




import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np




class    __lowerCAmelCase       :






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         torch.manual_seed(0				)
         lowercase    :Union[str, Any]      	=						TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"				)

         torch.manual_seed(0				)
         lowercase    :List[str]      	=						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"				)

         torch.manual_seed(0				)
         lowercase    :str      	=						UNetaDConditionModel(
             sample_size=32       ,					layers_per_block=1       ,					block_out_channels=[32, 64]       ,					down_block_types=[
                 "ResnetDownsampleBlock2D",
                 "SimpleCrossAttnDownBlock2D",
             ]       ,					mid_block_type="UNetMidBlock2DSimpleCrossAttn"       ,					up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]       ,					in_channels=3       ,					out_channels=6       ,					cross_attention_dim=32       ,					encoder_hid_dim=32       ,					attention_head_dim=8       ,					addition_embed_type="text"       ,					addition_embed_type_num_heads=2       ,					cross_attention_norm="group_norm"       ,					resnet_time_scale_shift="scale_shift"       ,					act_fn="gelu"       ,					)
         unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

         torch.manual_seed(0				)
         lowercase    :Optional[int]      	=						DDPMScheduler(
             num_train_timesteps=10_00       ,					beta_schedule="squaredcos_cap_v2"       ,					beta_start=0.00_01       ,					beta_end=0.02       ,					thresholding=_lowerCAmelCase       ,					dynamic_thresholding_ratio=0.95       ,					sample_max_value=1.0       ,					prediction_type="epsilon"       ,					variance_type="learned_range"       ,					)

         torch.manual_seed(0				)
         lowercase    :Optional[Any]      	=						IFWatermarker()

         return {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "watermarker": watermarker,
             "safety_checker": None,
             "feature_extractor": None,
         }






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         torch.manual_seed(0				)
         lowercase    :Optional[Any]      	=						TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"				)

         torch.manual_seed(0				)
         lowercase    :int      	=						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"				)

         torch.manual_seed(0				)
         lowercase    :Dict      	=						UNetaDConditionModel(
             sample_size=32       ,					layers_per_block=[1, 2]       ,					block_out_channels=[32, 64]       ,					down_block_types=[
                 "ResnetDownsampleBlock2D",
                 "SimpleCrossAttnDownBlock2D",
             ]       ,					mid_block_type="UNetMidBlock2DSimpleCrossAttn"       ,					up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]       ,					in_channels=6       ,					out_channels=6       ,					cross_attention_dim=32       ,					encoder_hid_dim=32       ,					attention_head_dim=8       ,					addition_embed_type="text"       ,					addition_embed_type_num_heads=2       ,					cross_attention_norm="group_norm"       ,					resnet_time_scale_shift="scale_shift"       ,					act_fn="gelu"       ,					class_embed_type="timestep"       ,					mid_block_scale_factor=1.4_14       ,					time_embedding_act_fn="gelu"       ,					time_embedding_dim=32       ,					)
         unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

         torch.manual_seed(0				)
         lowercase    :Optional[int]      	=						DDPMScheduler(
             num_train_timesteps=10_00       ,					beta_schedule="squaredcos_cap_v2"       ,					beta_start=0.00_01       ,					beta_end=0.02       ,					thresholding=_lowerCAmelCase       ,					dynamic_thresholding_ratio=0.95       ,					sample_max_value=1.0       ,					prediction_type="epsilon"       ,					variance_type="learned_range"       ,					)

         torch.manual_seed(0				)
         lowercase    :List[str]      	=						DDPMScheduler(
             num_train_timesteps=10_00       ,					beta_schedule="squaredcos_cap_v2"       ,					beta_start=0.00_01       ,					beta_end=0.02       ,					)

         torch.manual_seed(0				)
         lowercase    :int      	=						IFWatermarker()

         return {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "image_noising_scheduler": image_noising_scheduler,
             "watermarker": watermarker,
             "safety_checker": None,
             "feature_extractor": None,
         }






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :List[str]      	=						self.get_dummy_components()
         lowercase    :int      	=						self.pipeline_class(**_lowerCAmelCase				)
         pipe.to(_lowerCAmelCase				)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase				)

         lowercase    :Any      	=						self.get_dummy_inputs(_lowerCAmelCase				)

         lowercase    :Dict      	=						inputs["prompt"]
         lowercase    :str      	=						inputs["generator"]
         lowercase    :Union[str, Any]      	=						inputs["num_inference_steps"]
         lowercase    :Union[str, Any]      	=						inputs["output_type"]

         if "image" in inputs:
           lowercase    :Union[str, Any]      	=						inputs["image"]
         else:
           lowercase    :List[Any]      	=						None

         if "mask_image" in inputs:
           lowercase    :Optional[Any]      	=						inputs["mask_image"]
         else:
           lowercase    :Tuple      	=						None

         if "original_image" in inputs:
           lowercase    :Optional[int]      	=						inputs["original_image"]
         else:
           lowercase    :Optional[int]      	=						None

         lowercase	,    lowercase    :Dict      	=						pipe.encode_prompt(_lowerCAmelCase				)

         # inputs with prompt converted to embeddings
         lowercase    :Any      	=						{
             "prompt_embeds": prompt_embeds,
             "negative_prompt_embeds": negative_prompt_embeds,
             "generator": generator,
             "num_inference_steps": num_inference_steps,
             "output_type": output_type,
         }

         if image is not None:
           lowercase    :Union[str, Any]      	=						image

         if mask_image is not None:
           lowercase    :Dict      	=						mask_image

         if original_image is not None:
           lowercase    :str      	=						original_image

         # set all optional components to None
         for optional_component in pipe._optional_components:
           setattr(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				)

         lowercase    :Optional[int]      	=						pipe(**_lowerCAmelCase				)[0]

         with tempfile.TemporaryDirectory() as tmpdir:
           pipe.save_pretrained(_lowerCAmelCase				)
           lowercase    :List[Any]      	=						self.pipeline_class.from_pretrained(_lowerCAmelCase				)
           pipe_loaded.to(_lowerCAmelCase				)
           pipe_loaded.set_progress_bar_config(disable=_lowerCAmelCase				)

         pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

         for optional_component in pipe._optional_components:
           self.assertTrue(
               getattr(_lowerCAmelCase       ,					_lowerCAmelCase				) is None       ,					F"`{optional_component}` did not stay set to None after loading."       ,					)

         lowercase    :List[str]      	=						self.get_dummy_inputs(_lowerCAmelCase				)

         lowercase    :Optional[Any]      	=						inputs["generator"]
         lowercase    :Union[str, Any]      	=						inputs["num_inference_steps"]
         lowercase    :str      	=						inputs["output_type"]

         # inputs with prompt converted to embeddings
         lowercase    :Tuple      	=						{
             "prompt_embeds": prompt_embeds,
             "negative_prompt_embeds": negative_prompt_embeds,
             "generator": generator,
             "num_inference_steps": num_inference_steps,
             "output_type": output_type,
         }

         if image is not None:
           lowercase    :int      	=						image

         if mask_image is not None:
           lowercase    :Dict      	=						mask_image

         if original_image is not None:
           lowercase    :str      	=						original_image

         lowercase    :Union[str, Any]      	=						pipe_loaded(**_lowerCAmelCase				)[0]

         lowercase    :Optional[Any]      	=						np.abs(to_np(_lowerCAmelCase				) - to_np(_lowerCAmelCase				)				).max()
         self.assertLess(_lowerCAmelCase       ,					1e-4				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         lowercase    :Union[str, Any]      	=						self.get_dummy_components()
         lowercase    :Tuple      	=						self.pipeline_class(**_lowerCAmelCase				)
         pipe.to(_lowerCAmelCase				)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase				)

         lowercase    :List[str]      	=						self.get_dummy_inputs(_lowerCAmelCase				)
         lowercase    :Optional[int]      	=						pipe(**_lowerCAmelCase				)[0]

         with tempfile.TemporaryDirectory() as tmpdir:
           pipe.save_pretrained(_lowerCAmelCase				)
           lowercase    :Any      	=						self.pipeline_class.from_pretrained(_lowerCAmelCase				)
           pipe_loaded.to(_lowerCAmelCase				)
           pipe_loaded.set_progress_bar_config(disable=_lowerCAmelCase				)

         pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

         lowercase    :Tuple      	=						self.get_dummy_inputs(_lowerCAmelCase				)
         lowercase    :List[str]      	=						pipe_loaded(**_lowerCAmelCase				)[0]

         lowercase    :List[str]      	=						np.abs(to_np(_lowerCAmelCase				) - to_np(_lowerCAmelCase				)				).max()
         self.assertLess(_lowerCAmelCase       ,					1e-4				)









import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class    __lowerCAmelCase       (							lowerCAmelCase):
       _a 					=						['''image_processor''', '''tokenizer''']
       _a 					=						'''LayoutLMv2ImageProcessor'''
       _a 					=						('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






       def __init__(   self:					int       ,					_lowerCAmelCase:					Any=None       ,					_lowerCAmelCase:					List[Any]=None       ,					**_lowerCAmelCase:					Optional[Any]				):
         if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."       ,					_lowerCAmelCase       ,					)
           lowercase    :Optional[Any]      	=						kwargs.pop("feature_extractor"				)

         lowercase    :Union[str, Any]      	=						image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."				)
         if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."				)

         super().__init__(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def __call__(   self:					List[str]       ,					_lowerCAmelCase:					Optional[Any]       ,					_lowerCAmelCase:					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,					_lowerCAmelCase:					Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,					_lowerCAmelCase:					Union[List[List[int]], List[List[List[int]]]] = None       ,					_lowerCAmelCase:					Optional[Union[List[int], List[List[int]]]] = None       ,					_lowerCAmelCase:					bool = True       ,					_lowerCAmelCase:					Union[bool, str, PaddingStrategy] = False       ,					_lowerCAmelCase:					Union[bool, str, TruncationStrategy] = None       ,					_lowerCAmelCase:					Optional[int] = None       ,					_lowerCAmelCase:					int = 0       ,					_lowerCAmelCase:					Optional[int] = None       ,					_lowerCAmelCase:					Optional[bool] = None       ,					_lowerCAmelCase:					Optional[bool] = None       ,					_lowerCAmelCase:					bool = False       ,					_lowerCAmelCase:					bool = False       ,					_lowerCAmelCase:					bool = False       ,					_lowerCAmelCase:					bool = False       ,					_lowerCAmelCase:					bool = True       ,					_lowerCAmelCase:					Optional[Union[str, TensorType]] = None       ,					**_lowerCAmelCase:					Tuple       ,					):
         # verify input
         if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes "
               "if you initialized the image processor with apply_ocr set to True."				)

         if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."				)

         if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."				)

         # first, apply the image processor
         lowercase    :int      	=						self.image_processor(images=_lowerCAmelCase       ,					return_tensors=_lowerCAmelCase				)

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(_lowerCAmelCase       ,					_lowerCAmelCase				):
             lowercase    :int      	=						[text]  # add batch dimension (as the image processor always adds a batch dimension)
           lowercase    :int      	=						features["words"]

         lowercase    :Optional[Any]      	=						self.tokenizer(
             text=text if text is not None else features["words"]       ,					text_pair=text_pair if text_pair is not None else None       ,					boxes=boxes if boxes is not None else features["boxes"]       ,					word_labels=_lowerCAmelCase       ,					add_special_tokens=_lowerCAmelCase       ,					padding=_lowerCAmelCase       ,					truncation=_lowerCAmelCase       ,					max_length=_lowerCAmelCase       ,					stride=_lowerCAmelCase       ,					pad_to_multiple_of=_lowerCAmelCase       ,					return_token_type_ids=_lowerCAmelCase       ,					return_attention_mask=_lowerCAmelCase       ,					return_overflowing_tokens=_lowerCAmelCase       ,					return_special_tokens_mask=_lowerCAmelCase       ,					return_offsets_mapping=_lowerCAmelCase       ,					return_length=_lowerCAmelCase       ,					verbose=_lowerCAmelCase       ,					return_tensors=_lowerCAmelCase       ,					**_lowerCAmelCase       ,					)

         # add pixel values
         lowercase    :Any      	=						features.pop("pixel_values"				)
         if return_overflowing_tokens is True:
           lowercase    :Any      	=						self.get_overflowing_images(_lowerCAmelCase       ,					encoded_inputs["overflow_to_sample_mapping"]				)
         lowercase    :Optional[int]      	=						images

         return encoded_inputs






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple       ,					_lowerCAmelCase:					Tuple       ,					_lowerCAmelCase:					List[Any]				):
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         lowercase    :Dict      	=						[]
         for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]				)

         if len(_lowerCAmelCase				) != len(_lowerCAmelCase				):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F" {len(_lowerCAmelCase				)} and {len(_lowerCAmelCase				)}"				)

         return images_with_overflow






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict       ,					*_lowerCAmelCase:					str       ,					**_lowerCAmelCase:					List[Any]				):
         return self.tokenizer.batch_decode(*_lowerCAmelCase       ,					**_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str       ,					*_lowerCAmelCase:					Tuple       ,					**_lowerCAmelCase:					Union[str, Any]				):
         return self.tokenizer.decode(*_lowerCAmelCase       ,					**_lowerCAmelCase				)






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         return ["input_ids", "bbox", "attention_mask", "image"]






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         warnings.warn(
             "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."       ,					_lowerCAmelCase       ,					)
         return self.image_processor_class






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         warnings.warn(
             "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,					_lowerCAmelCase       ,					)
         return self.image_processor



