



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__						(							_SCREAMING_SNAKE_CASE       :			List[str]    ):
				"""simple docstring"""







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






@require_tf
class    SCREAMING_SNAKE_CASE			:





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		Dict      ,				__lowercase		:		List[Any]	):

											'''simple docstring'''
											pass





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											pass





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

											'''simple docstring'''
											pass





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

											'''simple docstring'''
											__a       =		VisionTextDualEncoderConfig.from_vision_text_configs(__lowercase      ,				__lowercase	)

											__a       =		TFVisionTextDualEncoderModel(__lowercase	)

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

											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 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		int      ,				__lowercase		:		Any      ,				__lowercase		:		str      ,				__lowercase		:		Any      ,				__lowercase		:		Optional[int]=None      ,				**__lowercase		:		List[Any]	):

											'''simple docstring'''
											__a     ,					__a       =		self.get_vision_text_model(__lowercase      ,				__lowercase	)
											__a       =		TFVisionTextDualEncoderModel(vision_model=__lowercase      ,				text_model=__lowercase	)

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

											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 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		Any      ,				__lowercase		:		int      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		Optional[int]=None      ,				**__lowercase		:		Tuple	):

											'''simple docstring'''
											__a     ,					__a       =		self.get_vision_text_model(__lowercase      ,				__lowercase	)
											__a       =		{"""vision_model""": vision_model, """text_model""": text_model}
											__a       =		TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__lowercase	)

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

											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 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		List[str]      ,				__lowercase		:		Any      ,				__lowercase		:		Dict      ,				__lowercase		:		int      ,				__lowercase		:		Optional[int]=None      ,				**__lowercase		:		Any	):

											'''simple docstring'''
											__a     ,					__a       =		self.get_vision_text_model(__lowercase      ,				__lowercase	)
											__a       =		TFVisionTextDualEncoderModel(vision_model=__lowercase      ,				text_model=__lowercase	)

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

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

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





							def 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		List[str]      ,				__lowercase		:		Tuple      ,				__lowercase		:		List[str]      ,				__lowercase		:		str      ,				__lowercase		:		Tuple=None      ,				**__lowercase		:		Any	):

											'''simple docstring'''
											__a     ,					__a       =		self.get_vision_text_model(__lowercase      ,				__lowercase	)
											__a       =		TFVisionTextDualEncoderModel(vision_model=__lowercase      ,				text_model=__lowercase	)

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

											__a       =		output.vision_model_output.attentions
											self.assertEqual(len(__lowercase	)      ,				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(__lowercase	)      ,				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 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		float	):

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





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		self.prepare_config_and_inputs()
											self.check_vision_text_dual_encoder_model(**__lowercase	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		self.prepare_config_and_inputs()
											self.check_model_from_pretrained_configs(**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		self.prepare_config_and_inputs()
											self.check_vision_text_dual_encoder_from_pretrained(**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		self.prepare_config_and_inputs()
											self.check_save_load(**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		self.prepare_config_and_inputs()
											self.check_vision_text_output_attention(**__lowercase	)







							@slow
							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a     ,					__a       =		self.get_pretrained_model_and_inputs()

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

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

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






@require_tf
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''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 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		str      ,				__lowercase		:		List[Any]	):

											'''simple docstring'''
											__a       =		TFViTModel(__lowercase      ,				name="""vision_model"""	)
											__a       =		TFBertModel(__lowercase      ,				name="""text_model"""	)
											return vision_model, text_model







							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''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    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''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 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		str      ,				__lowercase		:		Optional[int]      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		str      ,				__lowercase		:		Any=None      ,				**__lowercase		:		str	):

											'''simple docstring'''
											__a     ,					__a       =		self.get_vision_text_model(__lowercase      ,				__lowercase	)
											__a       =		TFVisionTextDualEncoderModel(vision_model=__lowercase      ,				text_model=__lowercase	)

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

											__a       =		output.vision_model_output.attentions
											self.assertEqual(len(__lowercase	)      ,				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(__lowercase	)      ,				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 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		int      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		TFDeiTModel(__lowercase      ,				name="""vision_model"""	)
											__a       =		TFRobertaModel(__lowercase      ,				name="""text_model"""	)
											return vision_model, text_model







							def 						UpperCamelCase_					(   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    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''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 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		Dict      ,				__lowercase		:		List[Any]	):

											'''simple docstring'''
											__a       =		TFCLIPVisionModel(__lowercase      ,				name="""vision_model"""	)
											__a       =		TFBertModel(__lowercase      ,				name="""text_model"""	)
											return vision_model, text_model







							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''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    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							@slow
							def 						UpperCamelCase_					(   self		:		Dict	):

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

											__a       =		model(**__lowercase	)

											# 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([[1.2284727, 0.3104122]]	)

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





def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			list				,     _SCREAMING_SNAKE_CASE       :			list				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				if index == number_of_items:
								return 0
				__a       =		0
				__a       =		0
				__a       =		knapsack(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     index + 1    )
				if weights[index] <= max_weight:
								__a       =		values[index] + knapsack(
								    _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     max_weight - weights[index]				,     index + 1    )
				return max(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
