
"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class  lowerCAmelCase__							(		unittest.TestCase      ):







     @property
     def 	lowercase      (				self     :     Tuple     ):
       torch.manual_seed(0     )
       _snake_case   	=			UNetaDModel(
           block_out_channels=(32, 64)       , layers_per_block=2       , sample_size=32       , in_channels=3       , out_channels=3       , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       , )
       return model







     def 	lowercase      (				self     :     Tuple     ):
       _snake_case   	=			self.dummy_uncond_unet
       _snake_case   	=			PNDMScheduler()

       _snake_case   	=			PNDMPipeline(unet=lowerCAmelCase__       , scheduler=lowerCAmelCase__     )
       pndm.to(lowerCAmelCase__     )
       pndm.set_progress_bar_config(disable=lowerCAmelCase__     )

       _snake_case   	=			torch.manual_seed(0     )
       _snake_case   	=			pndm(generator=lowerCAmelCase__       , num_inference_steps=20       , output_type='''numpy'''     ).images

       _snake_case   	=			torch.manual_seed(0     )
       _snake_case   	=			pndm(generator=lowerCAmelCase__       , num_inference_steps=20       , output_type='''numpy'''       , return_dict=lowerCAmelCase__     )[0]

       _snake_case   	=			image[0, -3:, -3:, -1]
       _snake_case   	=			image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       _snake_case   	=			np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]     )

       assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1e-2






@slow
@require_torch
class  lowerCAmelCase__							(		unittest.TestCase      ):







     def 	lowercase      (				self     :     Any     ):
       _snake_case   	=			'''google/ddpm-cifar10-32'''

       _snake_case   	=			UNetaDModel.from_pretrained(lowerCAmelCase__     )
       _snake_case   	=			PNDMScheduler()

       _snake_case   	=			PNDMPipeline(unet=lowerCAmelCase__       , scheduler=lowerCAmelCase__     )
       pndm.to(lowerCAmelCase__     )
       pndm.set_progress_bar_config(disable=lowerCAmelCase__     )
       _snake_case   	=			torch.manual_seed(0     )
       _snake_case   	=			pndm(generator=lowerCAmelCase__       , output_type='''numpy'''     ).images

       _snake_case   	=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       _snake_case   	=			np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]     )

       assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2










'''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.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
   from PIL import Image
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	str									=       'Salesforce/blip-image-captioning-base'
       _snake_case      :	Union[str, Any]									=       (
           'This is a tool that generates a description of an image. It takes an input named `image` which should be the '
           'image to caption, and returns a text that contains the description in English.'
       )
       _snake_case      :	List[Any]									=       'image_captioner'
       _snake_case      :	Union[str, Any]									=       AutoModelForVisionaSeq

       _snake_case      :	Dict									=       ['image']
       _snake_case      :	Optional[int]									=       ['text']





       def __init__(				self  :						Union[str, Any]			,					*lowerCAmelCase__  :						Optional[Any]			,					**lowerCAmelCase__  :						Optional[int]	)       ->	Tuple:

            '''simple docstring'''




            requires_backends(self			,					['''vision''']	)
            super().__init__(*lowerCAmelCase__			,					**lowerCAmelCase__	)





       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						"Image"	)       ->	Optional[int]:

            '''simple docstring'''




            return self.pre_processor(images=lowerCAmelCase__			,					return_tensors='''pt'''	)





       def snake_case__		(				self  :						Any			,					lowerCAmelCase__  :						Union[str, Any]	)       ->	Union[str, Any]:

            '''simple docstring'''




            return self.model.generate(**lowerCAmelCase__	)







       def snake_case__		(				self  :						int			,					lowerCAmelCase__  :						List[str]	)       ->	List[str]:

            '''simple docstring'''




            return self.pre_processor.batch_decode(lowerCAmelCase__			,					skip_special_tokens=lowerCAmelCase__	)[0].strip()



