


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 							__lowercase       (			__lowerCAmelCase							:    Optional[int]       ) ->    Tuple:
							if isinstance(__lowerCAmelCase ,   collections.abc.Iterable       ):
														return x
							return (x, x)







@require_tf
class 	snake_case_       :
					def 			lowerCamelCase__(				self       :str      ,__snake_case       :Union[str, Any]      ,__snake_case       :List[Any]				) ->   Optional[int]:
												pass
					def 			lowerCamelCase__(				self       :List[str]				) ->   str:
												pass
					def 			lowerCamelCase__(				self       :Dict				) ->   Dict:
												pass
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :int      ,__snake_case       :Optional[Any]      ,__snake_case       :Optional[Any]      ,__snake_case       :Optional[Any]      ,__snake_case       :int=None      ,**__snake_case       :Optional[int]				) ->   Any:
												a__					=					VisionTextDualEncoderConfig.from_vision_text_configs(__snake_case      ,__snake_case				)

												a__					=					TFVisionTextDualEncoderModel(__snake_case				)

												a__					=					model(input_ids=__snake_case      ,pixel_values=__snake_case      ,attention_mask=__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       :Optional[int]      ,__snake_case       :List[str]      ,__snake_case       :Any      ,__snake_case       :Any      ,__snake_case       :Union[str, Any]      ,__snake_case       :Dict=None      ,**__snake_case       :Any				) ->   Optional[int]:
												a__				, a__					=					self.get_vision_text_model(__snake_case      ,__snake_case				)
												a__					=					TFVisionTextDualEncoderModel(vision_model=__snake_case      ,text_model=__snake_case				)

												a__					=					model(input_ids=__snake_case      ,pixel_values=__snake_case      ,attention_mask=__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      ,__snake_case       :Union[str, Any]      ,__snake_case       :Optional[Any]      ,__snake_case       :Tuple      ,__snake_case       :str      ,__snake_case       :Optional[Any]=None      ,**__snake_case       :Optional[int]				) ->   Any:
												a__				, a__					=					self.get_vision_text_model(__snake_case      ,__snake_case				)
												a__					=					{'vision_model': vision_model, 'text_model': text_model}
												a__					=					TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__snake_case				)

												a__					=					model(input_ids=__snake_case      ,pixel_values=__snake_case      ,attention_mask=__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       :Optional[int]      ,__snake_case       :List[Any]      ,__snake_case       :Tuple      ,__snake_case       :List[Any]      ,__snake_case       :int      ,__snake_case       :Union[str, Any]=None      ,**__snake_case       :Dict				) ->   Union[str, Any]:
												a__				, a__					=					self.get_vision_text_model(__snake_case      ,__snake_case				)
												a__					=					TFVisionTextDualEncoderModel(vision_model=__snake_case      ,text_model=__snake_case				)

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

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

																			a__					=					model(input_ids=__snake_case      ,pixel_values=__snake_case      ,attention_mask=__snake_case				)
																			a__					=					after_output[0].numpy()
																			a__					=					np.amax(np.abs(out_a - out_a				)				)
																			self.assertLessEqual(__snake_case      ,1E-5				)
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :Optional[int]      ,__snake_case       :Optional[Any]      ,__snake_case       :Dict      ,__snake_case       :Optional[Any]      ,__snake_case       :Tuple=None      ,**__snake_case       :Optional[int]				) ->   List[Any]:
												a__				, a__					=					self.get_vision_text_model(__snake_case      ,__snake_case				)
												a__					=					TFVisionTextDualEncoderModel(vision_model=__snake_case      ,text_model=__snake_case				)

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

												a__					=					output.vision_model_output.attentions
												self.assertEqual(len(__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(__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       :List[str]      ,__snake_case       :np.ndarray      ,__snake_case       :np.ndarray      ,__snake_case       :float				) ->   int:
												a__					=					np.abs((a - b)				).max()
												self.assertLessEqual(__snake_case      ,__snake_case      ,F'Difference between torch and flax is {diff} (>= {tol}).'				)
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												a__					=					self.prepare_config_and_inputs()
												self.check_vision_text_dual_encoder_model(**__snake_case				)
					def 			lowerCamelCase__(				self       :str				) ->   List[Any]:
												a__					=					self.prepare_config_and_inputs()
												self.check_model_from_pretrained_configs(**__snake_case				)
					def 			lowerCamelCase__(				self       :List[str]				) ->   Any:
												a__					=					self.prepare_config_and_inputs()
												self.check_vision_text_dual_encoder_from_pretrained(**__snake_case				)
					def 			lowerCamelCase__(				self       :int				) ->   str:
												a__					=					self.prepare_config_and_inputs()
												self.check_save_load(**__snake_case				)
					def 			lowerCamelCase__(				self       :List[str]				) ->   int:
												a__					=					self.prepare_config_and_inputs()
												self.check_vision_text_output_attention(**__snake_case				)



					@slow
					def 			lowerCamelCase__(				self       :List[str]				) ->   str:
												a__				, a__					=					self.get_pretrained_model_and_inputs()

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

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

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







@require_tf
class 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					def 			lowerCamelCase__(				self       :Tuple				) ->   Union[str, Any]:
												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       :Dict      ,__snake_case       :List[Any]      ,__snake_case       :Tuple				) ->   Tuple:
												a__					=					TFViTModel(__snake_case      ,name='vision_model'				)
												a__					=					TFBertModel(__snake_case      ,name='text_model'				)
												return vision_model, text_model



					def 			lowerCamelCase__(				self       :str				) ->   Tuple:
												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 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					def 			lowerCamelCase__(				self       :int				) ->   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.
												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[str]      ,__snake_case       :Optional[Any]      ,__snake_case       :Optional[int]      ,__snake_case       :int      ,__snake_case       :List[Any]      ,__snake_case       :Dict=None      ,**__snake_case       :Optional[int]				) ->   Union[str, Any]:
												a__				, a__					=					self.get_vision_text_model(__snake_case      ,__snake_case				)
												a__					=					TFVisionTextDualEncoderModel(vision_model=__snake_case      ,text_model=__snake_case				)

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

												a__					=					output.vision_model_output.attentions
												self.assertEqual(len(__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(__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       :Optional[Any]      ,__snake_case       :Tuple      ,__snake_case       :List[str]				) ->   Any:
												a__					=					TFDeiTModel(__snake_case      ,name='vision_model'				)
												a__					=					TFRobertaModel(__snake_case      ,name='text_model'				)
												return vision_model, text_model



					def 			lowerCamelCase__(				self       :Dict				) ->   Dict:
												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 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Optional[int]:
												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       :int      ,__snake_case       :Optional[int]      ,__snake_case       :str				) ->   Optional[int]:
												a__					=					TFCLIPVisionModel(__snake_case      ,name='vision_model'				)
												a__					=					TFBertModel(__snake_case      ,name='text_model'				)
												return vision_model, text_model



					def 			lowerCamelCase__(				self       :Dict				) ->   str:
												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 	snake_case_       (unittest.TestCase				):
					@slow
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[str]:
												a__					=					TFVisionTextDualEncoderModel.from_pretrained(
												    'clip-italian/clip-italian'      ,logit_scale_init_value=1.0      ,from_pt=__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=__snake_case      ,padding=__snake_case      ,return_tensors='np'				)

												a__					=					model(**__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()      ,__snake_case      ,atol=1E-3				)				)








from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




