 model.vision_model.config.image_size,
                 model.vision_model.config.image_size,
             ])
         A__					=       ids_tensor([batch_size, 4]			,    model.text_model.config.vocab_size)
         A__					=       random_attention_mask([batch_size, 4])
         A__					=       {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

         return model, inputs




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__=None			,    **a__):
         A__			,						A__					=       self.get_vision_text_model(a__			,    a__)
         A__					=       TFVisionTextDualEncoderModel(vision_model=a__			,    text_model=a__)

         A__					=       model(
             input_ids=a__			,    pixel_values=a__			,    attention_mask=a__			,    output_attentions=a__)

         A__					=       output.vision_model_output.attentions
         self.assertEqual(len(a__)			,    vision_config.num_hidden_layers)

         # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
         A__					=       to_atuple(vision_model.config.image_size)
         A__					=       to_atuple(vision_model.config.patch_size)
         A__					=       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         A__					=       num_patches + 2
         self.assertEqual(vision_attentions[0].shape[-3:]			,    (vision_config.num_attention_heads, seq_len, seq_len))

         A__					=       output.text_model_output.attentions
         self.assertEqual(len(a__)			,    text_config.num_hidden_layers)

         self.assertEqual(
             text_attentions[0].shape[-3:]			,    (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])			,    )




     def 		snake_case_		(					self			,    a__			,    a__):
         A__					=       TFDeiTModel(a__			,    name='''vision_model''')
         A__					=       TFRobertaModel(a__			,    name='''text_model''')
         return vision_model, text_model




     def 		snake_case_		(					self):
         A__					=       TFDeiTModelTester(self)
         A__					=       TFRobertaModelTester(self)
         A__					=       vit_model_tester.prepare_config_and_inputs()
         A__					=       bert_model_tester.prepare_config_and_inputs()

         A__			,						A__			,						A__					=       vision_config_and_inputs

         (
             (
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						
         )					=       text_config_and_inputs

         return {
             "text_config": text_config,
             "vision_config": vision_config,
             "pixel_values": pixel_values,
             "attention_mask": input_mask,
             "input_ids": input_ids,
             "text_token_type_ids": token_type_ids,
             "text_sequence_labels": sequence_labels,
             "text_token_labels": token_labels,
             "text_choice_labels": choice_labels,
         }







@require_tf
class      _UpperCAmelCase      (							A__      ,   unittest.TestCase  ):




     def 		snake_case_		(					self):
         A__					=       TFVisionTextDualEncoderModel.from_vision_text_pretrained(
             '''Rocketknight1/tiny-random-clip-tf'''			,    '''hf-internal-testing/tiny-random-bert''')
         A__					=       1_3
         A__					=       floats_tensor(
             [
                 batch_size,
                 model.vision_model.config.num_channels,
                 model.vision_model.config.image_size,
                 model.vision_model.config.image_size,
             ])
         A__					=       ids_tensor([batch_size, 4]			,    model.text_model.config.vocab_size)
         A__					=       random_attention_mask([batch_size, 4])
         A__					=       {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

         return model, inputs




     def 		snake_case_		(					self			,    a__			,    a__):
         A__					=       TFCLIPVisionModel(a__			,    name='''vision_model''')
         A__					=       TFBertModel(a__			,    name='''text_model''')
         return vision_model, text_model




     def 		snake_case_		(					self):
         A__					=       TFCLIPVisionModelTester(self)
         A__					=       TFBertModelTester(self)
         A__					=       clip_model_tester.prepare_config_and_inputs()
         A__					=       bert_model_tester.prepare_config_and_inputs()

         A__			,						A__					=       vision_config_and_inputs

         (
             (
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						
         )					=       text_config_and_inputs

         return {
             "text_config": text_config,
             "vision_config": vision_config,
             "pixel_values": pixel_values,
             "attention_mask": input_mask,
             "input_ids": input_ids,
             "text_token_type_ids": token_type_ids,
             "text_sequence_labels": sequence_labels,
             "text_token_labels": token_labels,
             "text_choice_labels": choice_labels,
         }







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




     @slow
     def 		snake_case_		(					self):
         A__					=       TFVisionTextDualEncoderModel.from_pretrained(
             '''clip-italian/clip-italian'''			,    logit_scale_init_value=1.0			,    from_pt=a__)
         A__					=       VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian''')

         A__					=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
         A__					=       processor(
             text=['''una foto di un gatto''', '''una foto di un cane''']			,    images=a__			,    padding=a__			,    return_tensors='''np''')

         A__					=       model(**a__)

         # verify the logits
         self.assertEqual(outputs.logits_per_image.shape			,    (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]))
         self.assertEqual(
             outputs.logits_per_text.shape			,    (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])			,    )

         A__					=       np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]])

         self.assertTrue(np.allclose(outputs.logits_per_image.numpy()			,    a__			,    atol=1e-3))
