
'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class    A     (	snake_case__  ,      snake_case__  ,      unittest.TestCase    ):
	snake_case__      :Optional[int]        =     IFImgaImgPipeline
	snake_case__      :str        =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
	snake_case__      :Any        =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	snake_case__      :int        =     PipelineTesterMixin.required_optional_params - {'latents'}





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]							):


				"""simple docstring"""

				return self._get_dummy_components()





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							__magic_name__			:       int      ,							__magic_name__			:       List[Any]=0							):


				"""simple docstring"""

				if str(__snake_case							).startswith("mps"							):
							lowerCAmelCase__								=							torch.manual_seed(__snake_case							)
				else:
							lowerCAmelCase__								=							torch.Generator(device=__snake_case							).manual_seed(__snake_case							)

				lowerCAmelCase__								=							floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(__snake_case							)							).to(__snake_case							)

				lowerCAmelCase__								=							{
				    "prompt": "A painting of a squirrel eating a burger",
				    "image": image,
				    "generator": generator,
				    "num_inference_steps": 2,
				    "output_type": "numpy",
				}

				return inputs





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]							):


				"""simple docstring"""

				self._test_save_load_optional_components()





	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()      ,							reason="XFormers attention is only available with CUDA and `xformers` installed"      ,							)
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any							):


				"""simple docstring"""

				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)





	@unittest.skipIf(torch_device != "cuda"      ,							reason="float16 requires CUDA"							)
	def 							__SCREAMING_SNAKE_CASE  (					self			:       str							):


				"""simple docstring"""

				super().test_save_load_floataa(expected_max_diff=1E-1							)





	@unittest.skipIf(torch_device != "cuda"      ,							reason="float16 requires CUDA"							)
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]							):


				"""simple docstring"""

				super().test_floataa_inference(expected_max_diff=1E-1							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""simple docstring"""

				self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict							):


				"""simple docstring"""

				self._test_save_load_local()







	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]							):


				"""simple docstring"""

				self._test_inference_batch_single_identical(
				    expected_max_diff=1E-2      ,							)





'''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__   : Optional[Any]					=		logging.get_logger(__name__)

UpperCAmelCase__   : str					=		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__   : Union[str, Any]					=		{
    "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": 5_12,
    "microsoft/layoutlm-large-uncased": 5_12,
}

UpperCAmelCase__   : List[str]					=		{
    "microsoft/layoutlm-base-uncased": {"do_lower_case": True},
    "microsoft/layoutlm-large-uncased": {"do_lower_case": True},
}






class    A     (	SCREAMING_SNAKE_CASE__    ):
	snake_case__      :Union[str, Any]        =     VOCAB_FILES_NAMES
	snake_case__      :List[str]        =     PRETRAINED_VOCAB_FILES_MAP
	snake_case__      :Union[str, Any]        =     PRETRAINED_INIT_CONFIGURATION
	snake_case__      :int        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__      :int        =     LayoutLMTokenizer





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


				"""simple docstring"""

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

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

				lowerCAmelCase__								=							do_lower_case





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]      ,							__magic_name__			:       Optional[int]      ,							__magic_name__			:       List[str]=None							):


				"""simple docstring"""

				lowerCAmelCase__								=							[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 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple      ,							__magic_name__			:       List[int]      ,							__magic_name__			:       Optional[List[int]] = None							):


				"""simple docstring"""

				lowerCAmelCase__								=							[self.sep_token_id]
				lowerCAmelCase__								=							[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 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							__magic_name__			:       str      ,							__magic_name__			:       Optional[str] = None							):


				"""simple docstring"""

				lowerCAmelCase__								=							self._tokenizer.model.save(__magic_name__      ,							name=__magic_name__							)
				return tuple(__magic_name__							)





