






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     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     Any     	=  PriorTransformer
	snake_case__							:     List[Any]     	=  """hidden_states"""





	@property
	def 						_A (		self				:					List[Any]	):
		UpperCamelCase     :List[str]          =	4
		UpperCamelCase     :Optional[int]          =	8
		UpperCamelCase     :Dict          =	7

		UpperCamelCase     :Tuple          =	floats_tensor((batch_size, embedding_dim)	).to(__lowerCamelCase	)

		UpperCamelCase     :Optional[int]          =	floats_tensor((batch_size, embedding_dim)	).to(__lowerCamelCase	)
		UpperCamelCase     :int          =	floats_tensor((batch_size, num_embeddings, embedding_dim)	).to(__lowerCamelCase	)

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





	def 						_A (		self				:					Optional[int] ,  __lowerCamelCase				:					int=0	):
		torch.manual_seed(__lowerCamelCase	)
		UpperCamelCase     :Dict          =	4
		UpperCamelCase     :Optional[Any]          =	8
		UpperCamelCase     :List[Any]          =	7

		UpperCamelCase     :str          =	torch.randn((batch_size, embedding_dim)	).to(__lowerCamelCase	)

		UpperCamelCase     :int          =	torch.randn((batch_size, embedding_dim)	).to(__lowerCamelCase	)
		UpperCamelCase     :Tuple          =	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(__lowerCamelCase	)

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





	@property
	def 						_A (		self				:					Optional[int]	):
		return (4, 8)





	@property
	def 						_A (		self				:					Optional[int]	):
		return (4, 8)





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     :Dict          =	{
		    """num_attention_heads""": 2,
		    """attention_head_dim""": 4,
		    """num_layers""": 2,
		    """embedding_dim""": 8,
		    """num_embeddings""": 7,
		    """additional_embeddings""": 4,
		}
		UpperCamelCase     :int          =	self.dummy_input
		return init_dict, inputs_dict





	def 						_A (		self				:					Dict	):
		UpperCamelCase     ,      UpperCamelCase     :Optional[int]          =	PriorTransformer.from_pretrained(
		    """hf-internal-testing/prior-dummy""" ,  output_loading_info=__lowerCamelCase	)
		self.assertIsNotNone(__lowerCamelCase	)
		self.assertEqual(len(loading_info["""missing_keys"""]	) ,  0	)

		model.to(__lowerCamelCase	)
		UpperCamelCase     :Union[str, Any]          =	model(**self.dummy_input	)[0]

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





	def 						_A (		self				:					int	):
		UpperCamelCase     ,      UpperCamelCase     :str          =	self.prepare_init_args_and_inputs_for_common()

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

		UpperCamelCase     :int          =	["""hidden_states""", """timestep"""]
		self.assertListEqual(arg_names[:2] ,  __lowerCamelCase	)





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     :Optional[int]          =	PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""	)
		UpperCamelCase     :Optional[Any]          =	model.to(__lowerCamelCase	)

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

		UpperCamelCase     :Dict          =	self.get_dummy_seed_input()

		with torch.no_grad():
			UpperCamelCase     :Dict          =	model(**__lowerCamelCase	)[0]

		UpperCamelCase     :int          =	output[0, :5].flatten().cpu()
		print(__lowerCamelCase	)

		# 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.
		UpperCamelCase     :Optional[int]          =	torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]	)
		self.assertTrue(torch_all_close(__lowerCamelCase ,  __lowerCamelCase ,  rtol=1E-2	)	)

@slow
class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





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

		UpperCamelCase     :List[Any]          =	torch.randn((batch_size, embedding_dim)	).to(__lowerCamelCase	)

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

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





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





	@parameterized.expand(
	    [
	        # fmt: off
	        [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
	        [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
	        # fmt: on
	    ]	)
	def 						_A (		self				:					Any ,  __lowerCamelCase				:					Any ,  __lowerCamelCase				:					Dict	):
		UpperCamelCase     :Any          =	PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior""" ,  subfolder="""prior"""	)
		model.to(__lowerCamelCase	)
		UpperCamelCase     :Any          =	self.get_dummy_seed_input(seed=__lowerCamelCase	)

		with torch.no_grad():
			UpperCamelCase     :Optional[int]          =	model(**__lowerCamelCase	)[0]

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

		UpperCamelCase     :Dict          =	sample[0, :8].flatten().cpu()
		print(__lowerCamelCase	)
		UpperCamelCase     :List[str]          =	torch.tensor(__lowerCamelCase	)

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








from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
	from ...processing_utils import ProcessorMixin
	from ...utils import TensorType


UpperCAmelCase_ :						Dict      =      logging.get_logger(__name__)

UpperCAmelCase_ :						str      =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Optional[int]     	=  """layoutlmv3"""





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					Optional[Any]=50_265 ,  __lowerCamelCase				:					Dict=768 ,  __lowerCamelCase				:					Any=12 ,  __lowerCamelCase				:					int=12 ,  __lowerCamelCase				:					str=3_072 ,  __lowerCamelCase				:					List[Any]="gelu" ,  __lowerCamelCase				:					List[str]=0.1 ,  __lowerCamelCase				:					Optional[int]=0.1 ,  __lowerCamelCase				:					Optional[Any]=512 ,  __lowerCamelCase				:					Optional[int]=2 ,  __lowerCamelCase				:					Union[str, Any]=0.02 ,  __lowerCamelCase				:					Union[str, Any]=1E-5 ,  __lowerCamelCase				:					Any=1 ,  __lowerCamelCase				:					Optional[int]=0 ,  __lowerCamelCase				:					List[Any]=2 ,  __lowerCamelCase				:					Dict=1_024 ,  __lowerCamelCase				:					List[Any]=128 ,  __lowerCamelCase				:					str=128 ,  __lowerCamelCase				:					Optional[Any]=True ,  __lowerCamelCase				:					str=32 ,  __lowerCamelCase				:					List[Any]=128 ,  __lowerCamelCase				:					str=64 ,  __lowerCamelCase				:					List[str]=256 ,  __lowerCamelCase				:					Dict=True ,  __lowerCamelCase				:					List[Any]=True ,  __lowerCamelCase				:					Tuple=True ,  __lowerCamelCase				:					Tuple=224 ,  __lowerCamelCase				:					Tuple=3 ,  __lowerCamelCase				:					Dict=16 ,  __lowerCamelCase				:					Union[str, Any]=None ,  **__lowerCamelCase				:					Optional[Any] ,  ):
		super().__init__(
		    vocab_size=__lowerCamelCase ,  hidden_size=__lowerCamelCase ,  num_hidden_layers=__lowerCamelCase ,  num_attention_heads=__lowerCamelCase ,  intermediate_size=__lowerCamelCase ,  hidden_act=__lowerCamelCase ,  hidden_dropout_prob=__lowerCamelCase ,  attention_probs_dropout_prob=__lowerCamelCase ,  max_position_embeddings=__lowerCamelCase ,  type_vocab_size=__lowerCamelCase ,  initializer_range=__lowerCamelCase ,  layer_norm_eps=__lowerCamelCase ,  pad_token_id=__lowerCamelCase ,  bos_token_id=__lowerCamelCase ,  eos_token_id=__lowerCamelCase ,  **__lowerCamelCase ,  )
		UpperCamelCase     :int          =	max_ad_position_embeddings
		UpperCamelCase     :Tuple          =	coordinate_size
		UpperCamelCase     :List[Any]          =	shape_size
		UpperCamelCase     :Union[str, Any]          =	has_relative_attention_bias
		UpperCamelCase     :Any          =	rel_pos_bins
		UpperCamelCase     :Optional[Any]          =	max_rel_pos
		UpperCamelCase     :str          =	has_spatial_attention_bias
		UpperCamelCase     :Tuple          =	rel_ad_pos_bins
		UpperCamelCase     :Optional[int]          =	max_rel_ad_pos
		UpperCamelCase     :Tuple          =	text_embed
		UpperCamelCase     :str          =	visual_embed
		UpperCamelCase     :Optional[Any]          =	input_size
		UpperCamelCase     :str          =	num_channels
		UpperCamelCase     :List[Any]          =	patch_size
		UpperCamelCase     :Optional[Any]          =	classifier_dropout

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     int     	=  version.parse("""1.12"""      )





	@property
	def 						_A (		self				:					Optional[int]	):
		# The order of inputs is different for question answering and sequence classification
		if self.task in ["question-answering", "sequence-classification"]:
			return OrderedDict(
			    [
			        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
			        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
			        ("""bbox""", {0: """batch""", 1: """sequence"""}),
			        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
			    ]	)
		else:
			return OrderedDict(
			    [
			        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
			        ("""bbox""", {0: """batch""", 1: """sequence"""}),
			        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
			        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
			    ]	)





	@property
	def 						_A (		self				:					str	):
		return 1E-5





	@property
	def 						_A (		self				:					Dict	):
		return 12





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					"ProcessorMixin" ,  __lowerCamelCase				:					int = -1 ,  __lowerCamelCase				:					int = -1 ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					Optional["TensorType"] = None ,  __lowerCamelCase				:					int = 3 ,  __lowerCamelCase				:					int = 40 ,  __lowerCamelCase				:					int = 40 ,  ):
		setattr(processor.image_processor ,  """apply_ocr""" ,  __lowerCamelCase	)

		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
		UpperCamelCase     :Optional[Any]          =	compute_effective_axis_dimension(
		    __lowerCamelCase ,  fixed_dimension=OnnxConfig.default_fixed_batch ,  num_token_to_add=0	)
		# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
		UpperCamelCase     :Optional[int]          =	processor.tokenizer.num_special_tokens_to_add(__lowerCamelCase	)
		UpperCamelCase     :int          =	compute_effective_axis_dimension(
		    __lowerCamelCase ,  fixed_dimension=OnnxConfig.default_fixed_sequence ,  num_token_to_add=__lowerCamelCase	)
		# Generate dummy inputs according to compute batch and sequence
		UpperCamelCase     :Any          =	[[""" """.join([processor.tokenizer.unk_token]	) * seq_length]] * batch_size

		# Generate dummy bounding boxes
		UpperCamelCase     :Optional[Any]          =	[[[48, 84, 73, 128]]] * batch_size

		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
		# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
		UpperCamelCase     :List[str]          =	self._generate_dummy_images(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)

		UpperCamelCase     :Any          =	dict(
		    processor(
		        __lowerCamelCase ,  text=__lowerCamelCase ,  boxes=__lowerCamelCase ,  return_tensors=__lowerCamelCase ,  )	)

		return inputs
