





import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
							from transformers import (
							    FlaxBertModel,
							    FlaxCLIPVisionModel,
							    FlaxVisionTextDualEncoderModel,
							    FlaxViTModel,
							    VisionTextDualEncoderConfig,
							    VisionTextDualEncoderProcessor,
							)
							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)


if is_torch_available():
							import torch

							from transformers import VisionTextDualEncoderModel

if is_vision_available():
							from PIL import Image





def      _lowercase					(						_UpperCAmelCase				)						->  Optional[Any]:
				if isinstance(_UpperCAmelCase			,      collections.abc.Iterable				):
								return x
				return (x, x)

@require_flax
class       __A     :


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								pass


				def _snake_case       (		self							):
								pass


				def _snake_case       (		self							):
								pass


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =np.abs((a - b)							).max()
								self.assertLessEqual(UpperCAmelCase_   , UpperCAmelCase_   , f"""Difference between torch and flax is {diff} (>= {tol})."""							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=None   , **UpperCAmelCase_							):
								lowerCamelCase	 =VisionTextDualEncoderConfig.from_vision_text_configs(UpperCAmelCase_   , UpperCAmelCase_							)

								lowerCamelCase	 =FlaxVisionTextDualEncoderModel(UpperCAmelCase_							)

								lowerCamelCase	 =model(input_ids=UpperCAmelCase_   , pixel_values=UpperCAmelCase_   , attention_mask=UpperCAmelCase_							)

								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 _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=None   , **UpperCAmelCase_							):
								lowerCamelCase ,  lowerCamelCase	 =self.get_vision_text_model(UpperCAmelCase_   , UpperCAmelCase_							)
								lowerCamelCase	 ={"""vision_model""": vision_model, """text_model""": text_model}
								lowerCamelCase	 =FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCAmelCase_							)

								lowerCamelCase	 =model(input_ids=UpperCAmelCase_   , pixel_values=UpperCAmelCase_   , attention_mask=UpperCAmelCase_							)

								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 _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=None   , **UpperCAmelCase_							):
								lowerCamelCase ,  lowerCamelCase	 =self.get_vision_text_model(UpperCAmelCase_   , UpperCAmelCase_							)
								lowerCamelCase	 ={"""vision_model""": vision_model, """text_model""": text_model}
								lowerCamelCase	 =FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCAmelCase_							)

								lowerCamelCase	 =model(input_ids=UpperCAmelCase_   , pixel_values=UpperCAmelCase_   , attention_mask=UpperCAmelCase_							)
								lowerCamelCase	 =output[0]

								with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(UpperCAmelCase_							)
												lowerCamelCase	 =FlaxVisionTextDualEncoderModel.from_pretrained(UpperCAmelCase_							)

												lowerCamelCase	 =model(input_ids=UpperCAmelCase_   , pixel_values=UpperCAmelCase_   , attention_mask=UpperCAmelCase_							)
												lowerCamelCase	 =after_output[0]
												lowerCamelCase	 =np.amax(np.abs(out_a - out_a							)							)
												self.assertLessEqual(UpperCAmelCase_   , 1E-3							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=None   , **UpperCAmelCase_							):
								lowerCamelCase ,  lowerCamelCase	 =self.get_vision_text_model(UpperCAmelCase_   , UpperCAmelCase_							)
								lowerCamelCase	 ={"""vision_model""": vision_model, """text_model""": text_model}
								lowerCamelCase	 =FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCAmelCase_							)

								lowerCamelCase	 =model(
								    input_ids=UpperCAmelCase_   , pixel_values=UpperCAmelCase_   , attention_mask=UpperCAmelCase_   , output_attentions=UpperCAmelCase_							)

								lowerCamelCase	 =output.vision_model_output.attentions
								self.assertEqual(len(UpperCAmelCase_							)   , vision_config.num_hidden_layers							)

								# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
								lowerCamelCase	 =to_atuple(vision_model.config.image_size							)
								lowerCamelCase	 =to_atuple(vision_model.config.patch_size							)
								lowerCamelCase	 =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								lowerCamelCase	 =num_patches + 1
								self.assertEqual(vision_attentions[0].shape[-3:]   , (vision_config.num_attention_heads, seq_len, seq_len)							)

								lowerCamelCase	 =output.text_model_output.attentions
								self.assertEqual(len(UpperCAmelCase_							)   , 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 _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								pt_model.to(UpperCAmelCase_							)
								pt_model.eval()

								# prepare inputs
								lowerCamelCase	 =inputs_dict
								lowerCamelCase	 ={k: torch.tensor(v.tolist()							) for k, v in flax_inputs.items()}

								with torch.no_grad():
												lowerCamelCase	 =pt_model(**UpperCAmelCase_							).to_tuple()

								lowerCamelCase	 =fx_model(**UpperCAmelCase_							).to_tuple()
								self.assertEqual(len(UpperCAmelCase_							)   , len(UpperCAmelCase_							)   , """Output lengths differ between Flax and PyTorch"""							)
								for fx_output, pt_output in zip(fx_outputs[:4]   , pt_outputs[:4]							):
												self.assert_almost_equals(UpperCAmelCase_   , pt_output.numpy()   , 4E-2							)

								# PT -> Flax
								with tempfile.TemporaryDirectory() as tmpdirname:
												pt_model.save_pretrained(UpperCAmelCase_							)
												lowerCamelCase	 =FlaxVisionTextDualEncoderModel.from_pretrained(UpperCAmelCase_   , from_pt=UpperCAmelCase_							)

								lowerCamelCase	 =fx_model_loaded(**UpperCAmelCase_							).to_tuple()
								self.assertEqual(len(UpperCAmelCase_							)   , len(UpperCAmelCase_							)   , """Output lengths differ between Flax and PyTorch"""							)
								for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]   , pt_outputs[:4]							):
												self.assert_almost_equals(UpperCAmelCase_   , pt_output.numpy()   , 4E-2							)

								# Flax -> PT
								with tempfile.TemporaryDirectory() as tmpdirname:
												fx_model.save_pretrained(UpperCAmelCase_							)
												lowerCamelCase	 =VisionTextDualEncoderModel.from_pretrained(UpperCAmelCase_   , from_flax=UpperCAmelCase_							)

								pt_model_loaded.to(UpperCAmelCase_							)
								pt_model_loaded.eval()

								with torch.no_grad():
												lowerCamelCase	 =pt_model_loaded(**UpperCAmelCase_							).to_tuple()

								self.assertEqual(len(UpperCAmelCase_							)   , len(UpperCAmelCase_							)   , """Output lengths differ between Flax and PyTorch"""							)
								for fx_output, pt_output_loaded in zip(fx_outputs[:4]   , pt_outputs_loaded[:4]							):
												self.assert_almost_equals(UpperCAmelCase_   , pt_output_loaded.numpy()   , 4E-2							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =VisionTextDualEncoderConfig.from_vision_text_configs(UpperCAmelCase_   , UpperCAmelCase_							)

								lowerCamelCase	 =VisionTextDualEncoderModel(UpperCAmelCase_							)
								lowerCamelCase	 =FlaxVisionTextDualEncoderModel(UpperCAmelCase_							)

								lowerCamelCase	 =convert_pytorch_state_dict_to_flax(pt_model.state_dict()   , UpperCAmelCase_							)
								lowerCamelCase	 =fx_state

								self.check_pt_flax_equivalence(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =VisionTextDualEncoderConfig.from_vision_text_configs(UpperCAmelCase_   , UpperCAmelCase_							)

								lowerCamelCase	 =VisionTextDualEncoderModel(UpperCAmelCase_							)
								lowerCamelCase	 =FlaxVisionTextDualEncoderModel(UpperCAmelCase_							)

								lowerCamelCase	 =load_flax_weights_in_pytorch_model(UpperCAmelCase_   , fx_model.params							)

								self.check_pt_flax_equivalence(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.prepare_config_and_inputs()
								self.check_model_from_pretrained_configs(**UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.prepare_config_and_inputs()
								self.check_vision_text_dual_encoder_from_pretrained(**UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.prepare_config_and_inputs()
								self.check_save_load(**UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.prepare_config_and_inputs()
								self.check_vision_text_output_attention(**UpperCAmelCase_							)


				@is_pt_flax_cross_test
				def _snake_case       (		self							):
								lowerCamelCase	 =self.prepare_config_and_inputs()
								lowerCamelCase	 =config_inputs_dict.pop("""vision_config"""							)
								lowerCamelCase	 =config_inputs_dict.pop("""text_config"""							)

								lowerCamelCase	 =config_inputs_dict

								self.check_equivalence_pt_to_flax(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)
								self.check_equivalence_flax_to_pt(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				@slow
				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.get_pretrained_model_and_inputs()

								lowerCamelCase	 =model_a(**UpperCAmelCase_							)
								lowerCamelCase	 =outputs[0]

								with tempfile.TemporaryDirectory() as tmp_dirname:
												model_a.save_pretrained(UpperCAmelCase_							)
												lowerCamelCase	 =FlaxVisionTextDualEncoderModel.from_pretrained(UpperCAmelCase_							)

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




@require_flax
class       __A     (						a    ,					unittest.TestCase    ):


				def _snake_case       (		self							):
								lowerCamelCase	 =FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
								    """hf-internal-testing/tiny-random-vit"""   , """hf-internal-testing/tiny-bert"""   , vision_from_pt=UpperCAmelCase_   , text_from_pt=UpperCAmelCase_   , )
								lowerCamelCase	 =13
								lowerCamelCase	 =floats_tensor(
								    [
								        batch_size,
								        model.config.vision_config.num_channels,
								        model.config.vision_config.image_size,
								        model.config.vision_config.image_size,
								    ]							)
								lowerCamelCase	 =ids_tensor([batch_size, 4]   , model.config.text_config.vocab_size							)
								lowerCamelCase	 =random_attention_mask([batch_size, 4]							)
								lowerCamelCase	 ={"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

								return model, inputs


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =FlaxViTModel(UpperCAmelCase_							)
								lowerCamelCase	 =FlaxBertModel(UpperCAmelCase_							)
								return vision_model, text_model


				def _snake_case       (		self							):
								lowerCamelCase	 =FlaxViTModelTester(self							)
								lowerCamelCase	 =FlaxBertModelTester(self							)
								lowerCamelCase	 =vit_model_tester.prepare_config_and_inputs()
								lowerCamelCase	 =bert_model_tester.prepare_config_and_inputs()

								lowerCamelCase ,  lowerCamelCase	 =vision_config_and_inputs

								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =text_config_and_inputs

								# make sure that cross attention layers are added
								return {
								    "text_config": text_config,
								    "vision_config": vision_config,
								    "pixel_values": pixel_values,
								    "attention_mask": attention_mask,
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								}




@require_torch
class       __A     (						a    ,					unittest.TestCase    ):


				def _snake_case       (		self							):
								lowerCamelCase	 =FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
								    """hf-internal-testing/tiny-random-clip"""   , """hf-internal-testing/tiny-bert"""   , vision_from_pt=UpperCAmelCase_   , text_from_pt=UpperCAmelCase_   , )
								lowerCamelCase	 =13
								lowerCamelCase	 =floats_tensor(
								    [
								        batch_size,
								        model.config.vision_config.num_channels,
								        model.config.vision_config.image_size,
								        model.config.vision_config.image_size,
								    ]							)
								lowerCamelCase	 =ids_tensor([batch_size, 4]   , model.config.text_config.vocab_size							)
								lowerCamelCase	 =random_attention_mask([batch_size, 4]							)
								lowerCamelCase	 ={"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

								return model, inputs


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =FlaxCLIPVisionModel(UpperCAmelCase_							)
								lowerCamelCase	 =FlaxBertModel(UpperCAmelCase_							)
								return vision_model, text_model


				def _snake_case       (		self							):
								lowerCamelCase	 =FlaxCLIPVisionModelTester(self							)
								lowerCamelCase	 =FlaxBertModelTester(self							)
								lowerCamelCase	 =clip_model_tester.prepare_config_and_inputs()
								lowerCamelCase	 =bert_model_tester.prepare_config_and_inputs()

								lowerCamelCase ,  lowerCamelCase	 =vision_config_and_inputs

								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =text_config_and_inputs

								# make sure that cross attention layers are added
								return {
								    "text_config": text_config,
								    "vision_config": vision_config,
								    "pixel_values": pixel_values,
								    "attention_mask": attention_mask,
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								}




@require_flax
@require_vision
class       __A     (						unittest.TestCase    ):


				@slow
				def _snake_case       (		self							):
								lowerCamelCase	 =FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian"""   , logit_scale_init_value=1.0							)
								lowerCamelCase	 =VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""							)

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

								lowerCamelCase	 =model(**UpperCAmelCase_							)

								# 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])   , )

								lowerCamelCase	 =np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]]							)

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












from typing import List

from .keymap import KEYMAP, get_character





def      _lowercase					(						_UpperCAmelCase				)						->  Tuple:

				def decorator(_UpperCAmelCase				):
								lowerCamelCase	 =getattr(_UpperCAmelCase			,      """handle_key"""			,      []				)
								handle += [key]
								setattr(_UpperCAmelCase			,      """handle_key"""			,      _UpperCAmelCase				)
								return func

				return decorator





def      _lowercase					(						*_UpperCAmelCase				)						->  Tuple:

				def decorator(_UpperCAmelCase				):
								lowerCamelCase	 =getattr(_UpperCAmelCase			,      """handle_key"""			,      []				)
								handle += keys
								setattr(_UpperCAmelCase			,      """handle_key"""			,      _UpperCAmelCase				)
								return func

				return decorator

class       __A     (						a    ):


				def __new__(		cls   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =super().__new__(cls   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)
								if not hasattr(UpperCAmelCase_   , """key_handler"""							):
												setattr(UpperCAmelCase_   , """key_handler"""   , {}							)
								setattr(UpperCAmelCase_   , """handle_input"""   , KeyHandler.handle_input							)

								for value in attrs.values():
												lowerCamelCase	 =getattr(UpperCAmelCase_   , """handle_key"""   , []							)
												for key in handled_keys:
																lowerCamelCase	 =value
								return new_cls


				@staticmethod
				def _snake_case       (		cls							):
								lowerCamelCase	 =get_character()
								if char != KEYMAP["undefined"]:
												lowerCamelCase	 =ord(UpperCAmelCase_							)
								lowerCamelCase	 =cls.key_handler.get(UpperCAmelCase_							)
								if handler:
												lowerCamelCase	 =char
												return handler(cls							)
								else:
												return None





def      _lowercase					(						cls				)						->  List[str]:
				return KeyHandler(cls.__name__			,      cls.__bases__			,      cls.__dict__.copy()				)





