
import unittest

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

from diffusers import KandinskyVaaPriorPipeline, 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 			A_  (     __lowerCamelCase ,      unittest.TestCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	Tuple									=			KandinskyVaaPriorPipeline
	_UpperCamelCase	:	Optional[Any]									=			["""prompt"""]
	_UpperCamelCase	:	List[str]									=			["""prompt""", """negative_prompt"""]
	_UpperCamelCase	:	str									=			[
	    """num_images_per_prompt""",
	    """generator""",
	    """num_inference_steps""",
	    """latents""",
	    """negative_prompt""",
	    """guidance_scale""",
	    """output_type""",
	    """return_dict""",
	]
	_UpperCamelCase	:	Any									=			False





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return 32





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return 32





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





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





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		return 100





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
		return tokenizer





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		torch.manual_seed(0			)
		lowercase	    =       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(snake_case			)





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

		lowercase	    =       {
		    'num_attention_heads': 2,
		    'attention_head_dim': 12,
		    'embedding_dim': self.text_embedder_hidden_size,
		    'num_layers': 1,
		}

		lowercase	    =       PriorTransformer(**snake_case			)
		# 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
		lowercase	    =       nn.Parameter(torch.ones(model.clip_std.shape			)			)
		return model





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		torch.manual_seed(0			)
		lowercase	    =       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						,       )

		lowercase	    =       CLIPVisionModelWithProjection(snake_case			)
		return model





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       CLIPImageProcessor(
		    crop_size=224						,       do_center_crop=snake_case						,       do_normalize=snake_case						,       do_resize=snake_case						,       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       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       self.dummy_prior
		lowercase	    =       self.dummy_image_encoder
		lowercase	    =       self.dummy_text_encoder
		lowercase	    =       self.dummy_tokenizer
		lowercase	    =       self.dummy_image_processor

		lowercase	    =       UnCLIPScheduler(
		    variance_type='fixed_small_log'						,       prediction_type='sample'						,       num_train_timesteps=1000						,       clip_sample=snake_case						,       clip_sample_range=10.0						,       )

		lowercase	    =       {
		    'prior': prior,
		    'image_encoder': image_encoder,
		    'text_encoder': text_encoder,
		    'tokenizer': tokenizer,
		    'scheduler': scheduler,
		    'image_processor': image_processor,
		}

		return components





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case=0			):
		if str(snake_case			).startswith('mps'			):
			lowercase	    =       torch.manual_seed(snake_case			)
		else:
			lowercase	    =       torch.Generator(device=snake_case			).manual_seed(snake_case			)
		lowercase	    =       {
		    'prompt': 'horse',
		    'generator': generator,
		    'guidance_scale': 4.0,
		    'num_inference_steps': 2,
		    'output_type': 'np',
		}
		return inputs





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       'cpu'

		lowercase	    =       self.get_dummy_components()

		lowercase	    =       self.pipeline_class(**snake_case			)
		lowercase	    =       pipe.to(snake_case			)

		pipe.set_progress_bar_config(disable=snake_case			)

		lowercase	    =       pipe(**self.get_dummy_inputs(snake_case			)			)
		lowercase	    =       output.image_embeds

		lowercase	    =       pipe(
		    **self.get_dummy_inputs(snake_case			)						,       return_dict=snake_case						,       )[0]

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

		assert image.shape == (1, 32)

		lowercase	    =       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       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       torch_device == 'cpu'
		lowercase	    =       True
		lowercase	    =       False

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





	@skip_mps
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       torch_device == 'cpu'
		lowercase	    =       False

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



'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
lowercase__						       =      "\nHuman: <<task>>\n\nAssistant: "


lowercase__						       =      "huggingface-tools/default-prompts"
lowercase__						       =      {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__="run"			)  ->							Dict:


		'''simple docstring'''


		if prompt_or_repo_id is None:
				snake_case  :       Union[str, Any]     			=						DEFAULT_PROMPTS_REPO

		# prompt is considered a repo ID when it does not contain any kind of space
		if re.search('''\\s''' ,       SCREAMING_SNAKE_CASE__			) is not None:
				return prompt_or_repo_id

		snake_case  :       List[Any]     			=						cached_file(
		    SCREAMING_SNAKE_CASE__ ,       PROMPT_FILES[mode] ,       repo_type='''dataset''' ,       user_agent={'''agent''': agent_name}			)
		with open(SCREAMING_SNAKE_CASE__ ,       '''r''' ,       encoding='''utf-8'''			) as f:
				return f.read()


