






from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						Union[str, Any]							,	__A  :						int							,	__A  :						Optional[int]=1_3							,	__A  :						Dict=3_0							,	__A  :						str=2							,	__A  :						List[str]=3							,	__A  :						Union[str, Any]=True							,	__A  :						List[Any]=True							,	__A  :						List[Any]=3_2							,	__A  :						str=2							,	__A  :						Any=4							,	__A  :						Dict=3_7							,	__A  :						Optional[int]="gelu"							,	__A  :						List[str]=0.1							,	__A  :						List[str]=0.1							,	__A  :						str=1_0							,	__A  :						Any=0.0_2							,	__A  :						str=3							,	__A  :						Any=None							,	):
						snake_case__				:   Optional[int]     =						parent
						snake_case__				:   str     =						batch_size
						snake_case__				:   Optional[Any]     =						image_size
						snake_case__				:   Tuple     =						patch_size
						snake_case__				:   Union[str, Any]     =						num_channels
						snake_case__				:   List[Any]     =						is_training
						snake_case__				:   Optional[int]     =						use_labels
						snake_case__				:   Union[str, Any]     =						hidden_size
						snake_case__				:   Any     =						num_hidden_layers
						snake_case__				:   str     =						num_attention_heads
						snake_case__				:   int     =						intermediate_size
						snake_case__				:   Any     =						hidden_act
						snake_case__				:   str     =						hidden_dropout_prob
						snake_case__				:   str     =						attention_probs_dropout_prob
						snake_case__				:   Optional[Any]     =						type_sequence_label_size
						snake_case__				:   Optional[Any]     =						initializer_range
						snake_case__				:   str     =						scope

						# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						snake_case__				:   Any     =						(image_size // patch_size) ** 2
						snake_case__				:   Optional[Any]     =						num_patches + 1



					def  _lowercase  (      self  :						Optional[Any] ):
						snake_case__				:   Tuple     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

						snake_case__				:   Tuple     =						None
						if self.use_labels:
							snake_case__				:   str     =						ids_tensor([self.batch_size]							,	self.type_sequence_label_size )

						snake_case__				:   Tuple     =						self.get_config()

						return config, pixel_values, labels



					def  _lowercase  (      self  :						Dict ):
						return ViTConfig(
						    image_size=self.image_size							,	patch_size=self.patch_size							,	num_channels=self.num_channels							,	hidden_size=self.hidden_size							,	num_hidden_layers=self.num_hidden_layers							,	num_attention_heads=self.num_attention_heads							,	intermediate_size=self.intermediate_size							,	hidden_act=self.hidden_act							,	hidden_dropout_prob=self.hidden_dropout_prob							,	attention_probs_dropout_prob=self.attention_probs_dropout_prob							,	is_decoder=__A							,	initializer_range=self.initializer_range							,	)



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Union[str, Any]							,	__A  :						Any							,	__A  :						Optional[int] ):
						snake_case__				:   List[Any]     =						TFViTModel(config=__A )
						snake_case__				:   Union[str, Any]     =						model(__A							,	training=__A )
						self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size) )

						# Test with an image with different size than the one specified in config.
						snake_case__				:   Optional[Any]     =						self.image_size // 2
						snake_case__				:   Dict     =						pixel_values[:, :, :image_size, :image_size]
						snake_case__				:   Any     =						model(__A							,	interpolate_pos_encoding=__A							,	training=__A )
						snake_case__				:   Any     =						(image_size // self.patch_size) ** 2 + 1
						self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, seq_length, self.hidden_size) )



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						int							,	__A  :						List[Any]							,	__A  :						List[Any] ):
						snake_case__				:   Any     =						self.type_sequence_label_size
						snake_case__				:   Optional[Any]     =						TFViTForImageClassification(__A )
						snake_case__				:   List[str]     =						model(__A							,	labels=__A							,	training=__A )
						self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.type_sequence_label_size) )

						# Test with an image with different size than the one specified in config.
						snake_case__				:   Tuple     =						self.image_size // 2
						snake_case__				:   str     =						pixel_values[:, :, :image_size, :image_size]
						snake_case__				:   int     =						model(__A							,	interpolate_pos_encoding=__A							,	training=__A )
						self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.type_sequence_label_size) )

						# test greyscale images
						snake_case__				:   Union[str, Any]     =						1
						snake_case__				:   Union[str, Any]     =						TFViTForImageClassification(__A )
						snake_case__				:   Tuple     =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
						snake_case__				:   str     =						model(__A )
						self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.type_sequence_label_size) )






					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__				:   Optional[int]     =						self.prepare_config_and_inputs()
						snake_case__				:   Any     =						config_and_inputs
						snake_case__				:   int     =						{"pixel_values": pixel_values}
						return config, inputs_dict



@require_tf
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
					a_			=	(
					    {"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
					    if is_tf_available()
					    else {}
					)

					a_			=	False
					a_			=	False
					a_			=	False



					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__				:   Any     =						TFViTModelTester(self )
						snake_case__				:   Dict     =						ConfigTester(self							,	config_class=__A							,	has_text_modality=__A							,	hidden_size=3_7 )



					def  _lowercase  (      self  :						Any ):
						self.config_tester.run_common_tests()



					@unittest.skip(reason="ViT does not use inputs_embeds" )
					def  _lowercase  (      self  :						Optional[int] ):
						pass



					@unittest.skip(reason="ViT does not use inputs_embeds" )
					def  _lowercase  (      self  :						Optional[int] ):
						pass



					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							snake_case__				:   Any     =						model_class(__A )
							self.assertIsInstance(model.get_input_embeddings()							,	(tf.keras.layers.Layer) )
							snake_case__				:   Optional[int]     =						model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(__A							,	tf.keras.layers.Layer ) )



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   str     =						self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							snake_case__				:   Optional[Any]     =						model_class(__A )
							snake_case__				:   Union[str, Any]     =						inspect.signature(model.call )
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							snake_case__				:   Dict     =						[*signature.parameters.keys()]

							snake_case__				:   Tuple     =						["pixel_values"]
							self.assertListEqual(arg_names[:1]							,	__A )



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   List[Any]     =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__A )



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Tuple     =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__A )






					@slow
					def  _lowercase  (      self  :						str ):
						snake_case__				:   List[str]     =						TFViTModel.from_pretrained("google/vit-base-patch16-224" )
						self.assertIsNotNone(__A )







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Dict     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
	return image





@require_tf
@require_vision
class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					@cached_property
					def  _lowercase  (      self  :						Union[str, Any] ):
						return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224" ) if is_vision_available() else None






					@slow
					def  _lowercase  (      self  :						int ):
						snake_case__				:   Tuple     =						TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224" )

						snake_case__				:   List[str]     =						self.default_image_processor
						snake_case__				:   Optional[int]     =						prepare_img()
						snake_case__				:   int     =						image_processor(images=__A							,	return_tensors="tf" )

						# forward pass
						snake_case__				:   Dict     =						model(**__A )

						# verify the logits
						snake_case__				:   str     =						tf.TensorShape((1, 1_0_0_0) )
						self.assertEqual(outputs.logits.shape							,	__A )

						snake_case__				:   int     =						tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6] )

						tf.debugging.assert_near(outputs.logits[0, :3]							,	__A							,	atol=1e-4 )









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 KandinskyPipeline, KandinskyPriorPipeline
else:
	from .pipeline_kandinsky import KandinskyPipeline
	from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
	from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
	from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
	from .text_encoder import MultilingualCLIP



