
'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class  A				(							__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		KandinskyVaaImgaImgPipeline
     __magic_name__						=		['''image_embeds''', '''negative_image_embeds''', '''image''']
     __magic_name__						=		[
         '''image_embeds''',
         '''negative_image_embeds''',
         '''image''',
     ]
     __magic_name__						=		[
         '''generator''',
         '''height''',
         '''width''',
         '''strength''',
         '''guidance_scale''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     __magic_name__						=		False





     @property
     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         return 32





     @property
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         return 32





     @property
     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         return self.time_input_dim





     @property
     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         return self.time_input_dim * 4





     @property
     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         return 100





     @property
     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         torch.manual_seed(0	)

         A       :   Union[str, Any]					=						{
             '''in_channels''': 4,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''image''',
             '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
             '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
             '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
             '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
             '''layers_per_block''': 1,
             '''encoder_hid_dim''': self.text_embedder_hidden_size,
             '''encoder_hid_dim_type''': '''image_proj''',
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': None,
         }

         A       :   Any					=						UNetaDConditionModel(**SCREAMING_SNAKE_CASE	)
         return model





     @property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }





     @property
     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         torch.manual_seed(0	)
         A       :   Optional[Any]					=						VQModel(**self.dummy_movq_kwargs	)
         return model





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   Optional[int]					=						self.dummy_unet
         A       :   List[Any]					=						self.dummy_movq

         A       :   Optional[Any]					=						{
             '''num_train_timesteps''': 1000,
             '''beta_schedule''': '''linear''',
             '''beta_start''': 0.00_085,
             '''beta_end''': 0.012,
             '''clip_sample''': False,
             '''set_alpha_to_one''': False,
             '''steps_offset''': 0,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
         }

         A       :   List[Any]					=						DDIMScheduler(**SCREAMING_SNAKE_CASE	)

         A       :   Union[str, Any]					=						{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=0	) ->				str:






         """simple docstring"""






         A       :   Optional[int]					=						floats_tensor((1, self.text_embedder_hidden_size)						,			rng=random.Random(SCREAMING_SNAKE_CASE	)	).to(SCREAMING_SNAKE_CASE	)
         A       :   str					=						floats_tensor((1, self.text_embedder_hidden_size)						,			rng=random.Random(seed + 1	)	).to(
             SCREAMING_SNAKE_CASE	)
         # create init_image
         A       :   Dict					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(SCREAMING_SNAKE_CASE	)	).to(SCREAMING_SNAKE_CASE	)
         A       :   str					=						image.cpu().permute(0						,			2						,			3						,			1	)[0]
         A       :   Union[str, Any]					=						Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE	)	).convert('''RGB'''	).resize((256, 256)	)

         if str(SCREAMING_SNAKE_CASE	).startswith('''mps'''	):
             A       :   int					=						torch.manual_seed(SCREAMING_SNAKE_CASE	)
         else:
             A       :   Tuple					=						torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						{
             '''image''': init_image,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 64,
             '''width''': 64,
             '''num_inference_steps''': 10,
             '''guidance_scale''': 7.0,
             '''strength''': 0.2,
             '''output_type''': '''np''',
         }
         return inputs





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   Optional[Any]					=						'''cpu'''

         A       :   Tuple					=						self.get_dummy_components()

         A       :   List[str]					=						self.pipeline_class(**SCREAMING_SNAKE_CASE	)
         A       :   Any					=						pipe.to(SCREAMING_SNAKE_CASE	)

         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)	)
         A       :   List[Any]					=						output.images

         A       :   Union[str, Any]					=						pipe(
             **self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)						,			return_dict=SCREAMING_SNAKE_CASE						,			)[0]

         A       :   List[str]					=						image[0, -3:, -3:, -1]
         A       :   Any					=						image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         A       :   Optional[int]					=						np.array(
             [0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263]	)
         assert (
             np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
         ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2
         ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'





@slow
@require_torch_gpu
class  A				(							unittest.TestCase						):





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   int					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''	)

         A       :   Dict					=						load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''	)
         A       :   int					=						'''A red cartoon frog, 4k'''

         A       :   Tuple					=						KandinskyVaaPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-prior'''						,			torch_dtype=torch.floataa	)
         pipe_prior.to(SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						KandinskyVaaImgaImgPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-decoder'''						,			torch_dtype=torch.floataa	)
         A       :   Dict					=						pipeline.to(SCREAMING_SNAKE_CASE	)

         pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

         A       :   str					=						torch.Generator(device='''cpu'''	).manual_seed(0	)
         A,  A       :   List[str]					=						pipe_prior(
             SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			num_inference_steps=5						,			negative_prompt=''''''						,			).to_tuple()

         A       :   Optional[int]					=						pipeline(
             image=SCREAMING_SNAKE_CASE						,			image_embeds=SCREAMING_SNAKE_CASE						,			negative_image_embeds=SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			num_inference_steps=100						,			height=768						,			width=768						,			strength=0.2						,			output_type='''np'''						,			)

         A       :   List[Any]					=						output.images[0]

         assert image.shape == (768, 768, 3)

         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)






'''simple docstring'''




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase			:				List[str]    		=			logging.get_logger(__name__)

lowercase			:				str    		=			{
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}





class  A				(							__snake_case						):
     __magic_name__						=		'''gpt_neo'''
     __magic_name__						=		['''past_key_values''']
     __magic_name__						=		{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





     def __init__(							self						,			SCREAMING_SNAKE_CASE=50257						,			SCREAMING_SNAKE_CASE=2048						,			SCREAMING_SNAKE_CASE=2048						,			SCREAMING_SNAKE_CASE=24						,			SCREAMING_SNAKE_CASE=[[["global", "local"], 12]]						,			SCREAMING_SNAKE_CASE=16						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=256						,			SCREAMING_SNAKE_CASE="gelu_new"						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=1e-5						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=50256						,			SCREAMING_SNAKE_CASE=50256						,			**SCREAMING_SNAKE_CASE						,			) ->				int:






         """simple docstring"""






         A       :   Union[str, Any]					=						vocab_size
         A       :   Optional[Any]					=						max_position_embeddings
         A       :   Dict					=						hidden_size
         A       :   Optional[Any]					=						num_layers
         A       :   Tuple					=						num_heads
         A       :   int					=						intermediate_size
         A       :   Optional[Any]					=						window_size
         A       :   List[Any]					=						activation_function
         A       :   Union[str, Any]					=						resid_dropout
         A       :   Any					=						embed_dropout
         A       :   List[Any]					=						attention_dropout
         A       :   str					=						classifier_dropout
         A       :   List[Any]					=						layer_norm_epsilon
         A       :   str					=						initializer_range
         A       :   List[str]					=						use_cache

         A       :   Optional[int]					=						bos_token_id
         A       :   List[Any]					=						eos_token_id

         A       :   int					=						attention_types
         A       :   int					=						self.expand_attention_types_params(SCREAMING_SNAKE_CASE	)

         if len(self.attention_layers	) != self.num_layers:
             raise ValueError(
                 '''Configuration for convolutional module is incorrect. '''
                 '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
                 F'but is `len(config.attention_layers) = {len(self.attention_layers	)}`, '
                 F'`config.num_layers = {self.num_layers}`. '
                 '''`config.attention_layers` is prepared using `config.attention_types`. '''
                 '''Please verify the value of `config.attention_types` argument.'''	)

         super().__init__(bos_token_id=SCREAMING_SNAKE_CASE						,			eos_token_id=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE	) ->				int:






         """simple docstring"""






         A       :   List[str]					=						[]
         for item in attention_types:
             for _ in range(item[1]	):
                 attentions.extend(item[0]	)
         return attentions







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    import torch

    A       :   Tuple					=						input.size()
    A       :   Union[str, Any]					=						len(snake_case__						)
    A       :   List[str]					=						shape[dimension]

    A       :   Union[str, Any]					=						torch.arange(0		,				snake_case__		,				snake_case__						)
    A       :   List[str]					=						torch.div(sizedim - size		,				snake_case__		,				rounding_mode='''floor'''						) + 1
    A       :   Optional[int]					=						torch.arange(snake_case__						) + low_indices[:min_length][:, None]

    A       :   str					=						[slice(snake_case__						)] * rank
    A       :   List[Any]					=						indices
    A       :   Union[str, Any]					=						input[s]

    A       :   List[str]					=						list(range(0		,				rank + 1						)						)
    perm.append(perm.pop(dimension + 1						)						)

    return sliced.permute(snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    import torch

    A       :   List[str]					=						torch.arange(1		,				snake_case__						)
    A       :   Optional[int]					=						torch.remainder(snake_case__		,				snake_case__						)
    A       :   Optional[int]					=						remainders == 0
    A       :   Optional[Any]					=						candidates[divisor_indices]
    A       :   Optional[int]					=						torch.max(snake_case__						)
    return largest_divisor, torch.div(snake_case__		,				snake_case__		,				rounding_mode='''floor'''						)







class  A				(							__snake_case						):





     @property
     def 				__lowerCAmelCase							(							self	) ->				Mapping[str, Mapping[int, str]]:






         """simple docstring"""






         A       :   Tuple					=						OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
         if self.use_past:
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE						,			direction='''inputs'''	)
             A       :   Optional[Any]					=						{0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
             A       :   Dict					=						{0: '''batch''', 1: '''sequence'''}

         return common_inputs





     @property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return self._config.num_heads





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = -1						,			SCREAMING_SNAKE_CASE = -1						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = None						,			) ->				Mapping[str, Any]:






         """simple docstring"""






         A       :   List[str]					=						super(SCREAMING_SNAKE_CASE						,			self	).generate_dummy_inputs(
             SCREAMING_SNAKE_CASE						,			batch_size=SCREAMING_SNAKE_CASE						,			seq_length=SCREAMING_SNAKE_CASE						,			is_pair=SCREAMING_SNAKE_CASE						,			framework=SCREAMING_SNAKE_CASE	)

         # We need to order the input in the way they appears in the forward()
         A       :   Any					=						OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)

         # Need to add the past_keys
         if self.use_past:
             if not is_torch_available():
                 raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
             else:
                 import torch

                 A,  A       :   Dict					=						common_inputs['''input_ids'''].shape
                 # Not using the same length for past_key_values
                 A       :   str					=						seqlen + 2
                 A       :   List[Any]					=						(
                     batch,
                     self.num_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // self.num_attention_heads,
                 )
                 A       :   Any					=						[
                     (torch.zeros(SCREAMING_SNAKE_CASE	), torch.zeros(SCREAMING_SNAKE_CASE	)) for _ in range(self.num_layers	)
                 ]

         A       :   str					=						common_inputs['''attention_mask''']
         if self.use_past:
             A       :   Optional[int]					=						ordered_inputs['''attention_mask'''].dtype
             A       :   List[str]					=						torch.cat(
                 [ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			dtype=SCREAMING_SNAKE_CASE	)]						,			dim=1	)

         return ordered_inputs





     @property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return 13




