
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


lowercase__				:				Dict						=      logging.get_logger(__name__)

lowercase__				:				List[Any]						=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase__				:				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"
        ),
    },
}

lowercase__				:				int						=      {
    "microsoft/layoutlm-base-uncased": 5_1_2,
    "microsoft/layoutlm-large-uncased": 5_1_2,
}

lowercase__				:				Tuple						=      {
    "microsoft/layoutlm-base-uncased": {"do_lower_case": True},
    "microsoft/layoutlm-large-uncased": {"do_lower_case": True},
}






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	VOCAB_FILES_NAMES
							_snake_case  		=	PRETRAINED_VOCAB_FILES_MAP
							_snake_case  		=	PRETRAINED_INIT_CONFIGURATION
							_snake_case  		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_snake_case  		=	LayoutLMTokenizer



							def __init__(	self  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_="[UNK]"  ,						SCREAMING_SNAKE_CASE_="[SEP]"  ,						SCREAMING_SNAKE_CASE_="[PAD]"  ,						SCREAMING_SNAKE_CASE_="[CLS]"  ,						SCREAMING_SNAKE_CASE_="[MASK]"  ,						SCREAMING_SNAKE_CASE_=True  ,						SCREAMING_SNAKE_CASE_=None  ,						**SCREAMING_SNAKE_CASE_  ,						)-> Tuple:





													'''simple docstring'''






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

													__UpperCamelCase  	=      json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
													if (
													    normalizer_state.get('''lowercase'''  ,						SCREAMING_SNAKE_CASE_      ) != do_lower_case
													    or normalizer_state.get('''strip_accents'''  ,						SCREAMING_SNAKE_CASE_      ) != strip_accents
													    or normalizer_state.get('''handle_chinese_chars'''  ,						SCREAMING_SNAKE_CASE_      ) != tokenize_chinese_chars
													):
																			__UpperCamelCase  	=      getattr(SCREAMING_SNAKE_CASE_  ,						normalizer_state.pop('''type'''      )      )
																			__UpperCamelCase  	=      do_lower_case
																			__UpperCamelCase  	=      strip_accents
																			__UpperCamelCase  	=      tokenize_chinese_chars
																			__UpperCamelCase  	=      normalizer_class(**SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      do_lower_case



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=None      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      [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      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None      )-> List[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      [self.sep_token_id]
													__UpperCamelCase  	=      [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      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None      )-> Tuple[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      self._tokenizer.model.save(SCREAMING_SNAKE_CASE_  ,						name=SCREAMING_SNAKE_CASE_      )
													return tuple(SCREAMING_SNAKE_CASE_      )






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


lowercase__				:				List[str]						=      False






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

							"""simple docstring"""






							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

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

													__UpperCamelCase  	=      torch.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt='''first prompt'''  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=2  ,						output_type='''numpy'''  ,						).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																			pipe.save_pretrained(SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained(SCREAMING_SNAKE_CASE_      )

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

													__UpperCamelCase  	=      generator.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt='''first prompt'''  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						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      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
													pipe.remove_unused_weights()
													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      '''cyberpunk 2077'''
													__UpperCamelCase  	=      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''      )
													__UpperCamelCase  	=      torch.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt=SCREAMING_SNAKE_CASE_  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=50  ,						output_type='''numpy'''  ,						).images

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

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase  	=      np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]      )

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




