





"""simple docstring"""




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			)  ->					Dict:
				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 .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels





