
import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin




@skip_mps
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       __UpperCamelCase							,       unittest.TestCase			):
      _A    :		Any     						=  IFInpaintingPipeline
      _A    :		Union[str, Any]     						=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
      _A    :		str     						=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      _A    :		str     						=  PipelineTesterMixin.required_optional_params - {'latents'}
      def A_      (      self			):
          return self._get_dummy_components()
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase=0			):
          if str(lowerCamelCase			).startswith("mps"			):
              snake_case__    =					torch.manual_seed(lowerCamelCase			)
          else:
              snake_case__    =					torch.Generator(device=lowerCamelCase			).manual_seed(lowerCamelCase			)

          snake_case__    =					floats_tensor((1, 3, 32, 32)		,						rng=random.Random(lowerCamelCase			)			).to(lowerCamelCase			)
          snake_case__    =					floats_tensor((1, 3, 32, 32)		,						rng=random.Random(lowerCamelCase			)			).to(lowerCamelCase			)

          snake_case__    =					{
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "mask_image": mask_image,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs
      @unittest.skipIf(
          torch_device != "cuda" or not is_xformers_available()		,						reason="XFormers attention is only available with CUDA and `xformers` installed"		,						)
      def A_      (      self			):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)
      def A_      (      self			):
          self._test_save_load_optional_components()
      @unittest.skipIf(torch_device != "cuda"		,						reason="float16 requires CUDA"			)
      def A_      (      self			):
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1e-1			)
      def A_      (      self			):
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)
      def A_      (      self			):
          self._test_save_load_local()



      def A_      (      self			):
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2		,						)





# 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


__magic_name__  							=	'''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)



