
import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


A  :			int     				=				logging.get_logger(__name__)  # pylint: disable=invalid-name


def 					__lowerCAmelCase    (    self		,					a__		,					a__		,					a__		,					a__						) ->						Dict:
				__a    							=					image.to(device=a__						)
				__a    							=					image  # take image as image_embeddings
				__a    							=					image_embeddings.unsqueeze(1						)

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				__a							,  __a							,  __a    							=					image_embeddings.shape
				__a    							=					image_embeddings.repeat(1		,					a__		,					1						)
				__a    							=					image_embeddings.view(bs_embed * num_images_per_prompt		,					a__		,					-1						)

				if do_classifier_free_guidance:
								__a    							=					torch.zeros_like(a__						)

								# For classifier free guidance, we need to do two forward passes.
								# Here we concatenate the unconditional and text embeddings into a single batch
								# to avoid doing two forward passes
								__a    							=					torch.cat([uncond_embeddings, image_embeddings]						)

				return image_embeddings







class     __A(				a    ):




				def __init__(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case = None	,  )					->   List[str]:


								'''simple docstring'''




								super().__init__()

								__a    							=					{'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

								__a    							=					decoder_pipe_kwargs.get('''torch_dtype'''	,  _snake_case       ) or prior.dtype

								__a    							=					StableDiffusionImageVariationPipeline.from_pretrained(
								    '''lambdalabs/sd-image-variations-diffusers'''	,  **_snake_case       )

								# replace `_encode_image` method
								__a    							=					types.MethodType(_encode_image	,  self.decoder_pipe       )

								self.register_modules(
								    prior=_snake_case	,  tokenizer=_snake_case	,  text_encoder=_snake_case	,  prior_scheduler=_snake_case	,  )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case = None	,  _snake_case = None	,  )					->   List[Any]:


								'''simple docstring'''




								if text_model_output is None:
												__a    							=					len(_snake_case       ) if isinstance(_snake_case	,  _snake_case       ) else 1
												# get prompt text embeddings
												__a    							=					self.tokenizer(
												    _snake_case	,  padding='''max_length'''	,  max_length=self.tokenizer.model_max_length	,  return_tensors='''pt'''	,  )
												__a    							=					text_inputs.input_ids
												__a    							=					text_inputs.attention_mask.bool().to(_snake_case       )

												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																__a    							=					self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]       )
																logger.warning(
																    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""       )
																__a    							=					text_input_ids[:, : self.tokenizer.model_max_length]

												__a    							=					self.text_encoder(text_input_ids.to(_snake_case       )       )

												__a    							=					text_encoder_output.text_embeds
												__a    							=					text_encoder_output.last_hidden_state

								else:
												__a    							=					text_model_output[0].shape[0]
												__a							,  __a    							=					text_model_output[0], text_model_output[1]
												__a    							=					text_attention_mask

								__a    							=					text_embeddings.repeat_interleave(_snake_case	,  dim=0       )
								__a    							=					text_encoder_hidden_states.repeat_interleave(_snake_case	,  dim=0       )
								__a    							=					text_mask.repeat_interleave(_snake_case	,  dim=0       )

								if do_classifier_free_guidance:
												__a    							=					[''''''] * batch_size

												__a    							=					self.tokenizer(
												    _snake_case	,  padding='''max_length'''	,  max_length=self.tokenizer.model_max_length	,  truncation=_snake_case	,  return_tensors='''pt'''	,  )
												__a    							=					uncond_input.attention_mask.bool().to(_snake_case       )
												__a    							=					self.text_encoder(uncond_input.input_ids.to(_snake_case       )       )

												__a    							=					uncond_embeddings_text_encoder_output.text_embeds
												__a    							=					uncond_embeddings_text_encoder_output.last_hidden_state

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

												__a    							=					uncond_embeddings.shape[1]
												__a    							=					uncond_embeddings.repeat(1	,  _snake_case       )
												__a    							=					uncond_embeddings.view(batch_size * num_images_per_prompt	,  _snake_case       )

												__a    							=					uncond_text_encoder_hidden_states.shape[1]
												__a    							=					uncond_text_encoder_hidden_states.repeat(1	,  _snake_case	,  1       )
												__a    							=					uncond_text_encoder_hidden_states.view(
												    batch_size * num_images_per_prompt	,  _snake_case	,  -1       )
												__a    							=					uncond_text_mask.repeat_interleave(_snake_case	,  dim=0       )

												# done duplicates

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												__a    							=					torch.cat([uncond_embeddings, text_embeddings]       )
												__a    							=					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]       )

												__a    							=					torch.cat([uncond_text_mask, text_mask]       )

								return text_embeddings, text_encoder_hidden_states, text_mask




				@property
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[Any]:


								'''simple docstring'''




								if self.device != torch.device('''meta'''       ) or not hasattr(self.prior	,  '''_hf_hook'''       ):
												return self.device
								for module in self.prior.modules():
												if (
												    hasattr(_snake_case	,  '''_hf_hook'''       )
												    and hasattr(module._hf_hook	,  '''execution_device'''       )
												    and module._hf_hook.execution_device is not None
												):
																return torch.device(module._hf_hook.execution_device       )
								return self.device




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								if latents is None:
												__a    							=					randn_tensor(_snake_case	,  generator=_snake_case	,  device=_snake_case	,  dtype=_snake_case       )
								else:
												if latents.shape != shape:
																raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""       )
												__a    							=					latents.to(_snake_case       )

								__a    							=					latents * scheduler.init_noise_sigma
								return latents




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case = None       )					->   Any:


								'''simple docstring'''




								self.decoder_pipe.to(_snake_case       )
								super().to(_snake_case       )




				@torch.no_grad()
				def __call__(  self	,  _snake_case = None	,  _snake_case = None	,  _snake_case = None	,  _snake_case = 1	,  _snake_case = 25	,  _snake_case = None	,  _snake_case = None	,  _snake_case = None	,  _snake_case = None	,  _snake_case = 4.0	,  _snake_case = 8.0	,  _snake_case = 50	,  _snake_case = 1	,  _snake_case = 0.0	,  _snake_case = "pil"	,  _snake_case = True	,  )					->   Tuple:


								'''simple docstring'''




								if prompt is not None:
												if isinstance(_snake_case	,  _snake_case       ):
																__a    							=					1
												elif isinstance(_snake_case	,  _snake_case       ):
																__a    							=					len(_snake_case       )
												else:
																raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(_snake_case       )}"""       )
								else:
												__a    							=					text_model_output[0].shape[0]

								__a    							=					self._execution_device

								__a    							=					batch_size * num_images_per_prompt

								__a    							=					prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

								__a							,  __a							,  __a    							=					self._encode_prompt(
								    _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case       )

								# prior

								self.prior_scheduler.set_timesteps(_snake_case	,  device=_snake_case       )
								__a    							=					self.prior_scheduler.timesteps

								__a    							=					self.prior.config.embedding_dim

								__a    							=					self.prepare_latents(
								    (batch_size, embedding_dim)	,  text_embeddings.dtype	,  _snake_case	,  _snake_case	,  _snake_case	,  self.prior_scheduler	,  )

								for i, t in enumerate(self.progress_bar(_snake_case       )       ):
												# expand the latents if we are doing classifier free guidance
												__a    							=					torch.cat([prior_latents] * 2       ) if do_classifier_free_guidance else prior_latents

												__a    							=					self.prior(
												    _snake_case	,  timestep=_snake_case	,  proj_embedding=_snake_case	,  encoder_hidden_states=_snake_case	,  attention_mask=_snake_case	,  ).predicted_image_embedding

												if do_classifier_free_guidance:
																__a							,  __a    							=					predicted_image_embedding.chunk(2       )
																__a    							=					predicted_image_embedding_uncond + prior_guidance_scale * (
																    predicted_image_embedding_text - predicted_image_embedding_uncond
																)

												if i + 1 == prior_timesteps_tensor.shape[0]:
																__a    							=					None
												else:
																__a    							=					prior_timesteps_tensor[i + 1]

												__a    							=					self.prior_scheduler.step(
												    _snake_case	,  timestep=_snake_case	,  sample=_snake_case	,  generator=_snake_case	,  prev_timestep=_snake_case	,  ).prev_sample

								__a    							=					self.prior.post_process_latents(_snake_case       )

								__a    							=					prior_latents

								__a    							=					self.decoder_pipe(
								    image=_snake_case	,  height=_snake_case	,  width=_snake_case	,  num_inference_steps=_snake_case	,  guidance_scale=_snake_case	,  generator=_snake_case	,  output_type=_snake_case	,  return_dict=_snake_case	,  num_images_per_prompt=_snake_case	,  eta=_snake_case	,  )
								return output

from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


A  :			int     				=				logging.get_logger(__name__)

A  :			Dict     				=				{
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
					import detectrona







class     __A(				a    ):
				snake_case_		     =					'''layoutlmv2'''




				def __init__(  self	,  _snake_case=30_522	,  _snake_case=768	,  _snake_case=12	,  _snake_case=12	,  _snake_case=3_072	,  _snake_case="gelu"	,  _snake_case=0.1	,  _snake_case=0.1	,  _snake_case=512	,  _snake_case=2	,  _snake_case=0.02	,  _snake_case=1E-12	,  _snake_case=0	,  _snake_case=1_024	,  _snake_case=128	,  _snake_case=32	,  _snake_case=True	,  _snake_case=256	,  _snake_case=64	,  _snake_case=True	,  _snake_case=[7, 7, 256]	,  _snake_case=128	,  _snake_case=128	,  _snake_case=True	,  _snake_case=True	,  _snake_case=False	,  _snake_case=None	,  **_snake_case	,  )					->   Optional[int]:


								'''simple docstring'''




								super().__init__(
								    vocab_size=_snake_case	,  hidden_size=_snake_case	,  num_hidden_layers=_snake_case	,  num_attention_heads=_snake_case	,  intermediate_size=_snake_case	,  hidden_act=_snake_case	,  hidden_dropout_prob=_snake_case	,  attention_probs_dropout_prob=_snake_case	,  max_position_embeddings=_snake_case	,  type_vocab_size=_snake_case	,  initializer_range=_snake_case	,  layer_norm_eps=_snake_case	,  pad_token_id=_snake_case	,  **_snake_case	,  )
								__a    							=					max_ad_position_embeddings
								__a    							=					max_rel_pos
								__a    							=					rel_pos_bins
								__a    							=					fast_qkv
								__a    							=					max_rel_ad_pos
								__a    							=					rel_ad_pos_bins
								__a    							=					convert_sync_batchnorm
								__a    							=					image_feature_pool_shape
								__a    							=					coordinate_size
								__a    							=					shape_size
								__a    							=					has_relative_attention_bias
								__a    							=					has_spatial_attention_bias
								__a    							=					has_visual_segment_embedding
								__a    							=					(
								    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
								)




				@classmethod
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Dict:


								'''simple docstring'''




								return {
								    "MODEL.MASK_ON": True,
								    "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
								    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
								    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
								    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
								    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
								    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_000,
								    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_000,
								    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_000,
								    "MODEL.POST_NMS_TOPK_TEST": 1_000,
								    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
								    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
								    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
								    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
								    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
								    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
								    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
								    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
								    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
								    "MODEL.RESNETS.DEPTH": 101,
								    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
								    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
								    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
								    "MODEL.RESNETS.NUM_GROUPS": 32,
								    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
								    "MODEL.RESNETS.STRIDE_IN_1X1": False,
								}




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


								'''simple docstring'''




								__a    							=					detectrona.config.get_cfg()
								for k, v in self.detectrona_config_args.items():
												__a    							=					k.split('''.'''       )
												__a    							=					detectrona_config
												for attribute in attributes[:-1]:
																__a    							=					getattr(_snake_case	,  _snake_case       )
												setattr(_snake_case	,  attributes[-1]	,  _snake_case       )

								return detectrona_config