






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







@require_tf
class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def  lowercase		(   self:     Optional[Any]				,		__A:     Optional[int]				,		__A:     Any    ):

												'''simple docstring'''

												pass




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												pass




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												pass




							def  lowercase		(   self:     int				,		__A:     List[str]				,		__A:     List[Any]				,		__A:     int				,		__A:     Union[str, Any]				,		__A:     List[Any]=None				,		**__A:     Tuple    ):

												'''simple docstring'''

												a__          =  VisionTextDualEncoderConfig.from_vision_text_configs(__A				,		__A    )

												a__          =  TFVisionTextDualEncoderModel(__A    )

												a__          =  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  lowercase		(   self:     Dict				,		__A:     str				,		__A:     Union[str, Any]				,		__A:     Optional[Any]				,		__A:     Tuple				,		__A:     str=None				,		**__A:     Optional[Any]    ):

												'''simple docstring'''

												a__       ,a__          =  self.get_vision_text_model(__A				,		__A    )
												a__          =  TFVisionTextDualEncoderModel(vision_model=__A				,		text_model=__A    )

												a__          =  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  lowercase		(   self:     int				,		__A:     Optional[Any]				,		__A:     Dict				,		__A:     Optional[Any]				,		__A:     Optional[Any]				,		__A:     Optional[int]=None				,		**__A:     int    ):

												'''simple docstring'''

												a__       ,a__          =  self.get_vision_text_model(__A				,		__A    )
												a__          =  {'''vision_model''': vision_model, '''text_model''': text_model}
												a__          =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__A    )

												a__          =  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  lowercase		(   self:     int				,		__A:     Optional[int]				,		__A:     int				,		__A:     Dict				,		__A:     Optional[Any]				,		__A:     int=None				,		**__A:     str    ):

												'''simple docstring'''

												a__       ,a__          =  self.get_vision_text_model(__A				,		__A    )
												a__          =  TFVisionTextDualEncoderModel(vision_model=__A				,		text_model=__A    )

												a__          =  model(input_ids=__A				,		pixel_values=__A				,		attention_mask=__A    )
												a__          =  output[0].numpy()

												with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_pretrained(__A    )
																	a__          =  TFVisionTextDualEncoderModel.from_pretrained(__A    )

																	a__          =  model(input_ids=__A				,		pixel_values=__A				,		attention_mask=__A    )
																	a__          =  after_output[0].numpy()
																	a__          =  np.amax(np.abs(out_a - out_a    )    )
																	self.assertLessEqual(__A				,		1e-5    )




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

												'''simple docstring'''

												a__       ,a__          =  self.get_vision_text_model(__A				,		__A    )
												a__          =  TFVisionTextDualEncoderModel(vision_model=__A				,		text_model=__A    )

												a__          =  model(
												    input_ids=__A				,		pixel_values=__A				,		attention_mask=__A				,		output_attentions=__A    )

												a__          =  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)
												a__          =  to_atuple(vision_model.config.image_size    )
												a__          =  to_atuple(vision_model.config.patch_size    )
												a__          =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
												a__          =  num_patches + 1
												self.assertEqual(vision_attentions[0].shape[-3:]				,		(vision_config.num_attention_heads, seq_len, seq_len)    )

												a__          =  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  lowercase		(   self:     Any				,		__A:     np.ndarray				,		__A:     np.ndarray				,		__A:     float    ):

												'''simple docstring'''

												a__          =  np.abs((a - b)    ).max()
												self.assertLessEqual(__A				,		__A				,		F'Difference between torch and flax is {diff} (>= {tol}).'    )




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												self.check_vision_text_dual_encoder_model(**__A    )




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												self.check_model_from_pretrained_configs(**__A    )




							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												self.check_vision_text_dual_encoder_from_pretrained(**__A    )




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												self.check_save_load(**__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												self.check_vision_text_output_attention(**__A    )





							@slow
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__       ,a__          =  self.get_pretrained_model_and_inputs()

												a__          =  model_a(**__A    )
												a__          =  outputs[0].numpy()

												with tempfile.TemporaryDirectory() as tmp_dirname:
																	model_a.save_pretrained(__A    )
																	a__          =  TFVisionTextDualEncoderModel.from_pretrained(__A    )

																	a__          =  model_a(**__A    )
																	a__          =  after_outputs[0].numpy()
																	a__          =  np.amax(np.abs(out_a - out_a    )    )
																	self.assertLessEqual(__A				,		1e-5    )







@require_tf
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
												    '''hf-internal-testing/tiny-random-vit'''				,		'''hf-internal-testing/tiny-random-bert'''    )
												a__          =  13
												a__          =  floats_tensor(
												    [
												        batch_size,
												        model.vision_model.config.num_channels,
												        model.vision_model.config.image_size,
												        model.vision_model.config.image_size,
												    ]    )
												a__          =  ids_tensor([batch_size, 4]				,		model.text_model.config.vocab_size    )
												a__          =  random_attention_mask([batch_size, 4]    )
												a__          =  {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

												return model, inputs




							def  lowercase		(   self:     Any				,		__A:     Tuple				,		__A:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  TFViTModel(__A				,		name='''vision_model'''    )
												a__          =  TFBertModel(__A				,		name='''text_model'''    )
												return vision_model, text_model





							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  TFViTModelTester(self    )
												a__          =  TFBertModelTester(self    )
												a__          =  vit_model_tester.prepare_config_and_inputs()
												a__          =  bert_model_tester.prepare_config_and_inputs()

												a__       ,a__       ,a__          =  vision_config_and_inputs

												(
												    (
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,
												)          =  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        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
												    '''Rocketknight1/tiny-random-deit-tf'''				,		'''hf-internal-testing/tiny-random-roberta'''    )
												a__          =  13
												a__          =  floats_tensor(
												    [
												        batch_size,
												        model.vision_model.config.num_channels,
												        model.vision_model.config.image_size,
												        model.vision_model.config.image_size,
												    ]    )
												a__          =  ids_tensor([batch_size, 4]				,		model.text_model.config.vocab_size    )
												a__          =  random_attention_mask([batch_size, 4]    )
												a__          =  {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

												return model, inputs




							def  lowercase		(   self:     Tuple				,		__A:     int				,		__A:     int				,		__A:     int				,		__A:     Tuple				,		__A:     Union[str, Any]=None				,		**__A:     Union[str, Any]    ):

												'''simple docstring'''

												a__       ,a__          =  self.get_vision_text_model(__A				,		__A    )
												a__          =  TFVisionTextDualEncoderModel(vision_model=__A				,		text_model=__A    )

												a__          =  model(
												    input_ids=__A				,		pixel_values=__A				,		attention_mask=__A				,		output_attentions=__A    )

												a__          =  output.vision_model_output.attentions
												self.assertEqual(len(__A    )				,		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)
												a__          =  to_atuple(vision_model.config.image_size    )
												a__          =  to_atuple(vision_model.config.patch_size    )
												a__          =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
												a__          =  num_patches + 2
												self.assertEqual(vision_attentions[0].shape[-3:]				,		(vision_config.num_attention_heads, seq_len, seq_len)    )

												a__          =  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  lowercase		(   self:     List[Any]				,		__A:     Optional[Any]				,		__A:     Dict    ):

												'''simple docstring'''

												a__          =  TFDeiTModel(__A				,		name='''vision_model'''    )
												a__          =  TFRobertaModel(__A				,		name='''text_model'''    )
												return vision_model, text_model





							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  TFDeiTModelTester(self    )
												a__          =  TFRobertaModelTester(self    )
												a__          =  vit_model_tester.prepare_config_and_inputs()
												a__          =  bert_model_tester.prepare_config_and_inputs()

												a__       ,a__       ,a__          =  vision_config_and_inputs

												(
												    (
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,
												)          =  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        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
												    '''Rocketknight1/tiny-random-clip-tf'''				,		'''hf-internal-testing/tiny-random-bert'''    )
												a__          =  13
												a__          =  floats_tensor(
												    [
												        batch_size,
												        model.vision_model.config.num_channels,
												        model.vision_model.config.image_size,
												        model.vision_model.config.image_size,
												    ]    )
												a__          =  ids_tensor([batch_size, 4]				,		model.text_model.config.vocab_size    )
												a__          =  random_attention_mask([batch_size, 4]    )
												a__          =  {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

												return model, inputs




							def  lowercase		(   self:     Tuple				,		__A:     Dict				,		__A:     Tuple    ):

												'''simple docstring'''

												a__          =  TFCLIPVisionModel(__A				,		name='''vision_model'''    )
												a__          =  TFBertModel(__A				,		name='''text_model'''    )
												return vision_model, text_model





							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  TFCLIPVisionModelTester(self    )
												a__          =  TFBertModelTester(self    )
												a__          =  clip_model_tester.prepare_config_and_inputs()
												a__          =  bert_model_tester.prepare_config_and_inputs()

												a__       ,a__          =  vision_config_and_inputs

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





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  TFVisionTextDualEncoderModel.from_pretrained(
												    '''clip-italian/clip-italian'''				,		logit_scale_init_value=1.0				,		from_pt=__A    )
												a__          =  VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''    )

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

												a__          =  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])				,		)

												a__          =  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()				,		__A				,		atol=1e-3    )    )










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a       :	str             = {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	int             = [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	__a       :	Dict             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


