
'''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 A (__lowerCamelCase :int						):
						if isinstance(__lowerCamelCase					,      collections.abc.Iterable						):
												return x
						return (x, x)




@require_tf
class     UpperCAmelCase_	:






						'''simple docstring'''






						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												pass




						def    _lowercase      (   self ):

												"""simple docstring"""



												pass




						def    _lowercase      (   self ):

												"""simple docstring"""



												pass




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase=None       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					VisionTextDualEncoderConfig.from_vision_text_configs(_lowercase       ,  _lowercase )

												_lowerCAmelCase    		=					TFVisionTextDualEncoderModel(_lowercase )

												_lowerCAmelCase    		=					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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase=None       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.get_vision_text_model(_lowercase       ,  _lowercase )
												_lowerCAmelCase    		=					TFVisionTextDualEncoderModel(vision_model=_lowercase       ,  text_model=_lowercase )

												_lowerCAmelCase    		=					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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase=None       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.get_vision_text_model(_lowercase       ,  _lowercase )
												_lowerCAmelCase    		=					{"""vision_model""": vision_model, """text_model""": text_model}
												_lowerCAmelCase    		=					TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowercase )

												_lowerCAmelCase    		=					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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase=None       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.get_vision_text_model(_lowercase       ,  _lowercase )
												_lowerCAmelCase    		=					TFVisionTextDualEncoderModel(vision_model=_lowercase       ,  text_model=_lowercase )

												_lowerCAmelCase    		=					model(input_ids=_lowercase       ,  pixel_values=_lowercase       ,  attention_mask=_lowercase )
												_lowerCAmelCase    		=					output[0].numpy()

												with tempfile.TemporaryDirectory() as tmpdirname:
																		model.save_pretrained(_lowercase )
																		_lowerCAmelCase    		=					TFVisionTextDualEncoderModel.from_pretrained(_lowercase )

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




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase=None       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.get_vision_text_model(_lowercase       ,  _lowercase )
												_lowerCAmelCase    		=					TFVisionTextDualEncoderModel(vision_model=_lowercase       ,  text_model=_lowercase )

												_lowerCAmelCase    		=					model(
												    input_ids=_lowercase       ,  pixel_values=_lowercase       ,  attention_mask=_lowercase       ,  output_attentions=_lowercase )

												_lowerCAmelCase    		=					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)
												_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(_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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.prepare_config_and_inputs()
												self.check_vision_text_dual_encoder_model(**_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.prepare_config_and_inputs()
												self.check_model_from_pretrained_configs(**_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.prepare_config_and_inputs()
												self.check_vision_text_dual_encoder_from_pretrained(**_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.prepare_config_and_inputs()
												self.check_save_load(**_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.prepare_config_and_inputs()
												self.check_vision_text_output_attention(**_lowercase )




						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.get_pretrained_model_and_inputs()

												_lowerCAmelCase    		=					model_a(**_lowercase )
												_lowerCAmelCase    		=					outputs[0].numpy()

												with tempfile.TemporaryDirectory() as tmp_dirname:
																		model_a.save_pretrained(_lowercase )
																		_lowerCAmelCase    		=					TFVisionTextDualEncoderModel.from_pretrained(_lowercase )

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






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






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFVisionTextDualEncoderModel.from_vision_text_pretrained(
												    """hf-internal-testing/tiny-random-vit"""       ,  """hf-internal-testing/tiny-random-bert""" )
												_lowerCAmelCase    		=					13
												_lowerCAmelCase    		=					floats_tensor(
												    [
												        batch_size,
												        model.vision_model.config.num_channels,
												        model.vision_model.config.image_size,
												        model.vision_model.config.image_size,
												    ] )
												_lowerCAmelCase    		=					ids_tensor([batch_size, 4]       ,  model.text_model.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    _lowercase      (   self       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFViTModel(_lowercase       ,  name="""vision_model""" )
												_lowerCAmelCase    		=					TFBertModel(_lowercase       ,  name="""text_model""" )
												return vision_model, text_model




						def    _lowercase      (   self ):

												"""simple docstring"""



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

												_lowerCAmelCase		,       _lowerCAmelCase		,       _lowerCAmelCase    		=					vision_config_and_inputs

												(
												    (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       
												)    		=					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     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFVisionTextDualEncoderModel.from_vision_text_pretrained(
												    """Rocketknight1/tiny-random-deit-tf"""       ,  """hf-internal-testing/tiny-random-roberta""" )
												_lowerCAmelCase    		=					13
												_lowerCAmelCase    		=					floats_tensor(
												    [
												        batch_size,
												        model.vision_model.config.num_channels,
												        model.vision_model.config.image_size,
												        model.vision_model.config.image_size,
												    ] )
												_lowerCAmelCase    		=					ids_tensor([batch_size, 4]       ,  model.text_model.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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase=None       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.get_vision_text_model(_lowercase       ,  _lowercase )
												_lowerCAmelCase    		=					TFVisionTextDualEncoderModel(vision_model=_lowercase       ,  text_model=_lowercase )

												_lowerCAmelCase    		=					model(
												    input_ids=_lowercase       ,  pixel_values=_lowercase       ,  attention_mask=_lowercase       ,  output_attentions=_lowercase )

												_lowerCAmelCase    		=					output.vision_model_output.attentions
												self.assertEqual(len(_lowercase )       ,  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    		=					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 + 2
												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(_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    _lowercase      (   self       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFDeiTModel(_lowercase       ,  name="""vision_model""" )
												_lowerCAmelCase    		=					TFRobertaModel(_lowercase       ,  name="""text_model""" )
												return vision_model, text_model




						def    _lowercase      (   self ):

												"""simple docstring"""



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

												_lowerCAmelCase		,       _lowerCAmelCase		,       _lowerCAmelCase    		=					vision_config_and_inputs

												(
												    (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       
												)    		=					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     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFVisionTextDualEncoderModel.from_vision_text_pretrained(
												    """Rocketknight1/tiny-random-clip-tf"""       ,  """hf-internal-testing/tiny-random-bert""" )
												_lowerCAmelCase    		=					13
												_lowerCAmelCase    		=					floats_tensor(
												    [
												        batch_size,
												        model.vision_model.config.num_channels,
												        model.vision_model.config.image_size,
												        model.vision_model.config.image_size,
												    ] )
												_lowerCAmelCase    		=					ids_tensor([batch_size, 4]       ,  model.text_model.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    _lowercase      (   self       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFCLIPVisionModel(_lowercase       ,  name="""vision_model""" )
												_lowerCAmelCase    		=					TFBertModel(_lowercase       ,  name="""text_model""" )
												return vision_model, text_model




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFCLIPVisionModelTester(self )
												_lowerCAmelCase    		=					TFBertModelTester(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
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       (
												    _lowerCAmelCase
												)		,       
												)    		=					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     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFVisionTextDualEncoderModel.from_pretrained(
												    """clip-italian/clip-italian"""       ,  logit_scale_init_value=1.0       ,  from_pt=_lowercase )
												_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=_lowercase       ,  padding=_lowercase       ,  return_tensors="""np""" )

												_lowerCAmelCase    		=					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])       ,  )

												_lowerCAmelCase    		=					np.array([[1.228_4727, 0.310_4122]] )

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


'''simple docstring'''



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    ImageTextPipelineOutput,
						    UniDiffuserPipeline,
						)
else:
						from .modeling_text_decoder import UniDiffuserTextDecoder
						from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
						from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
