



from __future__ import annotations





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    len(lowerCamelCase_    ) // 2

    # choose the middle 3 elements
    __a							=    lst[m - 1 : m + 2]

    # if middle element is peak
    if three[1] > three[0] and three[1] > three[2]:
        return three[1]

    # if increasing, recurse on right
    elif three[0] < three[2]:
        if len(lst[:m]    ) == 2:
            m -= 1
        return peak(lst[m:]    )

    # decreasing
    else:
        if len(lst[:m]    ) == 2:
            m += 1
        return peak(lst[:m]    )


if __name__ == "__main__":
 import doctest

 doctest.testmod()










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 					__snake_case     (      _UpperCAmelCase    ):
    if isinstance(_UpperCAmelCase       ,			collections.abc.Iterable    ):
        return x
    return (x, x)

@require_tf
class    _A	:



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=None					,		**__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           __a							=    VisionTextDualEncoderConfig.from_vision_text_configs(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)

           __a							=    TFVisionTextDualEncoderModel(__SCREAMING_SNAKE_CASE)

           __a							=    model(input_ids=__SCREAMING_SNAKE_CASE					,		pixel_values=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE)

           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 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					List[str]=None					,		**__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __a	,       __a							=    self.get_vision_text_model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE					,		text_model=__SCREAMING_SNAKE_CASE)

           __a							=    model(input_ids=__SCREAMING_SNAKE_CASE					,		pixel_values=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE)

           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 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=None					,		**__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           __a	,       __a							=    self.get_vision_text_model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    {'''vision_model''': vision_model, '''text_model''': text_model}
           __a							=    TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__SCREAMING_SNAKE_CASE)

           __a							=    model(input_ids=__SCREAMING_SNAKE_CASE					,		pixel_values=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE)

           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 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Dict=None					,		**__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           __a	,       __a							=    self.get_vision_text_model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE					,		text_model=__SCREAMING_SNAKE_CASE)

           __a							=    model(input_ids=__SCREAMING_SNAKE_CASE					,		pixel_values=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE)
           __a							=    output[0].numpy()

           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(__SCREAMING_SNAKE_CASE)
               __a							=    TFVisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE)

               __a							=    model(input_ids=__SCREAMING_SNAKE_CASE					,		pixel_values=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE)
               __a							=    after_output[0].numpy()
               __a							=    np.amax(np.abs(out_a - out_a))
               self.assertLessEqual(__SCREAMING_SNAKE_CASE					,		1E-5)



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					int=None					,		**__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           __a	,       __a							=    self.get_vision_text_model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE					,		text_model=__SCREAMING_SNAKE_CASE)

           __a							=    model(
               input_ids=__SCREAMING_SNAKE_CASE					,		pixel_values=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		output_attentions=__SCREAMING_SNAKE_CASE)

           __a							=    output.vision_model_output.attentions
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		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(__SCREAMING_SNAKE_CASE)					,		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 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					np.ndarray					,		__SCREAMING_SNAKE_CASE							:					np.ndarray					,		__SCREAMING_SNAKE_CASE							:					float):


           '''simple docstring'''

           __a							=    np.abs((a - b)).max()
           self.assertLessEqual(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		F'Difference between torch and flax is {diff} (>= {tol}).')



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    self.prepare_config_and_inputs()
           self.check_vision_text_dual_encoder_model(**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    self.prepare_config_and_inputs()
           self.check_model_from_pretrained_configs(**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    self.prepare_config_and_inputs()
           self.check_vision_text_dual_encoder_from_pretrained(**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.prepare_config_and_inputs()
           self.check_save_load(**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    self.prepare_config_and_inputs()
           self.check_vision_text_output_attention(**__SCREAMING_SNAKE_CASE)





       @slow
       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a	,       __a							=    self.get_pretrained_model_and_inputs()

           __a							=    model_a(**__SCREAMING_SNAKE_CASE)
           __a							=    outputs[0].numpy()

           with tempfile.TemporaryDirectory() as tmp_dirname:
               model_a.save_pretrained(__SCREAMING_SNAKE_CASE)
               __a							=    TFVisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE)

               __a							=    model_a(**__SCREAMING_SNAKE_CASE)
               __a							=    after_outputs[0].numpy()
               __a							=    np.amax(np.abs(out_a - out_a))
               self.assertLessEqual(__SCREAMING_SNAKE_CASE					,		1E-5)


@require_tf
class    _A	(     __UpperCAmelCase				,unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    TFVisionTextDualEncoderModel.from_vision_text_pretrained(
               '''hf-internal-testing/tiny-random-vit'''					,		'''hf-internal-testing/tiny-random-bert''')
           __a							=    13
           __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 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a							=    TFViTModel(__SCREAMING_SNAKE_CASE					,		name='''vision_model''')
           __a							=    TFBertModel(__SCREAMING_SNAKE_CASE					,		name='''text_model''')
           return vision_model, text_model





       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __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    _A	(     __UpperCAmelCase				,unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    TFVisionTextDualEncoderModel.from_vision_text_pretrained(
               '''Rocketknight1/tiny-random-deit-tf'''					,		'''hf-internal-testing/tiny-random-roberta''')
           __a							=    13
           __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 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=None					,		**__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a	,       __a							=    self.get_vision_text_model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    TFVisionTextDualEncoderModel(vision_model=__SCREAMING_SNAKE_CASE					,		text_model=__SCREAMING_SNAKE_CASE)

           __a							=    model(
               input_ids=__SCREAMING_SNAKE_CASE					,		pixel_values=__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		output_attentions=__SCREAMING_SNAKE_CASE)

           __a							=    output.vision_model_output.attentions
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)					,		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(__SCREAMING_SNAKE_CASE)					,		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 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __a							=    TFDeiTModel(__SCREAMING_SNAKE_CASE					,		name='''vision_model''')
           __a							=    TFRobertaModel(__SCREAMING_SNAKE_CASE					,		name='''text_model''')
           return vision_model, text_model





       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           __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    _A	(     __UpperCAmelCase				,unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    TFVisionTextDualEncoderModel.from_vision_text_pretrained(
               '''Rocketknight1/tiny-random-clip-tf'''					,		'''hf-internal-testing/tiny-random-bert''')
           __a							=    13
           __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 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           __a							=    TFCLIPVisionModel(__SCREAMING_SNAKE_CASE					,		name='''vision_model''')
           __a							=    TFBertModel(__SCREAMING_SNAKE_CASE					,		name='''text_model''')
           return vision_model, text_model





       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __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    _A	(     unittest.TestCase   ):



       @slow
       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    TFVisionTextDualEncoderModel.from_pretrained(
               '''clip-italian/clip-italian'''					,		logit_scale_init_value=1.0					,		from_pt=__SCREAMING_SNAKE_CASE)
           __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=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		return_tensors='''np''')

           __a							=    model(**__SCREAMING_SNAKE_CASE)

           # 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_28_47_27, 0.3_10_41_22]])

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





