


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      _lowercase					(				lowercase__				,    unittest.TestCase):


       """simple docstring"""




       A__   		=     KandinskyPriorPipeline
       A__   		=     ["prompt"]
       A__   		=     ["prompt", "negative_prompt"]
       A__   		=     [
           "num_images_per_prompt",
           "generator",
           "num_inference_steps",
           "latents",
           "negative_prompt",
           "guidance_scale",
           "output_type",
           "return_dict",
       ]
       A__   		=     False





       @property
       def 		lowerCAmelCase	(  self				:     Union[str, Any]			):


             '''simple docstring'''



             return 32





       @property
       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             return 32





       @property
       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             return self.time_input_dim





       @property
       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             return self.time_input_dim * 4





       @property
       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             return 100





       @property
       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)
             return tokenizer





       @property
       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             torch.manual_seed(0			)
             lowerCamelCase__						:							Any       =    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-0_5   , num_attention_heads=4   , num_hidden_layers=5   , pad_token_id=1   , vocab_size=1000   , )
             return CLIPTextModelWithProjection(__lowerCamelCase			)





       @property
       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             torch.manual_seed(0			)

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

             lowerCamelCase__						:							Optional[Any]       =    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__						:							Dict       =    nn.Parameter(torch.ones(model.clip_std.shape			)			)
             return model





       @property
       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             torch.manual_seed(0			)
             lowerCamelCase__						:							List[str]       =    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__						:							Union[str, Any]       =    CLIPVisionModelWithProjection(__lowerCamelCase			)
             return model





       @property
       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    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 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Dict       =    self.dummy_prior
             lowerCamelCase__						:							Any       =    self.dummy_image_encoder
             lowerCamelCase__						:							Union[str, Any]       =    self.dummy_text_encoder
             lowerCamelCase__						:							List[Any]       =    self.dummy_tokenizer
             lowerCamelCase__						:							Union[str, Any]       =    self.dummy_image_processor

             lowerCamelCase__						:							Union[str, Any]       =    UnCLIPScheduler(
                 variance_type="fixed_small_log"   , prediction_type="sample"   , num_train_timesteps=1000   , clip_sample=__lowerCamelCase   , clip_sample_range=1_0.0   , )

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

             return components





       def 		lowerCAmelCase	(  self				:     List[Any]   , __lowerCamelCase				:     Tuple   , __lowerCamelCase				:     Any=0			):


             '''simple docstring'''



             if str(__lowerCamelCase			).startswith("mps"			):
                   lowerCamelCase__						:							List[str]       =    torch.manual_seed(__lowerCamelCase			)
             else:
                   lowerCamelCase__						:							Tuple       =    torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)
             lowerCamelCase__						:							Any       =    {
                 "prompt": "horse",
                 "generator": generator,
                 "guidance_scale": 4.0,
                 "num_inference_steps": 2,
                 "output_type": "np",
             }
             return inputs





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


             '''simple docstring'''



             lowerCamelCase__						:							str       =    "cpu"

             lowerCamelCase__						:							List[str]       =    self.get_dummy_components()

             lowerCamelCase__						:							str       =    self.pipeline_class(**__lowerCamelCase			)
             lowerCamelCase__						:							str       =    pipe.to(__lowerCamelCase			)

             pipe.set_progress_bar_config(disable=__lowerCamelCase			)

             lowerCamelCase__						:							int       =    pipe(**self.get_dummy_inputs(__lowerCamelCase			)			)
             lowerCamelCase__						:							Any       =    output.image_embeds

             lowerCamelCase__						:							Any       =    pipe(
                 **self.get_dummy_inputs(__lowerCamelCase			)   , return_dict=__lowerCamelCase   , )[0]

             lowerCamelCase__						:							int       =    image[0, -10:]
             lowerCamelCase__						:							List[str]       =    image_from_tuple[0, -10:]

             assert image.shape == (1, 32)

             lowerCamelCase__						:							str       =    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 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    torch_device == "cpu"
             lowerCamelCase__						:							Tuple       =    True
             lowerCamelCase__						:							Optional[Any]       =    False

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





       @skip_mps
       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    torch_device == "cpu"
             lowerCamelCase__						:							Any       =    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,
)


A :       Optional[int]						=   {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       List[Any]						=   ["MPNetTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A :       List[str]						=   [
        "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:
    A :       Tuple						=   [
        "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

    A :       Union[str, Any]						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


