





'''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	):

    '''simple docstring'''
    UpperCamelCase			   =			IFImgaImgPipeline
    UpperCamelCase			   =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
    UpperCamelCase			   =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    UpperCamelCase			   =			PipelineTesterMixin.required_optional_params - {'''latents'''}


    def       snake_case_(		self	)		->       List[str]:
        return self._get_dummy_components()


    def       snake_case_(		self			,		A			,		A=0	)		->       Union[str, Any]:
        if str(A	).startswith("""mps"""	):
            _SCREAMING_SNAKE_CASE     =      torch.manual_seed(A	)
        else:
            _SCREAMING_SNAKE_CASE     =      torch.Generator(device=A	).manual_seed(A	)

        _SCREAMING_SNAKE_CASE     =      floats_tensor((1, 3, 32, 32)			,		rng=random.Random(A	)	).to(A	)

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

        return inputs


    def       snake_case_(		self	)		->       Tuple:
        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       snake_case_(		self	)		->       Any:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)


    @unittest.skipIf(torch_device != """cuda"""			,		reason="""float16 requires CUDA"""	)
    def       snake_case_(		self	)		->       Dict:
        # 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	)


    @unittest.skipIf(torch_device != """cuda"""			,		reason="""float16 requires CUDA"""	)
    def       snake_case_(		self	)		->       Union[str, Any]:
        super().test_floataa_inference(expected_max_diff=1e-1	)


    def       snake_case_(		self	)		->       str:
        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)


    def       snake_case_(		self	)		->       Union[str, 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'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_         =			logging.get_logger(__name__)

lowercase_         =			{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			'''rembert'''


    def __init__(		self			,		A=25_0300			,		A=1152			,		A=32			,		A=18			,		A=256			,		A=1664			,		A=4608			,		A="gelu"			,		A=0.0			,		A=0.0			,		A=0.1			,		A=512			,		A=2			,		A=0.02			,		A=1e-12			,		A=True			,		A=0			,		A=312			,		A=313			,		**A			,		)		->       Dict:
        super().__init__(pad_token_id=A			,		bos_token_id=A			,		eos_token_id=A			,		**A	)

        _SCREAMING_SNAKE_CASE     =      vocab_size
        _SCREAMING_SNAKE_CASE     =      input_embedding_size
        _SCREAMING_SNAKE_CASE     =      output_embedding_size
        _SCREAMING_SNAKE_CASE     =      max_position_embeddings
        _SCREAMING_SNAKE_CASE     =      hidden_size
        _SCREAMING_SNAKE_CASE     =      num_hidden_layers
        _SCREAMING_SNAKE_CASE     =      num_attention_heads
        _SCREAMING_SNAKE_CASE     =      intermediate_size
        _SCREAMING_SNAKE_CASE     =      hidden_act
        _SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
        _SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE     =      classifier_dropout_prob
        _SCREAMING_SNAKE_CASE     =      initializer_range
        _SCREAMING_SNAKE_CASE     =      type_vocab_size
        _SCREAMING_SNAKE_CASE     =      layer_norm_eps
        _SCREAMING_SNAKE_CASE     =      use_cache
        _SCREAMING_SNAKE_CASE     =      False




class  a_					(    snake_case_	):

    '''simple docstring'''


    @property
    def       snake_case_(		self	)		->       Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]	)





    @property
    def       snake_case_(		self	)		->       float:
        return 1e-4



