



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  __snake_case      (   SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      int         =					KandinskyVaaPriorPipeline
							UpperCamelCase__				:      Union[str, Any]         =					["""prompt"""]
							UpperCamelCase__				:      Optional[Any]         =					["""prompt""", """negative_prompt"""]
							UpperCamelCase__				:      Tuple         =					[
							    """num_images_per_prompt""",
							    """generator""",
							    """num_inference_steps""",
							    """latents""",
							    """negative_prompt""",
							    """guidance_scale""",
							    """output_type""",
							    """return_dict""",
							]
							UpperCamelCase__				:      Optional[Any]         =					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		):
											a__				      =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)
											return tokenizer


							@property
							def _a      (							self		):
											torch.manual_seed(0		)
											a__				      =		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=1_000  ,  )
											return CLIPTextModelWithProjection(a_		)


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

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

											a__				      =		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
											a__				      =		nn.Parameter(torch.ones(model.clip_std.shape		)		)
											return model


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

											a__				      =		CLIPVisionModelWithProjection(a_		)
											return model


							@property
							def _a      (							self		):
											a__				      =		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 _a      (							self		):
											a__				      =		self.dummy_prior
											a__				      =		self.dummy_image_encoder
											a__				      =		self.dummy_text_encoder
											a__				      =		self.dummy_tokenizer
											a__				      =		self.dummy_image_processor

											a__				      =		UnCLIPScheduler(
											    variance_type="""fixed_small_log"""  ,  prediction_type="""sample"""  ,  num_train_timesteps=1_000  ,  clip_sample=a_  ,  clip_sample_range=10.0  ,  )

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

											return components


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


							def _a      (							self		):
											a__				      =		"""cpu"""

											a__				      =		self.get_dummy_components()

											a__				      =		self.pipeline_class(**a_		)
											a__				      =		pipe.to(a_		)

											pipe.set_progress_bar_config(disable=a_		)

											a__				      =		pipe(**self.get_dummy_inputs(a_		)		)
											a__				      =		output.image_embeds

											a__				      =		pipe(
											    **self.get_dummy_inputs(a_		)  ,  return_dict=a_  ,  )[0]

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

											assert image.shape == (1, 32)

											a__				      =		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 _a      (							self		):
											a__				      =		torch_device == """cpu"""
											a__				      =		True
											a__				      =		False

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







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

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






# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCAmelCase							 =					"""3"""

print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)

try:
	import torch

	print("""Torch version:""", torch.__version__)
	print("""Cuda available:""", torch.cuda.is_available())
	print("""Cuda version:""", torch.version.cuda)
	print("""CuDNN version:""", torch.backends.cudnn.version())
	print("""Number of GPUs available:""", torch.cuda.device_count())
	print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
	print("""Torch version:""", None)

try:
	import deepspeed

	print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
	print("""DeepSpeed version:""", None)

try:
	import tensorflow as tf

	print("""TensorFlow version:""", tf.__version__)
	print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
	print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
	print("""TensorFlow version:""", None)

