"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


UpperCAmelCase       :    Dict									=			logging.get_logger(__name__)

UpperCAmelCase       :    int									=			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase       :    Tuple									=			{
    "vocab_file": {
        "microsoft/layoutlm-base-uncased": (
            "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"
        ),
        "microsoft/layoutlm-large-uncased": (
            "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "microsoft/layoutlm-base-uncased": (
            "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json"
        ),
        "microsoft/layoutlm-large-uncased": (
            "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase       :    List[Any]									=			{
    "microsoft/layoutlm-base-uncased": 512,
    "microsoft/layoutlm-large-uncased": 512,
}

UpperCAmelCase       :    Dict									=			{
    "microsoft/layoutlm-base-uncased": {"do_lower_case": True},
    "microsoft/layoutlm-large-uncased": {"do_lower_case": True},
}

class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):
				UpperCAmelCase	=      VOCAB_FILES_NAMES
				UpperCAmelCase	=      PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase	=      PRETRAINED_INIT_CONFIGURATION
				UpperCAmelCase	=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase	=      LayoutLMTokenizer







				def __init__(    self    :List[str]      ,__UpperCAmelCase    :Tuple=None      ,__UpperCAmelCase    :int=None      ,__UpperCAmelCase    :str=True      ,__UpperCAmelCase    :str="[UNK]"      ,__UpperCAmelCase    :List[Any]="[SEP]"      ,__UpperCAmelCase    :str="[PAD]"      ,__UpperCAmelCase    :Optional[int]="[CLS]"      ,__UpperCAmelCase    :Tuple="[MASK]"      ,__UpperCAmelCase    :str=True      ,__UpperCAmelCase    :Dict=None      ,**__UpperCAmelCase    :str      ,)      ->					Dict:




									"""simple docstring"""



									super().__init__(
									    UpperCAmelCase__      ,tokenizer_file=UpperCAmelCase__      ,do_lower_case=UpperCAmelCase__      ,unk_token=UpperCAmelCase__      ,sep_token=UpperCAmelCase__      ,pad_token=UpperCAmelCase__      ,cls_token=UpperCAmelCase__      ,mask_token=UpperCAmelCase__      ,tokenize_chinese_chars=UpperCAmelCase__      ,strip_accents=UpperCAmelCase__      ,**UpperCAmelCase__      ,)

									lowerCamelCase__		:   Any  				=					json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
									if (
									    normalizer_state.get('''lowercase'''      ,UpperCAmelCase__  ) != do_lower_case
									    or normalizer_state.get('''strip_accents'''      ,UpperCAmelCase__  ) != strip_accents
									    or normalizer_state.get('''handle_chinese_chars'''      ,UpperCAmelCase__  ) != tokenize_chinese_chars
									):
														lowerCamelCase__		:   List[Any]  				=					getattr(UpperCAmelCase__      ,normalizer_state.pop('''type'''  )  )
														lowerCamelCase__		:   str  				=					do_lower_case
														lowerCamelCase__		:   Union[str, Any]  				=					strip_accents
														lowerCamelCase__		:   str  				=					tokenize_chinese_chars
														lowerCamelCase__		:   int  				=					normalizer_class(**UpperCAmelCase__  )

									lowerCamelCase__		:   Optional[Any]  				=					do_lower_case







				def   lowercase_		(    self    :List[str]      ,__UpperCAmelCase    :Any      ,__UpperCAmelCase    :Optional[Any]=None  )      ->					int:




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
														output += token_ids_a + [self.sep_token_id]

									return output







				def   lowercase_		(    self    :str      ,__UpperCAmelCase    :Any      ,__UpperCAmelCase    :List[Any] = None  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[int]  				=					[self.sep_token_id]
									lowerCamelCase__		:   Optional[Any]  				=					[self.cls_token_id]
									if token_ids_a is None:
														return len(cls + token_ids_a + sep  ) * [0]
									return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







				def   lowercase_		(    self    :Any      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :str = None  )      ->					Any:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					self._tokenizer.model.save(UpperCAmelCase__      ,name=UpperCAmelCase__  )
									return tuple(UpperCAmelCase__  )










"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCamelCase  =	False


@nightly
@require_torch_gpu
class       lowerCamelCase_		(      unittest.TestCase ):





				"""simple docstring"""







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



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )

								SCREAMING_SNAKE_CASE__  				=					torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					pipe(
								    prompt="first prompt"							,		image=UpperCAmelCase__							,		text_to_image_strength=0.75							,		generator=UpperCAmelCase__							,		guidance_scale=7.5							,		num_inference_steps=2							,		output_type="numpy"							,		).images

								with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(UpperCAmelCase__  )
												SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCAmelCase__  )

								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					generator.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					pipe(
								    prompt="first prompt"							,		image=UpperCAmelCase__							,		text_to_image_strength=0.75							,		generator=UpperCAmelCase__							,		guidance_scale=7.5							,		num_inference_steps=2							,		output_type="numpy"							,		).images

								assert np.abs(image - new_image  ).sum() < 1e-5, "Models don't have the same forward pass"






				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
								pipe.remove_unused_weights()
								pipe.to(UpperCAmelCase__  )
								pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

								SCREAMING_SNAKE_CASE__  				=					"cyberpunk 2077"
								SCREAMING_SNAKE_CASE__  				=					load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )
								SCREAMING_SNAKE_CASE__  				=					torch.manual_seed(0  )
								SCREAMING_SNAKE_CASE__  				=					pipe(
								    prompt=UpperCAmelCase__							,		image=UpperCAmelCase__							,		text_to_image_strength=0.75							,		generator=UpperCAmelCase__							,		guidance_scale=7.5							,		num_inference_steps=50							,		output_type="numpy"							,		).images

								SCREAMING_SNAKE_CASE__  				=					image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								SCREAMING_SNAKE_CASE__  				=					np.array([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
