"""simple docstring"""


import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class    __UpperCAmelCase						(	__lowerCAmelCase      ,      unittest.TestCase	):
				A__      :   Any  							=       KandinskyPriorPipeline
				A__      :   int  							=       ['''prompt''']
				A__      :   List[Any]  							=       ['''prompt''', '''negative_prompt''']
				A__      :   Union[str, Any]  							=       [
				    '''num_images_per_prompt''',
				    '''generator''',
				    '''num_inference_steps''',
				    '''latents''',
				    '''negative_prompt''',
				    '''guidance_scale''',
				    '''output_type''',
				    '''return_dict''',
				]
				A__      :   Optional[int]  							=       False

				@property
				def 		_a	(      self      ):
										return 32

				@property
				def 		_a	(      self      ):
										return 32

				@property
				def 		_a	(      self      ):
										return self.time_input_dim

				@property
				def 		_a	(      self      ):
										return self.time_input_dim * 4

				@property
				def 		_a	(      self      ):
										return 100

				@property
				def 		_a	(      self      ):
										lowerCamelCase__					=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )
										return tokenizer

				@property
				def 		_a	(      self      ):
										torch.manual_seed(0      )
										lowerCamelCase__					=CLIPTextConfig(
										    bos_token_id=0	, eos_token_id=2	, hidden_size=self.text_embedder_hidden_size	, projection_dim=self.text_embedder_hidden_size	, intermediate_size=37	, layer_norm_eps=1E-05	, num_attention_heads=4	, num_hidden_layers=5	, pad_token_id=1	, vocab_size=1000	, )
										return CLIPTextModelWithProjection(_lowerCamelCase      )

				@property
				def 		_a	(      self      ):
										torch.manual_seed(0      )

										lowerCamelCase__					={
										    "num_attention_heads": 2,
										    "attention_head_dim": 12,
										    "embedding_dim": self.text_embedder_hidden_size,
										    "num_layers": 1,
										}

										lowerCamelCase__					=PriorTransformer(**_lowerCamelCase      )
										# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
										lowerCamelCase__					=nn.Parameter(torch.ones(model.clip_std.shape      )      )
										return model

				@property
				def 		_a	(      self      ):
										torch.manual_seed(0      )
										lowerCamelCase__					=CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size	, image_size=224	, projection_dim=self.text_embedder_hidden_size	, intermediate_size=37	, num_attention_heads=4	, num_channels=3	, num_hidden_layers=5	, patch_size=14	, )

										lowerCamelCase__					=CLIPVisionModelWithProjection(_lowerCamelCase      )
										return model

				@property
				def 		_a	(      self      ):
										lowerCamelCase__					=CLIPImageProcessor(
										    crop_size=224	, do_center_crop=_lowerCamelCase	, do_normalize=_lowerCamelCase	, do_resize=_lowerCamelCase	, image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]	, image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]	, resample=3	, size=224	, )

										return image_processor

				def 		_a	(      self      ):
										lowerCamelCase__					=self.dummy_prior
										lowerCamelCase__					=self.dummy_image_encoder
										lowerCamelCase__					=self.dummy_text_encoder
										lowerCamelCase__					=self.dummy_tokenizer
										lowerCamelCase__					=self.dummy_image_processor

										lowerCamelCase__					=UnCLIPScheduler(
										    variance_type="fixed_small_log"	, prediction_type="sample"	, num_train_timesteps=1000	, clip_sample=_lowerCamelCase	, clip_sample_range=1_0.0	, )

										lowerCamelCase__					={
										    "prior": prior,
										    "image_encoder": image_encoder,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "scheduler": scheduler,
										    "image_processor": image_processor,
										}

										return components

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase=0      ):
										if str(_lowerCamelCase      ).startswith("mps"      ):
																lowerCamelCase__					=torch.manual_seed(_lowerCamelCase      )
										else:
																lowerCamelCase__					=torch.Generator(device=_lowerCamelCase      ).manual_seed(_lowerCamelCase      )
										lowerCamelCase__					={
										    "prompt": "horse",
										    "generator": generator,
										    "guidance_scale": 4.0,
										    "num_inference_steps": 2,
										    "output_type": "np",
										}
										return inputs

				def 		_a	(      self      ):
										lowerCamelCase__					="cpu"

										lowerCamelCase__					=self.get_dummy_components()

										lowerCamelCase__					=self.pipeline_class(**_lowerCamelCase      )
										lowerCamelCase__					=pipe.to(_lowerCamelCase      )

										pipe.set_progress_bar_config(disable=_lowerCamelCase      )

										lowerCamelCase__					=pipe(**self.get_dummy_inputs(_lowerCamelCase      )      )
										lowerCamelCase__					=output.image_embeds

										lowerCamelCase__					=pipe(
										    **self.get_dummy_inputs(_lowerCamelCase      )	, return_dict=_lowerCamelCase	, )[0]

										lowerCamelCase__					=image[0, -10:]
										lowerCamelCase__					=image_from_tuple[0, -10:]

										assert image.shape == (1, 32)

										lowerCamelCase__					=np.array(
										    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]      )

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

				@skip_mps
				def 		_a	(      self      ):
										lowerCamelCase__					=torch_device == "cpu"
										lowerCamelCase__					=True
										lowerCamelCase__					=False

										self._test_inference_batch_single_identical(
										    test_max_difference=_lowerCamelCase	, relax_max_difference=_lowerCamelCase	, test_mean_pixel_difference=_lowerCamelCase	, )






				@skip_mps
				def 		_a	(      self      ):
										lowerCamelCase__					=torch_device == "cpu"
										lowerCamelCase__					=False

										self._test_attention_slicing_forward_pass(
										    test_max_difference=_lowerCamelCase	, test_mean_pixel_difference=_lowerCamelCase	, )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_    =					{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					['''MPNetTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MPNetForMaskedLM''',
        '''MPNetForMultipleChoice''',
        '''MPNetForQuestionAnswering''',
        '''MPNetForSequenceClassification''',
        '''MPNetForTokenClassification''',
        '''MPNetLayer''',
        '''MPNetModel''',
        '''MPNetPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFMPNetEmbeddings''',
        '''TFMPNetForMaskedLM''',
        '''TFMPNetForMultipleChoice''',
        '''TFMPNetForQuestionAnswering''',
        '''TFMPNetForSequenceClassification''',
        '''TFMPNetForTokenClassification''',
        '''TFMPNetMainLayer''',
        '''TFMPNetModel''',
        '''TFMPNetPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase_    =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)