


"""simple docstring"""
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast



@require_vision
class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				tempfile.mkdtemp()

			__snake_case	:						Tuple						=				BlipImageProcessor()
			__snake_case	:						str						=				GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''					)

			__snake_case	:						Dict						=				BlipaProcessor(a_				,       a_					)

			processor.save_pretrained(self.tmpdirname					)



		def 				SCREAMING_SNAKE_CASE  (self				,       **a_					):





			'''simple docstring'''

			return AutoProcessor.from_pretrained(self.tmpdirname				,       **a_					).tokenizer



		def 				SCREAMING_SNAKE_CASE  (self				,       **a_					):





			'''simple docstring'''

			return AutoProcessor.from_pretrained(self.tmpdirname				,       **a_					).image_processor



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			shutil.rmtree(self.tmpdirname					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				[np.random.randint(2_55				,       size=(3, 30, 4_00)				,       dtype=np.uinta					)]

			__snake_case	:						Tuple						=				[Image.fromarray(np.moveaxis(a_				,       0				,       -1					)					) for x in image_inputs]

			return image_inputs



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				BlipaProcessor(tokenizer=self.get_tokenizer()				,       image_processor=self.get_image_processor()					)
			processor.save_pretrained(self.tmpdirname					)

			__snake_case	:						Optional[int]						=				self.get_tokenizer(bos_token='''(BOS)'''				,       eos_token='''(EOS)'''					)
			__snake_case	:						List[Any]						=				self.get_image_processor(do_normalize=a_				,       padding_value=1.0					)

			__snake_case	:						int						=				BlipaProcessor.from_pretrained(
			    self.tmpdirname				,       bos_token='''(BOS)'''				,       eos_token='''(EOS)'''				,       do_normalize=a_				,       padding_value=1.0					)

			self.assertEqual(processor.tokenizer.get_vocab()				,       tokenizer_add_kwargs.get_vocab()					)
			self.assertIsInstance(processor.tokenizer				,       a_					)

			self.assertEqual(processor.image_processor.to_json_string()				,       image_processor_add_kwargs.to_json_string()					)
			self.assertIsInstance(processor.image_processor				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				self.get_image_processor()
			__snake_case	:						Any						=				self.get_tokenizer()

			__snake_case	:						int						=				BlipaProcessor(tokenizer=a_				,       image_processor=a_					)

			__snake_case	:						Optional[Any]						=				self.prepare_image_inputs()

			__snake_case	:						Optional[int]						=				image_processor(a_				,       return_tensors='''np'''					)
			__snake_case	:						Any						=				processor(images=a_				,       return_tensors='''np'''					)

			for key in input_feat_extract.keys():
				self.assertAlmostEqual(input_feat_extract[key].sum()				,       input_processor[key].sum()				,       delta=1E-2					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				self.get_image_processor()
			__snake_case	:						Union[str, Any]						=				self.get_tokenizer()

			__snake_case	:						Optional[Any]						=				BlipaProcessor(tokenizer=a_				,       image_processor=a_					)

			__snake_case	:						Optional[Any]						=				'''lower newer'''

			__snake_case	:						str						=				processor(text=a_					)

			__snake_case	:						Optional[int]						=				tokenizer(a_				,       return_token_type_ids=a_					)

			for key in encoded_tok.keys():
				self.assertListEqual(encoded_tok[key]				,       encoded_processor[key]					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				self.get_image_processor()
			__snake_case	:						Dict						=				self.get_tokenizer()

			__snake_case	:						Tuple						=				BlipaProcessor(tokenizer=a_				,       image_processor=a_					)

			__snake_case	:						List[str]						=				'''lower newer'''
			__snake_case	:						str						=				self.prepare_image_inputs()

			__snake_case	:						Optional[int]						=				processor(text=a_				,       images=a_					)

			self.assertListEqual(list(inputs.keys()					)				,       ['''pixel_values''', '''input_ids''', '''attention_mask''']					)

			# test if it raises when no input is passed
			with pytest.raises(a_					):
				processor()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				self.get_image_processor()
			__snake_case	:						int						=				self.get_tokenizer()

			__snake_case	:						Optional[int]						=				BlipaProcessor(tokenizer=a_				,       image_processor=a_					)

			__snake_case	:						Union[str, Any]						=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			__snake_case	:						Union[str, Any]						=				processor.batch_decode(a_					)
			__snake_case	:						int						=				tokenizer.batch_decode(a_					)

			self.assertListEqual(a_				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				self.get_image_processor()
			__snake_case	:						List[Any]						=				self.get_tokenizer()

			__snake_case	:						str						=				BlipaProcessor(tokenizer=a_				,       image_processor=a_					)

			__snake_case	:						Dict						=				'''lower newer'''
			__snake_case	:						Optional[int]						=				self.prepare_image_inputs()

			__snake_case	:						Tuple						=				processor(text=a_				,       images=a_					)

			# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
			self.assertListEqual(list(inputs.keys()					)				,       ['''pixel_values''', '''input_ids''', '''attention_mask''']					)






'''simple docstring'''




import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class     __magic_name__						(    _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       PriorTransformer
			UpperCamelCase__				=       '''hidden_states'''







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Optional[Any]										=       4
										lowercase_					:			int										=       8
										lowercase_					:			Union[str, Any]										=       7

										lowercase_					:			List[str]										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[str]										=       floats_tensor((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[Any]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			int										=       4
										lowercase_					:			Any										=       8
										lowercase_					:			Tuple										=       7

										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										return (4, 8)







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return (4, 8)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			Optional[Any]										=       {
										    """num_attention_heads""": 2,
										    """attention_head_dim""": 4,
										    """num_layers""": 2,
										    """embedding_dim""": 8,
										    """num_embeddings""": 7,
										    """additional_embeddings""": 4,
										}
										lowercase_					:			List[Any]										=       self.dummy_input
										return init_dict, inputs_dict







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_						,						lowercase_					:			Tuple										=       PriorTransformer.from_pretrained(
										    """hf-internal-testing/prior-dummy"""				,			output_loading_info=lowercase_    )
										self.assertIsNotNone(lowercase_    )
										self.assertEqual(len(loading_info["""missing_keys"""]    )				,			0    )

										model.to(lowercase_    )
										lowercase_					:			Optional[int]										=       model(**self.dummy_input    )[0]

										assert hidden_states is not None, "Make sure output is not None"







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_						,						lowercase_					:			str										=       self.prepare_init_args_and_inputs_for_common()

										lowercase_					:			List[Any]										=       self.model_class(**lowercase_    )
										lowercase_					:			Any										=       inspect.signature(model.forward    )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowercase_					:			Any										=       [*signature.parameters.keys()]

										lowercase_					:			Optional[Any]										=       ["""hidden_states""", """timestep"""]
										self.assertListEqual(arg_names[:2]				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_					:			Optional[Any]										=       PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""    )
										lowercase_					:			Any										=       model.to(lowercase_    )

										if hasattr(lowercase_				,			"""set_default_attn_processor"""    ):
																	model.set_default_attn_processor()

										lowercase_					:			Any										=       self.get_dummy_seed_input()

										with torch.no_grad():
																	lowercase_					:			List[str]										=       model(**lowercase_    )[0]

										lowercase_					:			Tuple										=       output[0, :5].flatten().cpu()
										print(lowercase_    )

										# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
										# the expected output slices are not the same for CPU and GPU.
										lowercase_					:			Dict										=       torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]    )
										self.assertTrue(torch_all_close(lowercase_				,			lowercase_				,			rtol=1E-2    )    )





@slow
class     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Union[str, Any]=1				,			lowercase_				:  Optional[Any]=768				,			lowercase_				:  Tuple=77				,			lowercase_				:  Optional[int]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			Optional[Any]										=       batch_size
										lowercase_					:			int										=       embedding_dim
										lowercase_					:			int										=       num_embeddings

										lowercase_					:			Dict										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Any										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
			        [37, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
			        # fmt: on
			    ]    )
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Any				,			lowercase_				:  List[str]    ):
										lowercase_					:			List[Any]										=       PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""				,			subfolder="""prior"""    )
										model.to(lowercase_    )
										lowercase_					:			Optional[Any]										=       self.get_dummy_seed_input(seed=lowercase_    )

										with torch.no_grad():
																	lowercase_					:			Tuple										=       model(**lowercase_    )[0]

										assert list(sample.shape    ) == [1, 768]

										lowercase_					:			Union[str, Any]										=       sample[0, :8].flatten().cpu()
										print(lowercase_    )
										lowercase_					:			Optional[Any]										=       torch.tensor(lowercase_    )

										assert torch_all_close(lowercase_				,			lowercase_				,			atol=1E-3    )





