






'''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 	__magic_name__( _A					):
						'''simple docstring'''





						if isinstance(_A						,      collections.abc.Iterable					):
												return x
						return (x, x)
@require_flax
class    _SCREAMING_SNAKE_CASE							:






			def 				A      (   self							:						Tuple ,      lowercase							:						Optional[int] ,      lowercase							:						Any			)							->    str:

									'''simple docstring'''







									pass






			def 				A      (   self							:						str			)							->    Dict:

									'''simple docstring'''







									pass






			def 				A      (   self							:						str			)							->    List[Any]:

									'''simple docstring'''







									pass






			def 				A      (   self							:						Optional[int] ,      lowercase							:						np.ndarray ,      lowercase							:						np.ndarray ,      lowercase							:						float			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = np.abs((a - b)			).max()
									self.assertLessEqual(lowercase ,      lowercase ,      f"Difference between torch and flax is {diff} (>= {tol})."			)






			def 				A      (   self							:						str ,      lowercase							:						Optional[Any] ,      lowercase							:						int ,      lowercase							:						Optional[int] ,      lowercase							:						Optional[Any] ,      lowercase							:						List[str]=None ,      **lowercase							:						List[str]			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase ,      lowercase			)

									UpperCamelCase__         = FlaxVisionTextDualEncoderModel(lowercase			)

									UpperCamelCase__         = model(input_ids=lowercase ,      pixel_values=lowercase ,      attention_mask=lowercase			)

									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 				A      (   self							:						Optional[int] ,      lowercase							:						List[str] ,      lowercase							:						Dict ,      lowercase							:						List[str] ,      lowercase							:						Optional[Any] ,      lowercase							:						List[str]=None ,      **lowercase							:						Tuple			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__    ,   UpperCamelCase__         = self.get_vision_text_model(lowercase ,      lowercase			)
									UpperCamelCase__         = {"""vision_model""": vision_model, """text_model""": text_model}
									UpperCamelCase__         = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase			)

									UpperCamelCase__         = model(input_ids=lowercase ,      pixel_values=lowercase ,      attention_mask=lowercase			)

									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 				A      (   self							:						List[Any] ,      lowercase							:						Tuple ,      lowercase							:						List[Any] ,      lowercase							:						Optional[int] ,      lowercase							:						List[str] ,      lowercase							:						Optional[int]=None ,      **lowercase							:						Optional[int]			)							->    int:

									'''simple docstring'''







									UpperCamelCase__    ,   UpperCamelCase__         = self.get_vision_text_model(lowercase ,      lowercase			)
									UpperCamelCase__         = {"""vision_model""": vision_model, """text_model""": text_model}
									UpperCamelCase__         = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase			)

									UpperCamelCase__         = model(input_ids=lowercase ,      pixel_values=lowercase ,      attention_mask=lowercase			)
									UpperCamelCase__         = output[0]

									with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(lowercase			)
															UpperCamelCase__         = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase			)

															UpperCamelCase__         = model(input_ids=lowercase ,      pixel_values=lowercase ,      attention_mask=lowercase			)
															UpperCamelCase__         = after_output[0]
															UpperCamelCase__         = np.amax(np.abs(out_a - out_a			)			)
															self.assertLessEqual(lowercase ,      1e-3			)






			def 				A      (   self							:						List[str] ,      lowercase							:						List[str] ,      lowercase							:						Optional[int] ,      lowercase							:						Any ,      lowercase							:						Tuple ,      lowercase							:						Tuple=None ,      **lowercase							:						Any			)							->    Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__    ,   UpperCamelCase__         = self.get_vision_text_model(lowercase ,      lowercase			)
									UpperCamelCase__         = {"""vision_model""": vision_model, """text_model""": text_model}
									UpperCamelCase__         = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowercase			)

									UpperCamelCase__         = model(
									    input_ids=lowercase ,      pixel_values=lowercase ,      attention_mask=lowercase ,      output_attentions=lowercase			)

									UpperCamelCase__         = output.vision_model_output.attentions
									self.assertEqual(len(lowercase			) ,      vision_config.num_hidden_layers			)

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

									UpperCamelCase__         = output.text_model_output.attentions
									self.assertEqual(len(lowercase			) ,      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 				A      (   self							:						Tuple ,      lowercase							:						str ,      lowercase							:						Union[str, Any] ,      lowercase							:						Dict			)							->    Optional[int]:

									'''simple docstring'''







									pt_model.to(lowercase			)
									pt_model.eval()

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

									with torch.no_grad():
															UpperCamelCase__         = pt_model(**lowercase			).to_tuple()

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

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

									UpperCamelCase__         = fx_model_loaded(**lowercase			).to_tuple()
									self.assertEqual(len(lowercase			) ,      len(lowercase			) ,      """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(lowercase ,      pt_output.numpy() ,      4e-2			)

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

									pt_model_loaded.to(lowercase			)
									pt_model_loaded.eval()

									with torch.no_grad():
															UpperCamelCase__         = pt_model_loaded(**lowercase			).to_tuple()

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






			def 				A      (   self							:						int ,      lowercase							:						str ,      lowercase							:						Tuple ,      lowercase							:						Optional[int]			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase ,      lowercase			)

									UpperCamelCase__         = VisionTextDualEncoderModel(lowercase			)
									UpperCamelCase__         = FlaxVisionTextDualEncoderModel(lowercase			)

									UpperCamelCase__         = convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,      lowercase			)
									UpperCamelCase__         = fx_state

									self.check_pt_flax_equivalence(lowercase ,      lowercase ,      lowercase			)






			def 				A      (   self							:						int ,      lowercase							:						Tuple ,      lowercase							:						str ,      lowercase							:						Any			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = VisionTextDualEncoderConfig.from_vision_text_configs(lowercase ,      lowercase			)

									UpperCamelCase__         = VisionTextDualEncoderModel(lowercase			)
									UpperCamelCase__         = FlaxVisionTextDualEncoderModel(lowercase			)

									UpperCamelCase__         = load_flax_weights_in_pytorch_model(lowercase ,      fx_model.params			)

									self.check_pt_flax_equivalence(lowercase ,      lowercase ,      lowercase			)






			def 				A      (   self							:						str			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = self.prepare_config_and_inputs()
									self.check_model_from_pretrained_configs(**lowercase			)






			def 				A      (   self							:						Any			)							->    Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__         = self.prepare_config_and_inputs()
									self.check_vision_text_dual_encoder_from_pretrained(**lowercase			)






			def 				A      (   self							:						Union[str, Any]			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = self.prepare_config_and_inputs()
									self.check_save_load(**lowercase			)






			def 				A      (   self							:						Union[str, Any]			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = self.prepare_config_and_inputs()
									self.check_vision_text_output_attention(**lowercase			)






			@is_pt_flax_cross_test
			def 				A      (   self							:						Optional[int]			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = self.prepare_config_and_inputs()
									UpperCamelCase__         = config_inputs_dict.pop("""vision_config"""			)
									UpperCamelCase__         = config_inputs_dict.pop("""text_config"""			)

									UpperCamelCase__         = config_inputs_dict

									self.check_equivalence_pt_to_flax(lowercase ,      lowercase ,      lowercase			)
									self.check_equivalence_flax_to_pt(lowercase ,      lowercase ,      lowercase			)







			@slow
			def 				A      (   self							:						List[Any]			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__    ,   UpperCamelCase__         = self.get_pretrained_model_and_inputs()

									UpperCamelCase__         = model_a(**lowercase			)
									UpperCamelCase__         = outputs[0]

									with tempfile.TemporaryDirectory() as tmp_dirname:
															model_a.save_pretrained(lowercase			)
															UpperCamelCase__         = FlaxVisionTextDualEncoderModel.from_pretrained(lowercase			)

															UpperCamelCase__         = model_a(**lowercase			)
															UpperCamelCase__         = after_outputs[0]
															UpperCamelCase__         = np.amax(np.abs(out_a - out_a			)			)
															self.assertLessEqual(lowercase ,      1e-5			)
@require_flax
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE       ,unittest.TestCase	):






			def 				A      (   self							:						List[str]			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
									    """hf-internal-testing/tiny-random-vit""" ,      """hf-internal-testing/tiny-bert""" ,      vision_from_pt=lowercase ,      text_from_pt=lowercase ,      )
									UpperCamelCase__         = 1_3
									UpperCamelCase__         = floats_tensor(
									    [
									        batch_size,
									        model.config.vision_config.num_channels,
									        model.config.vision_config.image_size,
									        model.config.vision_config.image_size,
									    ]			)
									UpperCamelCase__         = ids_tensor([batch_size, 4] ,      model.config.text_config.vocab_size			)
									UpperCamelCase__         = random_attention_mask([batch_size, 4]			)
									UpperCamelCase__         = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

									return model, inputs






			def 				A      (   self							:						int ,      lowercase							:						Tuple ,      lowercase							:						Any			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = FlaxViTModel(lowercase			)
									UpperCamelCase__         = FlaxBertModel(lowercase			)
									return vision_model, text_model







			def 				A      (   self							:						int			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = FlaxViTModelTester(self			)
									UpperCamelCase__         = FlaxBertModelTester(self			)
									UpperCamelCase__         = vit_model_tester.prepare_config_and_inputs()
									UpperCamelCase__         = bert_model_tester.prepare_config_and_inputs()

									UpperCamelCase__    ,   UpperCamelCase__         = vision_config_and_inputs

									UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__         = 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    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE       ,unittest.TestCase	):






			def 				A      (   self							:						str			)							->    Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__         = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
									    """hf-internal-testing/tiny-random-clip""" ,      """hf-internal-testing/tiny-bert""" ,      vision_from_pt=lowercase ,      text_from_pt=lowercase ,      )
									UpperCamelCase__         = 1_3
									UpperCamelCase__         = floats_tensor(
									    [
									        batch_size,
									        model.config.vision_config.num_channels,
									        model.config.vision_config.image_size,
									        model.config.vision_config.image_size,
									    ]			)
									UpperCamelCase__         = ids_tensor([batch_size, 4] ,      model.config.text_config.vocab_size			)
									UpperCamelCase__         = random_attention_mask([batch_size, 4]			)
									UpperCamelCase__         = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

									return model, inputs






			def 				A      (   self							:						str ,      lowercase							:						Tuple ,      lowercase							:						int			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = FlaxCLIPVisionModel(lowercase			)
									UpperCamelCase__         = FlaxBertModel(lowercase			)
									return vision_model, text_model







			def 				A      (   self							:						str			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = FlaxCLIPVisionModelTester(self			)
									UpperCamelCase__         = FlaxBertModelTester(self			)
									UpperCamelCase__         = clip_model_tester.prepare_config_and_inputs()
									UpperCamelCase__         = bert_model_tester.prepare_config_and_inputs()

									UpperCamelCase__    ,   UpperCamelCase__         = vision_config_and_inputs

									UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__         = 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    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):






			@slow
			def 				A      (   self							:						List[Any]			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" ,      logit_scale_init_value=1.0			)
									UpperCamelCase__         = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""			)

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

									UpperCamelCase__         = model(**lowercase			)

									# 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]) ,      )

									UpperCamelCase__         = 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 ,      lowercase ,      atol=1e-3			)			)








'''simple docstring'''


import numpy as np

from transformers import Pipeline







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = np.max(_A						,      axis=-1						,      keepdims=_A					)
						UpperCamelCase__         = np.exp(outputs - maxes					)
						return shifted_exp / shifted_exp.sum(axis=-1						,      keepdims=_A					)







class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def 				A      (   self							:						int ,      **lowercase							:						List[Any]			)							->    List[str]:

									'''simple docstring'''







									UpperCamelCase__         = {}
									if "second_text" in kwargs:
															UpperCamelCase__         = kwargs["""second_text"""]
									return preprocess_kwargs, {}, {}






			def 				A      (   self							:						Optional[int] ,      lowercase							:						Union[str, Any] ,      lowercase							:						Any=None			)							->    Optional[int]:

									'''simple docstring'''







									return self.tokenizer(lowercase ,      text_pair=lowercase ,      return_tensors=self.framework			)






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[int]			)							->    Any:

									'''simple docstring'''







									return self.model(**lowercase			)







			def 				A      (   self							:						List[str] ,      lowercase							:						Any			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = model_outputs.logits[0].numpy()
									UpperCamelCase__         = softmax(lowercase			)

									UpperCamelCase__         = np.argmax(lowercase			)
									UpperCamelCase__         = self.model.config.idalabel[best_class]
									UpperCamelCase__         = probabilities[best_class].item()
									UpperCamelCase__         = logits.tolist()
									return {"label": label, "score": score, "logits": logits}
