"""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      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Any:
							if isinstance(__SCREAMING_SNAKE_CASE     ,	collections.abc.Iterable  ):
														return x
							return (x, x)




@require_flax
class  _snake_case		:







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Tuple      ,    _A							:							str):



											"""simple docstring"""







											pass





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											pass





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											pass





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							np.ndarray      ,    _A							:							np.ndarray      ,    _A							:							float):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    np.abs((a - b)).max()
											self.assertLessEqual(_A      ,    _A      ,    f"""Difference between torch and flax is {diff} (>= {tol}).""")





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							List[str]      ,    _A							:							Dict      ,    _A							:							str      ,    _A							:							List[Any]      ,    _A							:							Dict=None      ,    **_A							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    VisionTextDualEncoderConfig.from_vision_text_configs(_A      ,    _A)

											_SCREAMING_SNAKE_CASE		:						str			  =    FlaxVisionTextDualEncoderModel(_A)

											_SCREAMING_SNAKE_CASE		:						str			  =    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 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							List[Any]      ,    _A							:							str      ,    _A							:							Optional[Any]      ,    _A							:							Dict      ,    _A							:							Optional[Any]=None      ,    **_A							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.get_vision_text_model(_A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    {"""vision_model""": vision_model, """text_model""": text_model}
											_SCREAMING_SNAKE_CASE		:						str			  =    FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    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 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Tuple      ,    _A							:							List[Any]      ,    _A							:							Dict      ,    _A							:							List[str]      ,    _A							:							int=None      ,    **_A							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    self.get_vision_text_model(_A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						int			  =    {"""vision_model""": vision_model, """text_model""": text_model}
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_A)

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    model(input_ids=_A      ,    pixel_values=_A      ,    attention_mask=_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    output[0]

											with tempfile.TemporaryDirectory() as tmpdirname:
																		model.save_pretrained(_A)
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    FlaxVisionTextDualEncoderModel.from_pretrained(_A)

																		_SCREAMING_SNAKE_CASE		:						str			  =    model(input_ids=_A      ,    pixel_values=_A      ,    attention_mask=_A)
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    after_output[0]
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    np.amax(np.abs(out_a - out_a))
																		self.assertLessEqual(_A      ,    1e-3)





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							Union[str, Any]      ,    _A							:							Tuple      ,    _A							:							Optional[Any]      ,    _A							:							Dict      ,    _A							:							List[str]=None      ,    **_A							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.get_vision_text_model(_A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						str			  =    {"""vision_model""": vision_model, """text_model""": text_model}
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_A)

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    model(
											    input_ids=_A      ,    pixel_values=_A      ,    attention_mask=_A      ,    output_attentions=_A)

											_SCREAMING_SNAKE_CASE		:						Dict			  =    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)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    to_atuple(vision_model.config.image_size)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    to_atuple(vision_model.config.patch_size)
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    num_patches + 1
											self.assertEqual(vision_attentions[0].shape[-3:]      ,    (vision_config.num_attention_heads, seq_len, seq_len))

											_SCREAMING_SNAKE_CASE		:						Dict			  =    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 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							List[str]      ,    _A							:							Optional[Any]      ,    _A							:							Tuple):



											"""simple docstring"""







											pt_model.to(_A)
											pt_model.eval()

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

											with torch.no_grad():
																		_SCREAMING_SNAKE_CASE		:						Any			  =    pt_model(**_A).to_tuple()

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

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

											_SCREAMING_SNAKE_CASE		:						Dict			  =    fx_model_loaded(**_A).to_tuple()
											self.assertEqual(len(_A)      ,    len(_A)      ,    """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(_A      ,    pt_output.numpy()      ,    4e-2)

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

											pt_model_loaded.to(_A)
											pt_model_loaded.eval()

											with torch.no_grad():
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    pt_model_loaded(**_A).to_tuple()

											self.assertEqual(len(_A)      ,    len(_A)      ,    """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(_A      ,    pt_output_loaded.numpy()      ,    4e-2)





				def 		_lowerCAmelCase		(      self							:							List[Any]      ,    _A							:							int      ,    _A							:							List[Any]      ,    _A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    VisionTextDualEncoderConfig.from_vision_text_configs(_A      ,    _A)

											_SCREAMING_SNAKE_CASE		:						Any			  =    VisionTextDualEncoderModel(_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    FlaxVisionTextDualEncoderModel(_A)

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,    _A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    fx_state

											self.check_pt_flax_equivalence(_A      ,    _A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							Optional[Any]      ,    _A							:							Dict      ,    _A							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    VisionTextDualEncoderConfig.from_vision_text_configs(_A      ,    _A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    VisionTextDualEncoderModel(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    FlaxVisionTextDualEncoderModel(_A)

											_SCREAMING_SNAKE_CASE		:						Any			  =    load_flax_weights_in_pytorch_model(_A      ,    fx_model.params)

											self.check_pt_flax_equivalence(_A      ,    _A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.prepare_config_and_inputs()
											self.check_model_from_pretrained_configs(**_A)





				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.prepare_config_and_inputs()
											self.check_vision_text_dual_encoder_from_pretrained(**_A)





				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.prepare_config_and_inputs()
											self.check_save_load(**_A)





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.prepare_config_and_inputs()
											self.check_vision_text_output_attention(**_A)





				@is_pt_flax_cross_test
				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    config_inputs_dict.pop("""vision_config""")
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    config_inputs_dict.pop("""text_config""")

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    config_inputs_dict

											self.check_equivalence_pt_to_flax(_A      ,    _A      ,    _A)
											self.check_equivalence_flax_to_pt(_A      ,    _A      ,    _A)





				@slow
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.get_pretrained_model_and_inputs()

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    model_a(**_A)
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    outputs[0]

											with tempfile.TemporaryDirectory() as tmp_dirname:
																		model_a.save_pretrained(_A)
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    FlaxVisionTextDualEncoderModel.from_pretrained(_A)

																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model_a(**_A)
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    after_outputs[0]
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    np.amax(np.abs(out_a - out_a))
																		self.assertLessEqual(_A      ,    1e-5)




@require_flax
class  _snake_case		(    __snake_case   ,       unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
											    """hf-internal-testing/tiny-random-vit"""      ,    """hf-internal-testing/tiny-bert"""      ,    vision_from_pt=_A      ,    text_from_pt=_A      ,    )
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    1_3
											_SCREAMING_SNAKE_CASE		:						Dict			  =    floats_tensor(
											    [
											        batch_size,
											        model.config.vision_config.num_channels,
											        model.config.vision_config.image_size,
											        model.config.vision_config.image_size,
											    ])
											_SCREAMING_SNAKE_CASE		:						int			  =    ids_tensor([batch_size, 4]      ,    model.config.text_config.vocab_size)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    random_attention_mask([batch_size, 4])
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

											return model, inputs





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							Tuple      ,    _A							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    FlaxViTModel(_A)
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    FlaxBertModel(_A)
											return vision_model, text_model





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    FlaxViTModelTester(self)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    FlaxBertModelTester(self)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    vit_model_tester.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    bert_model_tester.prepare_config_and_inputs()

											_SCREAMING_SNAKE_CASE		:						Dict			  =    vision_config_and_inputs

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    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  _snake_case		(    __snake_case   ,       unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
											    """hf-internal-testing/tiny-random-clip"""      ,    """hf-internal-testing/tiny-bert"""      ,    vision_from_pt=_A      ,    text_from_pt=_A      ,    )
											_SCREAMING_SNAKE_CASE		:						Any			  =    1_3
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    floats_tensor(
											    [
											        batch_size,
											        model.config.vision_config.num_channels,
											        model.config.vision_config.image_size,
											        model.config.vision_config.image_size,
											    ])
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    ids_tensor([batch_size, 4]      ,    model.config.text_config.vocab_size)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    random_attention_mask([batch_size, 4])
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

											return model, inputs





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							Optional[Any]      ,    _A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    FlaxCLIPVisionModel(_A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    FlaxBertModel(_A)
											return vision_model, text_model





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    FlaxCLIPVisionModelTester(self)
											_SCREAMING_SNAKE_CASE		:						int			  =    FlaxBertModelTester(self)
											_SCREAMING_SNAKE_CASE		:						Any			  =    clip_model_tester.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE		:						str			  =    bert_model_tester.prepare_config_and_inputs()

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    vision_config_and_inputs

											_SCREAMING_SNAKE_CASE		:						str			  =    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  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				@slow
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian"""      ,    logit_scale_init_value=1.0)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""")

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

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    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])      ,    )

											_SCREAMING_SNAKE_CASE		:						Dict			  =    np.array([[1.2_284_727, 0.3_104_122]])

											self.assertTrue(np.allclose(outputs.logits_per_image      ,    _A      ,    atol=1e-3))



"""simple docstring"""





import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"M-CLIP"





				def __init__(      self							:							Optional[Any]      ,    _A							:							List[str]=1_0_2_4      ,    _A							:							Union[str, Any]=7_6_8      ,    **_A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    transformerDimSize
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    imageDimSize
											super().__init__(**_A)




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						MCLIPConfig





				def __init__(      self							:							Dict      ,    _A							:							Optional[Any]      ,    *_A							:							Any      ,    **_A							:							Dict):



											"""simple docstring"""







											super().__init__(_A      ,    *_A      ,    **_A)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    XLMRobertaModel(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.nn.Linear(
											    in_features=config.transformerDimensions      ,    out_features=config.numDims)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							str      ,    _A							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.transformer(input_ids=_A      ,    attention_mask=_A)[0]
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]
											return self.LinearTransformation(_A), embs


