



"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class       UpperCamelCase__			(   _lowerCAmelCase       ,		_lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     IFInpaintingPipeline
			A__	:					Dict							  =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
			A__	:					List[Any]							  =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			A__	:					Optional[int]							  =     PipelineTesterMixin.required_optional_params - {"latents"}


			def   snake_case__	(      self		)      ->	str:
				return self._get_dummy_components()


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=0		)      ->	Tuple:
				if str(SCREAMING_SNAKE_CASE__		).startswith("mps"		):
					A__											=					torch.manual_seed(SCREAMING_SNAKE_CASE__		)
				else:
					A__											=					torch.Generator(device=SCREAMING_SNAKE_CASE__		).manual_seed(SCREAMING_SNAKE_CASE__		)

				A__											=					floats_tensor((1, 3, 32, 32)						,	rng=random.Random(SCREAMING_SNAKE_CASE__		)		).to(SCREAMING_SNAKE_CASE__		)
				A__											=					floats_tensor((1, 3, 32, 32)						,	rng=random.Random(SCREAMING_SNAKE_CASE__		)		).to(SCREAMING_SNAKE_CASE__		)

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

				return inputs


			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()						,	reason="XFormers attention is only available with CUDA and `xformers` installed"						,	)
			def   snake_case__	(      self		)      ->	Any:
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)


			def   snake_case__	(      self		)      ->	Tuple:
				self._test_save_load_optional_components()


			@unittest.skipIf(torch_device != "cuda"						,	reason="float16 requires CUDA"		)
			def   snake_case__	(      self		)      ->	List[str]:
				# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
				super().test_save_load_floataa(expected_max_diff=1e-1		)


			def   snake_case__	(      self		)      ->	Any:
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)


			def   snake_case__	(      self		)      ->	Any:
				self._test_save_load_local()







			def   snake_case__	(      self		)      ->	Optional[int]:
				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 .tokenization_electra import ElectraTokenizer


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

UpperCamelCase     =							{
    """vocab_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt""",
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-generator""": (
            """https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCamelCase     =							{
    """google/electra-small-generator""": 512,
    """google/electra-base-generator""": 512,
    """google/electra-large-generator""": 512,
    """google/electra-small-discriminator""": 512,
    """google/electra-base-discriminator""": 512,
    """google/electra-large-discriminator""": 512,
}

UpperCamelCase     =							{
    """google/electra-small-generator""": {"""do_lower_case""": True},
    """google/electra-base-generator""": {"""do_lower_case""": True},
    """google/electra-large-generator""": {"""do_lower_case""": True},
    """google/electra-small-discriminator""": {"""do_lower_case""": True},
    """google/electra-base-discriminator""": {"""do_lower_case""": True},
    """google/electra-large-discriminator""": {"""do_lower_case""": True},
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Dict							  =     VOCAB_FILES_NAMES
			A__	:					str							  =     PRETRAINED_VOCAB_FILES_MAP
			A__	:					List[Any]							  =     PRETRAINED_INIT_CONFIGURATION
			A__	:					Any							  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A__	:					List[str]							  =     ElectraTokenizer


			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__						,	)      ->	str:
				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__						,	)

				A__											=					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
				):
					A__											=					getattr(SCREAMING_SNAKE_CASE__						,	normalizer_state.pop("type"		)		)
					A__											=					do_lower_case
					A__											=					strip_accents
					A__											=					tokenize_chinese_chars
					A__											=					normalizer_class(**SCREAMING_SNAKE_CASE__		)

				A__											=					do_lower_case


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=None		)      ->	List[str]:
				A__											=					[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   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None		)      ->	List[int]:
				A__											=					[self.sep_token_id]
				A__											=					[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   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None		)      ->	Tuple[str]:
				A__											=					self._tokenizer.model.save(SCREAMING_SNAKE_CASE__						,	name=SCREAMING_SNAKE_CASE__		)
				return tuple(SCREAMING_SNAKE_CASE__		)




