
import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
			    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
			    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
			    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
			    LayoutLMvaConfig,
			    LayoutLMvaForQuestionAnswering,
			    LayoutLMvaForSequenceClassification,
			    LayoutLMvaForTokenClassification,
			    LayoutLMvaModel,
			)
			from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
			from PIL import Image

			from transformers import LayoutLMvaImageProcessor




class        _SCREAMING_SNAKE_CASE	:



	def __init__(       self		,    lowercase		,    lowercase=2		,    lowercase=3		,    lowercase=4		,    lowercase=2		,    lowercase=7		,    lowercase=True		,    lowercase=True		,    lowercase=True		,    lowercase=True		,    lowercase=99		,    lowercase=36		,    lowercase=3		,    lowercase=4		,    lowercase=37		,    lowercase="gelu"		,    lowercase=0.1		,    lowercase=0.1		,    lowercase=512		,    lowercase=16		,    lowercase=2		,    lowercase=0.0_2		,    lowercase=6		,    lowercase=6		,    lowercase=3		,    lowercase=4		,    lowercase=None		,    lowercase=1000		,    )							->			Dict:
					lowerCamelCase_         =     parent
					lowerCamelCase_         =     batch_size
					lowerCamelCase_         =     num_channels
					lowerCamelCase_         =     image_size
					lowerCamelCase_         =     patch_size
					lowerCamelCase_         =     text_seq_length
					lowerCamelCase_         =     is_training
					lowerCamelCase_         =     use_input_mask
					lowerCamelCase_         =     use_token_type_ids
					lowerCamelCase_         =     use_labels
					lowerCamelCase_         =     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_         =     type_sequence_label_size
					lowerCamelCase_         =     initializer_range
					lowerCamelCase_         =     coordinate_size
					lowerCamelCase_         =     shape_size
					lowerCamelCase_         =     num_labels
					lowerCamelCase_         =     num_choices
					lowerCamelCase_         =     scope
					lowerCamelCase_         =     range_bbox

					# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
					lowerCamelCase_         =     text_seq_length
					lowerCamelCase_         =     (image_size // patch_size) ** 2 + 1
					lowerCamelCase_         =     self.text_seq_length + self.image_seq_length



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					lowerCamelCase_         =     ids_tensor([self.batch_size, self.text_seq_length]		,    self.vocab_size					)

					lowerCamelCase_         =     ids_tensor([self.batch_size, self.text_seq_length, 4]		,    self.range_bbox					)
					# Ensure that bbox is legal
					for i in range(bbox.shape[0]					):
									for j in range(bbox.shape[1]					):
													if bbox[i, j, 3] < bbox[i, j, 1]:
																	lowerCamelCase_         =     bbox[i, j, 3]
																	lowerCamelCase_         =     bbox[i, j, 1]
																	lowerCamelCase_         =     t
													if bbox[i, j, 2] < bbox[i, j, 0]:
																	lowerCamelCase_         =     bbox[i, j, 2]
																	lowerCamelCase_         =     bbox[i, j, 0]
																	lowerCamelCase_         =     t

					lowerCamelCase_         =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

					lowerCamelCase_         =     None
					if self.use_input_mask:
									lowerCamelCase_         =     random_attention_mask([self.batch_size, self.text_seq_length]					)

					lowerCamelCase_         =     None
					if self.use_token_type_ids:
									lowerCamelCase_         =     ids_tensor([self.batch_size, self.text_seq_length]		,    self.type_vocab_size					)

					lowerCamelCase_         =     None
					lowerCamelCase_         =     None
					if self.use_labels:
									lowerCamelCase_         =     ids_tensor([self.batch_size]		,    self.type_sequence_label_size					)
									lowerCamelCase_         =     ids_tensor([self.batch_size, self.text_seq_length]		,    self.num_labels					)

					lowerCamelCase_         =     LayoutLMvaConfig(
					    vocab_size=self.vocab_size		,    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		,    max_position_embeddings=self.max_position_embeddings		,    type_vocab_size=self.type_vocab_size		,    initializer_range=self.initializer_range		,    coordinate_size=self.coordinate_size		,    shape_size=self.shape_size		,    input_size=self.image_size		,    patch_size=self.patch_size		,    )

					return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase					)							->			List[str]:
					lowerCamelCase_         =     LayoutLMvaModel(config=lowercase					)
					model.to(lowercase					)
					model.eval()

					# text + image
					lowerCamelCase_         =     model(lowercase		,    pixel_values=lowercase					)
					lowerCamelCase_         =     model(
					    lowercase		,    bbox=lowercase		,    pixel_values=lowercase		,    attention_mask=lowercase		,    token_type_ids=lowercase					)
					lowerCamelCase_         =     model(lowercase		,    bbox=lowercase		,    pixel_values=lowercase		,    token_type_ids=lowercase					)
					lowerCamelCase_         =     model(lowercase		,    bbox=lowercase		,    pixel_values=lowercase					)

					self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)					)

					# text only
					lowerCamelCase_         =     model(lowercase					)
					self.parent.assertEqual(
					    result.last_hidden_state.shape		,    (self.batch_size, self.text_seq_length, self.hidden_size)					)

					# image only
					lowerCamelCase_         =     model(pixel_values=lowercase					)
					self.parent.assertEqual(
					    result.last_hidden_state.shape		,    (self.batch_size, self.image_seq_length, self.hidden_size)					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase					)							->			Tuple:
					lowerCamelCase_         =     self.num_labels
					lowerCamelCase_         =     LayoutLMvaForSequenceClassification(lowercase					)
					model.to(lowercase					)
					model.eval()
					lowerCamelCase_         =     model(
					    lowercase		,    bbox=lowercase		,    pixel_values=lowercase		,    attention_mask=lowercase		,    token_type_ids=lowercase		,    labels=lowercase		,    )
					self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase					)							->			Optional[Any]:
					lowerCamelCase_         =     self.num_labels
					lowerCamelCase_         =     LayoutLMvaForTokenClassification(config=lowercase					)
					model.to(lowercase					)
					model.eval()
					lowerCamelCase_         =     model(
					    lowercase		,    bbox=lowercase		,    pixel_values=lowercase		,    attention_mask=lowercase		,    token_type_ids=lowercase		,    labels=lowercase		,    )
					self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.text_seq_length, self.num_labels)					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase					)							->			Optional[int]:
					lowerCamelCase_         =     LayoutLMvaForQuestionAnswering(config=lowercase					)
					model.to(lowercase					)
					model.eval()
					lowerCamelCase_         =     model(
					    lowercase		,    bbox=lowercase		,    pixel_values=lowercase		,    attention_mask=lowercase		,    token_type_ids=lowercase		,    start_positions=lowercase		,    end_positions=lowercase		,    )
					self.parent.assertEqual(result.start_logits.shape		,    (self.batch_size, self.seq_length)					)
					self.parent.assertEqual(result.end_logits.shape		,    (self.batch_size, self.seq_length)					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[Any]:
					lowerCamelCase_         =     self.prepare_config_and_inputs()
					(
					    lowerCamelCase_  
					)         =     config_and_inputs
					lowerCamelCase_         =     {
					    "input_ids": input_ids,
					    "bbox": bbox,
					    "pixel_values": pixel_values,
					    "token_type_ids": token_type_ids,
					    "attention_mask": input_mask,
					}
					return config, inputs_dict




@require_torch
class        _SCREAMING_SNAKE_CASE	( _UpperCamelCase     ,			_UpperCamelCase     ,			unittest.TestCase		):
	lowerCAmelCase__   =					False
	lowerCAmelCase__   =					False
	lowerCAmelCase__   =					False

	lowerCAmelCase__   =					(
	    (
	        LayoutLMvaModel,
	        LayoutLMvaForSequenceClassification,
	        LayoutLMvaForTokenClassification,
	        LayoutLMvaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCAmelCase__   =					(
	    {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
	    if is_torch_available()
	    else {}
	)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase					)							->			Union[str, Any]:
					return True



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					lowerCamelCase_         =     LayoutLMvaModelTester(self					)
					lowerCamelCase_         =     ConfigTester(self		,    config_class=lowercase		,    hidden_size=37					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase=False					)							->			Dict:
					lowerCamelCase_         =     copy.deepcopy(lowercase					)
					if model_class in get_values(lowercase					):
									lowerCamelCase_         =     {
									    k: v.unsqueeze(1					).expand(-1		,    self.model_tester.num_choices		,    -1					).contiguous()
									    if isinstance(lowercase		,    torch.Tensor					) and v.ndim > 1
									    else v
									    for k, v in inputs_dict.items()
									}
					if return_labels:
									if model_class in get_values(lowercase					):
													lowerCamelCase_         =     torch.ones(self.model_tester.batch_size		,    dtype=torch.long		,    device=lowercase					)
									elif model_class in get_values(lowercase					):
													lowerCamelCase_         =     torch.zeros(
													    self.model_tester.batch_size		,    dtype=torch.long		,    device=lowercase					)
													lowerCamelCase_         =     torch.zeros(
													    self.model_tester.batch_size		,    dtype=torch.long		,    device=lowercase					)
									elif model_class in [
									    *get_values(lowercase					),
									]:
													lowerCamelCase_         =     torch.zeros(
													    self.model_tester.batch_size		,    dtype=torch.long		,    device=lowercase					)
									elif model_class in [
									    *get_values(lowercase					),
									]:
													lowerCamelCase_         =     torch.zeros(
													    (self.model_tester.batch_size, self.model_tester.text_seq_length)		,    dtype=torch.long		,    device=lowercase		,    )

					return inputs_dict



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Tuple:
					self.config_tester.run_common_tests()



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					lowerCamelCase_         =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					lowerCamelCase_         =     self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
									lowerCamelCase_         =     type
									self.model_tester.create_and_check_model(*lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					lowerCamelCase_         =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					lowerCamelCase_         =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					lowerCamelCase_         =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*lowercase					)



	@slow
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[Any]:





					for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_         =     LayoutLMvaModel.from_pretrained(lowercase					)
									self.assertIsNotNone(lowercase					)





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





@require_torch
class        _SCREAMING_SNAKE_CASE	( unittest.TestCase		):



	@cached_property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					return LayoutLMvaImageProcessor(apply_ocr=lowercase					) if is_vision_available() else None



	@slow
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					lowerCamelCase_         =     LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"					).to(lowercase					)

					lowerCamelCase_         =     self.default_image_processor
					lowerCamelCase_         =     prepare_img()
					lowerCamelCase_         =     image_processor(images=lowercase		,    return_tensors="pt"					).pixel_values.to(lowercase					)

					lowerCamelCase_         =     torch.tensor([[1, 2]]					)
					lowerCamelCase_         =     torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]					).unsqueeze(0					)

					# forward pass
					lowerCamelCase_         =     model(
					    input_ids=input_ids.to(lowercase					)		,    bbox=bbox.to(lowercase					)		,    pixel_values=pixel_values.to(lowercase					)		,    )

					# verify the logits
					lowerCamelCase_         =     torch.Size((1, 199, 768)					)
					self.assertEqual(outputs.last_hidden_state.shape		,    lowercase					)

					lowerCamelCase_         =     torch.tensor(
					    [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]					).to(lowercase					)

					self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]		,    lowercase		,    atol=1e-4					)					)









import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


SCREAMING_SNAKE_CASE__							:      List[str]         =    argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
SCREAMING_SNAKE_CASE__							:      Tuple         =    parser.parse_args()


SCREAMING_SNAKE_CASE__							:      int         =    """cpu"""
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

SCREAMING_SNAKE_CASE__							:      List[Any]         =    """path-to-your-trained-model"""
SCREAMING_SNAKE_CASE__							:      Tuple         =    StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    pipe.to(device)

# to channels last
SCREAMING_SNAKE_CASE__							:      int         =    pipe.unet.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      List[Any]         =    pipe.vae.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      List[str]         =    pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    torch.randn(2, 4, 6_4, 6_4)
SCREAMING_SNAKE_CASE__							:      Any         =    torch.rand(1) * 9_9_9
SCREAMING_SNAKE_CASE__							:      List[Any]         =    torch.randn(2, 7_7, 7_6_8)
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    (sample, timestep, encoder_hidden_status)
try:
  SCREAMING_SNAKE_CASE__							:      Any         =    ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE__							:      str         =    ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    6_6_6
SCREAMING_SNAKE_CASE__							:      Any         =    torch.Generator(device).manual_seed(seed)
SCREAMING_SNAKE_CASE__							:      int         =    {"""generator""": generator}
if args.steps is not None:
  SCREAMING_SNAKE_CASE__							:      Optional[int]         =    args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
  SCREAMING_SNAKE_CASE__							:      Dict         =    pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")

