



'''simple docstring'''





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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



				"""simple docstring"""





				if isinstance(_lowerCamelCase							,   collections.abc.Iterable      ):
								return x
				return (x, x)






@require_tf
class 	lowerCAmelCase_:
		'''simple docstring'''




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		str:
						pass




		def 							UpperCAmelCase_     (   self				)   ->		Tuple:
						pass




		def 							UpperCAmelCase_     (   self				)   ->		Tuple:
						pass




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,**__UpperCAmelCase				)   ->		List[Any]:
						lowerCAmelCase__			:  List[Any]											= VisionTextDualEncoderConfig.from_vision_text_configs(__UpperCAmelCase  ,__UpperCAmelCase				)

						lowerCAmelCase__			:  List[str]											= TFVisionTextDualEncoderModel(__UpperCAmelCase				)

						lowerCAmelCase__			:  int											= 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 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,**__UpperCAmelCase				)   ->		Optional[int]:
						lowerCAmelCase__			:  Union[str, Any]											= self.get_vision_text_model(__UpperCAmelCase  ,__UpperCAmelCase				)
						lowerCAmelCase__			:  Optional[Any]											= TFVisionTextDualEncoderModel(vision_model=__UpperCAmelCase  ,text_model=__UpperCAmelCase				)

						lowerCAmelCase__			:  Optional[int]											= 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 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,**__UpperCAmelCase				)   ->		Tuple:
						lowerCAmelCase__			:  Optional[Any]											= self.get_vision_text_model(__UpperCAmelCase  ,__UpperCAmelCase				)
						lowerCAmelCase__			:  List[str]											= {"""vision_model""": vision_model, """text_model""": text_model}
						lowerCAmelCase__			:  int											= TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__UpperCAmelCase				)

						lowerCAmelCase__			:  Union[str, Any]											= 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 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,**__UpperCAmelCase				)   ->		List[Any]:
						lowerCAmelCase__			:  Dict											= self.get_vision_text_model(__UpperCAmelCase  ,__UpperCAmelCase				)
						lowerCAmelCase__			:  Tuple											= TFVisionTextDualEncoderModel(vision_model=__UpperCAmelCase  ,text_model=__UpperCAmelCase				)

						lowerCAmelCase__			:  Any											= model(input_ids=__UpperCAmelCase  ,pixel_values=__UpperCAmelCase  ,attention_mask=__UpperCAmelCase				)
						lowerCAmelCase__			:  str											= output[0].numpy()

						with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(__UpperCAmelCase				)
										lowerCAmelCase__			:  str											= TFVisionTextDualEncoderModel.from_pretrained(__UpperCAmelCase				)

										lowerCAmelCase__			:  Optional[int]											= model(input_ids=__UpperCAmelCase  ,pixel_values=__UpperCAmelCase  ,attention_mask=__UpperCAmelCase				)
										lowerCAmelCase__			:  int											= after_output[0].numpy()
										lowerCAmelCase__			:  Tuple											= np.amax(np.abs(out_a - out_a				)				)
										self.assertLessEqual(__UpperCAmelCase  ,1E-5				)




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,**__UpperCAmelCase				)   ->		str:
						lowerCAmelCase__			:  Dict											= self.get_vision_text_model(__UpperCAmelCase  ,__UpperCAmelCase				)
						lowerCAmelCase__			:  List[Any]											= TFVisionTextDualEncoderModel(vision_model=__UpperCAmelCase  ,text_model=__UpperCAmelCase				)

						lowerCAmelCase__			:  Dict											= model(
						    input_ids=__UpperCAmelCase  ,pixel_values=__UpperCAmelCase  ,attention_mask=__UpperCAmelCase  ,output_attentions=__UpperCAmelCase				)

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

						lowerCAmelCase__			:  Any											= 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 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Union[str, Any]:
						lowerCAmelCase__			:  Tuple											= np.abs((a - b)				).max()
						self.assertLessEqual(__UpperCAmelCase  ,__UpperCAmelCase  ,F"""Difference between torch and flax is {diff} (>= {tol})."""				)




		def 							UpperCAmelCase_     (   self				)   ->		str:
						lowerCAmelCase__			:  Dict											= self.prepare_config_and_inputs()
						self.check_vision_text_dual_encoder_model(**__UpperCAmelCase				)




		def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
						lowerCAmelCase__			:  Tuple											= self.prepare_config_and_inputs()
						self.check_model_from_pretrained_configs(**__UpperCAmelCase				)




		def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
						lowerCAmelCase__			:  Tuple											= self.prepare_config_and_inputs()
						self.check_vision_text_dual_encoder_from_pretrained(**__UpperCAmelCase				)




		def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
						lowerCAmelCase__			:  int											= self.prepare_config_and_inputs()
						self.check_save_load(**__UpperCAmelCase				)




		def 							UpperCAmelCase_     (   self				)   ->		Tuple:
						lowerCAmelCase__			:  Optional[Any]											= self.prepare_config_and_inputs()
						self.check_vision_text_output_attention(**__UpperCAmelCase				)






		@slow
		def 							UpperCAmelCase_     (   self				)   ->		Dict:
						lowerCAmelCase__			:  List[str]											= self.get_pretrained_model_and_inputs()

						lowerCAmelCase__			:  Any											= model_a(**__UpperCAmelCase				)
						lowerCAmelCase__			:  Any											= outputs[0].numpy()

						with tempfile.TemporaryDirectory() as tmp_dirname:
										model_a.save_pretrained(__UpperCAmelCase				)
										lowerCAmelCase__			:  List[str]											= TFVisionTextDualEncoderModel.from_pretrained(__UpperCAmelCase				)

										lowerCAmelCase__			:  Optional[int]											= model_a(**__UpperCAmelCase				)
										lowerCAmelCase__			:  Union[str, Any]											= after_outputs[0].numpy()
										lowerCAmelCase__			:  Dict											= np.amax(np.abs(out_a - out_a				)				)
										self.assertLessEqual(__UpperCAmelCase  ,1E-5				)







@require_tf
class 	lowerCAmelCase_(    __lowercase						,			unittest.TestCase					):
		'''simple docstring'''




		def 							UpperCAmelCase_     (   self				)   ->		Dict:
						lowerCAmelCase__			:  Union[str, Any]											= TFVisionTextDualEncoderModel.from_vision_text_pretrained(
						    """hf-internal-testing/tiny-random-vit"""  ,"""hf-internal-testing/tiny-random-bert"""				)
						lowerCAmelCase__			:  Tuple											= 13
						lowerCAmelCase__			:  List[str]											= floats_tensor(
						    [
						        batch_size,
						        model.vision_model.config.num_channels,
						        model.vision_model.config.image_size,
						        model.vision_model.config.image_size,
						    ]				)
						lowerCAmelCase__			:  Union[str, Any]											= ids_tensor([batch_size, 4]  ,model.text_model.config.vocab_size				)
						lowerCAmelCase__			:  Optional[Any]											= random_attention_mask([batch_size, 4]				)
						lowerCAmelCase__			:  str											= {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

						return model, inputs




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		List[Any]:
						lowerCAmelCase__			:  Optional[int]											= TFViTModel(__UpperCAmelCase  ,name="""vision_model"""				)
						lowerCAmelCase__			:  int											= TFBertModel(__UpperCAmelCase  ,name="""text_model"""				)
						return vision_model, text_model






		def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
						lowerCAmelCase__			:  Any											= TFViTModelTester(self				)
						lowerCAmelCase__			:  Optional[Any]											= TFBertModelTester(self				)
						lowerCAmelCase__			:  List[str]											= vit_model_tester.prepare_config_and_inputs()
						lowerCAmelCase__			:  Tuple											= bert_model_tester.prepare_config_and_inputs()

						lowerCAmelCase__			:  Optional[Any]											= vision_config_and_inputs

						(
						    lowerCAmelCase__      
						)			:  List[str]											= 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 	lowerCAmelCase_(    __lowercase						,			unittest.TestCase					):
		'''simple docstring'''




		def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
						lowerCAmelCase__			:  Tuple											= TFVisionTextDualEncoderModel.from_vision_text_pretrained(
						    """Rocketknight1/tiny-random-deit-tf"""  ,"""hf-internal-testing/tiny-random-roberta"""				)
						lowerCAmelCase__			:  int											= 13
						lowerCAmelCase__			:  List[str]											= floats_tensor(
						    [
						        batch_size,
						        model.vision_model.config.num_channels,
						        model.vision_model.config.image_size,
						        model.vision_model.config.image_size,
						    ]				)
						lowerCAmelCase__			:  Any											= ids_tensor([batch_size, 4]  ,model.text_model.config.vocab_size				)
						lowerCAmelCase__			:  Tuple											= random_attention_mask([batch_size, 4]				)
						lowerCAmelCase__			:  Optional[Any]											= {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

						return model, inputs




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,**__UpperCAmelCase				)   ->		Optional[int]:
						lowerCAmelCase__			:  Any											= self.get_vision_text_model(__UpperCAmelCase  ,__UpperCAmelCase				)
						lowerCAmelCase__			:  int											= TFVisionTextDualEncoderModel(vision_model=__UpperCAmelCase  ,text_model=__UpperCAmelCase				)

						lowerCAmelCase__			:  Any											= model(
						    input_ids=__UpperCAmelCase  ,pixel_values=__UpperCAmelCase  ,attention_mask=__UpperCAmelCase  ,output_attentions=__UpperCAmelCase				)

						lowerCAmelCase__			:  List[str]											= output.vision_model_output.attentions
						self.assertEqual(len(__UpperCAmelCase				)  ,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)
						lowerCAmelCase__			:  Optional[int]											= to_atuple(vision_model.config.image_size				)
						lowerCAmelCase__			:  Optional[int]											= to_atuple(vision_model.config.patch_size				)
						lowerCAmelCase__			:  Union[str, Any]											= (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
						lowerCAmelCase__			:  List[str]											= num_patches + 2
						self.assertEqual(vision_attentions[0].shape[-3:]  ,(vision_config.num_attention_heads, seq_len, seq_len)				)

						lowerCAmelCase__			:  Union[str, Any]											= 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 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		List[str]:
						lowerCAmelCase__			:  int											= TFDeiTModel(__UpperCAmelCase  ,name="""vision_model"""				)
						lowerCAmelCase__			:  Optional[Any]											= TFRobertaModel(__UpperCAmelCase  ,name="""text_model"""				)
						return vision_model, text_model






		def 							UpperCAmelCase_     (   self				)   ->		Any:
						lowerCAmelCase__			:  Dict											= TFDeiTModelTester(self				)
						lowerCAmelCase__			:  Optional[int]											= TFRobertaModelTester(self				)
						lowerCAmelCase__			:  Dict											= vit_model_tester.prepare_config_and_inputs()
						lowerCAmelCase__			:  Optional[int]											= bert_model_tester.prepare_config_and_inputs()

						lowerCAmelCase__			:  int											= vision_config_and_inputs

						(
						    lowerCAmelCase__      
						)			:  Optional[int]											= 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 	lowerCAmelCase_(    __lowercase						,			unittest.TestCase					):
		'''simple docstring'''




		def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
						lowerCAmelCase__			:  Optional[Any]											= TFVisionTextDualEncoderModel.from_vision_text_pretrained(
						    """Rocketknight1/tiny-random-clip-tf"""  ,"""hf-internal-testing/tiny-random-bert"""				)
						lowerCAmelCase__			:  Optional[int]											= 13
						lowerCAmelCase__			:  List[Any]											= floats_tensor(
						    [
						        batch_size,
						        model.vision_model.config.num_channels,
						        model.vision_model.config.image_size,
						        model.vision_model.config.image_size,
						    ]				)
						lowerCAmelCase__			:  int											= ids_tensor([batch_size, 4]  ,model.text_model.config.vocab_size				)
						lowerCAmelCase__			:  Optional[int]											= random_attention_mask([batch_size, 4]				)
						lowerCAmelCase__			:  Dict											= {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

						return model, inputs




		def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Any:
						lowerCAmelCase__			:  Tuple											= TFCLIPVisionModel(__UpperCAmelCase  ,name="""vision_model"""				)
						lowerCAmelCase__			:  str											= TFBertModel(__UpperCAmelCase  ,name="""text_model"""				)
						return vision_model, text_model






		def 							UpperCAmelCase_     (   self				)   ->		Dict:
						lowerCAmelCase__			:  List[Any]											= TFCLIPVisionModelTester(self				)
						lowerCAmelCase__			:  Dict											= TFBertModelTester(self				)
						lowerCAmelCase__			:  str											= clip_model_tester.prepare_config_and_inputs()
						lowerCAmelCase__			:  Tuple											= bert_model_tester.prepare_config_and_inputs()

						lowerCAmelCase__			:  Optional[int]											= vision_config_and_inputs

						(
						    lowerCAmelCase__      
						)			:  List[Any]											= 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 	lowerCAmelCase_(    unittest.TestCase					):
		'''simple docstring'''




		@slow
		def 							UpperCAmelCase_     (   self				)   ->		Any:
						lowerCAmelCase__			:  int											= TFVisionTextDualEncoderModel.from_pretrained(
						    """clip-italian/clip-italian"""  ,logit_scale_init_value=1.0  ,from_pt=__UpperCAmelCase				)
						lowerCAmelCase__			:  Optional[int]											= VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""				)

						lowerCAmelCase__			:  List[Any]											= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
						lowerCAmelCase__			:  Optional[int]											= processor(
						    text=["""una foto di un gatto""", """una foto di un cane"""]  ,images=__UpperCAmelCase  ,padding=__UpperCAmelCase  ,return_tensors="""np"""				)

						lowerCAmelCase__			:  str											= 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__			:  Optional[Any]											= 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.numpy()  ,__UpperCAmelCase  ,atol=1E-3				)				)







'''simple docstring'''





_lowerCAmelCase										=     '''Input must be a string of 8 numbers plus letter'''
_lowerCAmelCase										=     '''TRWAGMYFPDXBNJZSQVHLCKE'''
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    if not isinstance(UpperCamelCase							,   UpperCamelCase      ):
        lowerCAmelCase__			:  List[Any]											= f"""Expected string as input, found {type(UpperCamelCase      ).__name__}"""
        raise TypeError(UpperCamelCase      )

    lowerCAmelCase__			:  Union[str, Any]											= spanish_id.replace("""-"""							,   """"""      ).upper()
    if len(UpperCamelCase      ) != 9:
        raise ValueError(UpperCamelCase      )

    try:
        lowerCAmelCase__			:  Optional[int]											= int(spanish_id_clean[0:8]      )
        lowerCAmelCase__			:  int											= spanish_id_clean[8]
    except ValueError as ex:
        raise ValueError(UpperCamelCase      ) from ex

    if letter.isdigit():
        raise ValueError(UpperCamelCase      )

    return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
 import doctest

 doctest.testmod()


