


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]       ):
							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				)				)








def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any]       ):
							return [
							    {
							        0: [1, 2],
							        1: [0, 2],
							        2: [0, 1, 3, 5],
							        3: [2, 4],
							        4: [3],
							        5: [2, 6, 8],
							        6: [5, 7],
							        7: [6, 8],
							        8: [5, 7],
							    },
							    {
							        0: [6],
							        1: [9],
							        2: [4, 5],
							        3: [4],
							        4: [2, 3],
							        5: [2],
							        6: [0, 7],
							        7: [6],
							        8: [],
							        9: [1],
							    },
							    {
							        0: [4],
							        1: [6],
							        2: [],
							        3: [5, 6, 7],
							        4: [0, 6],
							        5: [3, 8, 9],
							        6: [1, 3, 4, 7],
							        7: [3, 6, 8, 9],
							        8: [5, 7],
							        9: [5, 7],
							    },
							    {
							        0: [1, 3],
							        1: [0, 2, 4],
							        2: [1, 3, 4],
							        3: [0, 2, 4],
							        4: [1, 2, 3],
							    },
							][index]






def 							__lowercase       (			__lowerCAmelCase							:    dict[int, list[int]]       ):
							a__					=					0
							a__					=					len(__lowerCAmelCase       )  # No of vertices in graph
							a__					=					[0] * n
							a__					=					[False] * n

							def dfs(__lowerCAmelCase							:    int ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Tuple       ):
														a__					=					True
														a__					=					id_
														id_ += 1
														for to in graph[at]:
																					if to == parent:
																												pass
																					elif not visited[to]:
																												dfs(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   id_       )
																												a__					=					min(low[at] ,   low[to]       )
																												if id_ <= low[to]:
																																			bridges.append((at, to) if at < to else (to, at)       )
																					else:
																												# This edge is a back edge and cannot be a bridge
																												a__					=					min(low[at] ,   low[to]       )

							a__					=					[]
							for i in range(__lowerCAmelCase       ):
														if not visited[i]:
																					dfs(__lowerCAmelCase ,   -1 ,   __lowerCAmelCase ,   id_       )
							return bridges


if __name__ == "__main__":
						import doctest

						doctest.testmod()




