






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




@require_tf
class 					UpperCamelCase_ :



	'''simple docstring'''




	def 				_UpperCamelCase     (			self			,     a			,     a   )			->       Dict:
								pass




	def 				_UpperCamelCase     (			self   )			->       Any:
								pass




	def 				_UpperCamelCase     (			self   )			->       int:
								pass




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a			,     a			,     a=None			,     **a   )			->       Union[str, Any]:
								snake_case_           =				VisionTextDualEncoderConfig.from_vision_text_configs(a			,     a   )

								snake_case_           =				TFVisionTextDualEncoderModel(a   )

								snake_case_           =				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 				_UpperCamelCase     (			self			,     a			,     a			,     a			,     a			,     a=None			,     **a   )			->       Tuple:
								snake_case_     ,						snake_case_           =				self.get_vision_text_model(a			,     a   )
								snake_case_           =				TFVisionTextDualEncoderModel(vision_model=a			,     text_model=a   )

								snake_case_           =				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 				_UpperCamelCase     (			self			,     a			,     a			,     a			,     a			,     a=None			,     **a   )			->       Dict:
								snake_case_     ,						snake_case_           =				self.get_vision_text_model(a			,     a   )
								snake_case_           =				{'vision_model': vision_model, 'text_model': text_model}
								snake_case_           =				TFVisionTextDualEncoderModel.from_vision_text_pretrained(**a   )

								snake_case_           =				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 				_UpperCamelCase     (			self			,     a			,     a			,     a			,     a			,     a=None			,     **a   )			->       List[str]:
								snake_case_     ,						snake_case_           =				self.get_vision_text_model(a			,     a   )
								snake_case_           =				TFVisionTextDualEncoderModel(vision_model=a			,     text_model=a   )

								snake_case_           =				model(input_ids=a			,     pixel_values=a			,     attention_mask=a   )
								snake_case_           =				output[0].numpy()

								with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(a   )
															snake_case_           =				TFVisionTextDualEncoderModel.from_pretrained(a   )

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




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a			,     a			,     a=None			,     **a   )			->       Optional[int]:
								snake_case_     ,						snake_case_           =				self.get_vision_text_model(a			,     a   )
								snake_case_           =				TFVisionTextDualEncoderModel(vision_model=a			,     text_model=a   )

								snake_case_           =				model(
								    input_ids=a			,     pixel_values=a			,     attention_mask=a			,     output_attentions=a   )

								snake_case_           =				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)
								snake_case_           =				to_atuple(vision_model.config.image_size   )
								snake_case_           =				to_atuple(vision_model.config.patch_size   )
								snake_case_           =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								snake_case_           =				num_patches + 1
								self.assertEqual(vision_attentions[0].shape[-3:]			,     (vision_config.num_attention_heads, seq_len, seq_len)   )

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




	def 				_UpperCamelCase     (			self   )			->       List[str]:
								snake_case_           =				self.prepare_config_and_inputs()
								self.check_vision_text_dual_encoder_model(**a   )




	def 				_UpperCamelCase     (			self   )			->       Tuple:
								snake_case_           =				self.prepare_config_and_inputs()
								self.check_model_from_pretrained_configs(**a   )




	def 				_UpperCamelCase     (			self   )			->       str:
								snake_case_           =				self.prepare_config_and_inputs()
								self.check_vision_text_dual_encoder_from_pretrained(**a   )




	def 				_UpperCamelCase     (			self   )			->       List[str]:
								snake_case_           =				self.prepare_config_and_inputs()
								self.check_save_load(**a   )




	def 				_UpperCamelCase     (			self   )			->       List[str]:
								snake_case_           =				self.prepare_config_and_inputs()
								self.check_vision_text_output_attention(**a   )






	@slow
	def 				_UpperCamelCase     (			self   )			->       int:
								snake_case_     ,						snake_case_           =				self.get_pretrained_model_and_inputs()

								snake_case_           =				model_a(**a   )
								snake_case_           =				outputs[0].numpy()

								with tempfile.TemporaryDirectory() as tmp_dirname:
															model_a.save_pretrained(a   )
															snake_case_           =				TFVisionTextDualEncoderModel.from_pretrained(a   )

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






@require_tf
class 					UpperCamelCase_ (     snake_case_			,   unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_           =				TFVisionTextDualEncoderModel.from_vision_text_pretrained(
								    'hf-internal-testing/tiny-random-vit'			,     'hf-internal-testing/tiny-random-bert'   )
								snake_case_           =				13
								snake_case_           =				floats_tensor(
								    [
								        batch_size,
								        model.vision_model.config.num_channels,
								        model.vision_model.config.image_size,
								        model.vision_model.config.image_size,
								    ]   )
								snake_case_           =				ids_tensor([batch_size, 4]			,     model.text_model.config.vocab_size   )
								snake_case_           =				random_attention_mask([batch_size, 4]   )
								snake_case_           =				{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

								return model, inputs




	def 				_UpperCamelCase     (			self			,     a			,     a   )			->       int:
								snake_case_           =				TFViTModel(a			,     name='vision_model'   )
								snake_case_           =				TFBertModel(a			,     name='text_model'   )
								return vision_model, text_model






	def 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				TFViTModelTester(self   )
								snake_case_           =				TFBertModelTester(self   )
								snake_case_           =				vit_model_tester.prepare_config_and_inputs()
								snake_case_           =				bert_model_tester.prepare_config_and_inputs()

								snake_case_     ,						snake_case_     ,						snake_case_           =				vision_config_and_inputs

								(
								    (
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						
								)           =				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_ (     snake_case_			,   unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
								# just reinitialize it.
								snake_case_           =				TFVisionTextDualEncoderModel.from_vision_text_pretrained(
								    'Rocketknight1/tiny-random-deit-tf'			,     'hf-internal-testing/tiny-random-roberta'   )
								snake_case_           =				13
								snake_case_           =				floats_tensor(
								    [
								        batch_size,
								        model.vision_model.config.num_channels,
								        model.vision_model.config.image_size,
								        model.vision_model.config.image_size,
								    ]   )
								snake_case_           =				ids_tensor([batch_size, 4]			,     model.text_model.config.vocab_size   )
								snake_case_           =				random_attention_mask([batch_size, 4]   )
								snake_case_           =				{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

								return model, inputs




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a			,     a			,     a=None			,     **a   )			->       List[Any]:
								snake_case_     ,						snake_case_           =				self.get_vision_text_model(a			,     a   )
								snake_case_           =				TFVisionTextDualEncoderModel(vision_model=a			,     text_model=a   )

								snake_case_           =				model(
								    input_ids=a			,     pixel_values=a			,     attention_mask=a			,     output_attentions=a   )

								snake_case_           =				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)
								snake_case_           =				to_atuple(vision_model.config.image_size   )
								snake_case_           =				to_atuple(vision_model.config.patch_size   )
								snake_case_           =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								snake_case_           =				num_patches + 2
								self.assertEqual(vision_attentions[0].shape[-3:]			,     (vision_config.num_attention_heads, seq_len, seq_len)   )

								snake_case_           =				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 				_UpperCamelCase     (			self			,     a			,     a   )			->       Union[str, Any]:
								snake_case_           =				TFDeiTModel(a			,     name='vision_model'   )
								snake_case_           =				TFRobertaModel(a			,     name='text_model'   )
								return vision_model, text_model






	def 				_UpperCamelCase     (			self   )			->       Any:
								snake_case_           =				TFDeiTModelTester(self   )
								snake_case_           =				TFRobertaModelTester(self   )
								snake_case_           =				vit_model_tester.prepare_config_and_inputs()
								snake_case_           =				bert_model_tester.prepare_config_and_inputs()

								snake_case_     ,						snake_case_     ,						snake_case_           =				vision_config_and_inputs

								(
								    (
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						
								)           =				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_ (     snake_case_			,   unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       int:
								snake_case_           =				TFVisionTextDualEncoderModel.from_vision_text_pretrained(
								    'Rocketknight1/tiny-random-clip-tf'			,     'hf-internal-testing/tiny-random-bert'   )
								snake_case_           =				13
								snake_case_           =				floats_tensor(
								    [
								        batch_size,
								        model.vision_model.config.num_channels,
								        model.vision_model.config.image_size,
								        model.vision_model.config.image_size,
								    ]   )
								snake_case_           =				ids_tensor([batch_size, 4]			,     model.text_model.config.vocab_size   )
								snake_case_           =				random_attention_mask([batch_size, 4]   )
								snake_case_           =				{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

								return model, inputs




	def 				_UpperCamelCase     (			self			,     a			,     a   )			->       List[Any]:
								snake_case_           =				TFCLIPVisionModel(a			,     name='vision_model'   )
								snake_case_           =				TFBertModel(a			,     name='text_model'   )
								return vision_model, text_model






	def 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				TFCLIPVisionModelTester(self   )
								snake_case_           =				TFBertModelTester(self   )
								snake_case_           =				clip_model_tester.prepare_config_and_inputs()
								snake_case_           =				bert_model_tester.prepare_config_and_inputs()

								snake_case_     ,						snake_case_           =				vision_config_and_inputs

								(
								    (
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						(
								    snake_case_
								)     ,						
								)           =				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							):



	'''simple docstring'''




	@slow
	def 				_UpperCamelCase     (			self   )			->       List[str]:
								snake_case_           =				TFVisionTextDualEncoderModel.from_pretrained(
								    'clip-italian/clip-italian'			,     logit_scale_init_value=1.0			,     from_pt=a   )
								snake_case_           =				VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'   )

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

								snake_case_           =				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])			,     )

								snake_case_           =				np.array([[1.2_284_727, 0.3_104_122]]   )

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













def 		__UpperCAmelCase     (	a_ = 10**9):
							snake_case_           =				1
							snake_case_           =				2

							snake_case_           =				0
							snake_case_           =				0
							snake_case_           =				0
							while perimeter <= max_perimeter:
														perimeters_sum += perimeter

														prev_value += 2 * value
														value += prev_value

														snake_case_           =				2 * value + 2 if i % 2 == 0 else 2 * value - 2
														i += 1

							return perimeters_sum


if __name__ == "__main__":
	print(f'{solution() = }')





