'''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 		SCREAMING_SNAKE_CASE_					(_a							,				unittest.TestCase      ):



			"""simple docstring"""






			__magic_name__ :				List[str]   							=       KandinskyPriorPipeline
			__magic_name__ :				str   							=       ["""prompt"""]
			__magic_name__ :				List[Any]   							=       ["""prompt""", """negative_prompt"""]
			__magic_name__ :				List[Any]   							=       [
			    """num_images_per_prompt""",
			    """generator""",
			    """num_inference_steps""",
			    """latents""",
			    """negative_prompt""",
			    """guidance_scale""",
			    """output_type""",
			    """return_dict""",
			]
			__magic_name__ :				Any   							=       False



			@property
			def   lowerCamelCase (							self					):
									return 32



			@property
			def   lowerCamelCase (							self					):
									return 32



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



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



			@property
			def   lowerCamelCase (							self					):
									return 100



			@property
			def   lowerCamelCase (							self					):
									snake_case :       Union[str, Any]				=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)
									return tokenizer



			@property
			def   lowerCamelCase (							self					):
									torch.manual_seed(0					)
									snake_case :       List[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-05				,			num_attention_heads=4				,			num_hidden_layers=5				,			pad_token_id=1				,			vocab_size=1_000				,			)
									return CLIPTextModelWithProjection(_a					)



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

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

									snake_case :       Tuple				=    PriorTransformer(**_a					)
									# 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
									snake_case :       Union[str, Any]				=    nn.Parameter(torch.ones(model.clip_std.shape					)					)
									return model



			@property
			def   lowerCamelCase (							self					):
									torch.manual_seed(0					)
									snake_case :       Any				=    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				,			)

									snake_case :       Any				=    CLIPVisionModelWithProjection(_a					)
									return model



			@property
			def   lowerCamelCase (							self					):
									snake_case :       Union[str, Any]				=    CLIPImageProcessor(
									    crop_size=224				,			do_center_crop=_a				,			do_normalize=_a				,			do_resize=_a				,			image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]				,			image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]				,			resample=3				,			size=224				,			)

									return image_processor



			def   lowerCamelCase (							self					):
									snake_case :       Dict				=    self.dummy_prior
									snake_case :       Any				=    self.dummy_image_encoder
									snake_case :       int				=    self.dummy_text_encoder
									snake_case :       str				=    self.dummy_tokenizer
									snake_case :       List[str]				=    self.dummy_image_processor

									snake_case :       int				=    UnCLIPScheduler(
									    variance_type="fixed_small_log"				,			prediction_type="sample"				,			num_train_timesteps=1_000				,			clip_sample=_a				,			clip_sample_range=10.0				,			)

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

									return components



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_=0					):
									if str(_a					).startswith("mps"					):
															snake_case :       List[Any]				=    torch.manual_seed(_a					)
									else:
															snake_case :       List[str]				=    torch.Generator(device=_a					).manual_seed(_a					)
									snake_case :       str				=    {
									    "prompt": "horse",
									    "generator": generator,
									    "guidance_scale": 4.0,
									    "num_inference_steps": 2,
									    "output_type": "np",
									}
									return inputs



			def   lowerCamelCase (							self					):
									snake_case :       Any				=    "cpu"

									snake_case :       str				=    self.get_dummy_components()

									snake_case :       Union[str, Any]				=    self.pipeline_class(**_a					)
									snake_case :       int				=    pipe.to(_a					)

									pipe.set_progress_bar_config(disable=_a					)

									snake_case :       Union[str, Any]				=    pipe(**self.get_dummy_inputs(_a					)					)
									snake_case :       Optional[int]				=    output.image_embeds

									snake_case :       str				=    pipe(
									    **self.get_dummy_inputs(_a					)				,			return_dict=_a				,			)[0]

									snake_case :       List[str]				=    image[0, -10:]
									snake_case :       List[Any]				=    image_from_tuple[0, -10:]

									assert image.shape == (1, 32)

									snake_case :       Optional[int]				=    np.array(
									    [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]					)

									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					):
									snake_case :       str				=    torch_device == "cpu"
									snake_case :       Tuple				=    True
									snake_case :       Optional[int]				=    False

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






			@skip_mps
			def   lowerCamelCase (							self					):
									snake_case :       str				=    torch_device == "cpu"
									snake_case :       str				=    False

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










from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__UpperCAmelCase    	=  {
    '''configuration_ernie_m''': ['''ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieMConfig'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    	=  ['''ErnieMTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    	=  [
		    '''ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ErnieMForMultipleChoice''',
		    '''ErnieMForQuestionAnswering''',
		    '''ErnieMForSequenceClassification''',
		    '''ErnieMForTokenClassification''',
		    '''ErnieMModel''',
		    '''ErnieMPreTrainedModel''',
		    '''ErnieMForInformationExtraction''',
		]


if TYPE_CHECKING:
		from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_ernie_m import ErnieMTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ernie_m import (
				    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ErnieMForInformationExtraction,
				    ErnieMForMultipleChoice,
				    ErnieMForQuestionAnswering,
				    ErnieMForSequenceClassification,
				    ErnieMForTokenClassification,
				    ErnieMModel,
				    ErnieMPreTrainedModel,
				)


else:
		import sys

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





