



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  lowerCamelCase__    (							lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=KandinskyVaaImgaImgPipeline
		snake_case_			=["""image_embeds""", """negative_image_embeds""", """image"""]
		snake_case_			=[
		    """image_embeds""",
		    """negative_image_embeds""",
		    """image""",
		]
		snake_case_			=[
		    """generator""",
		    """height""",
		    """width""",
		    """strength""",
		    """guidance_scale""",
		    """num_inference_steps""",
		    """return_dict""",
		    """guidance_scale""",
		    """num_images_per_prompt""",
		    """output_type""",
		    """return_dict""",
		]
		snake_case_			=False



		@property
		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						return 32



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

						"""simple docstring"""






						return 32



		@property
		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						return self.time_input_dim



		@property
		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						return self.time_input_dim * 4



		@property
		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						return 1_00



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

						"""simple docstring"""






						torch.manual_seed(0				)

						lowerCAmelCase__  :       int				=  {
						    '''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,
						}

						lowerCAmelCase__  :       Dict				=  UNetaDConditionModel(**__lowerCamelCase				)
						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				)  ->    Union[str, Any]:

						"""simple docstring"""






						torch.manual_seed(0				)
						lowerCAmelCase__  :       Union[str, Any]				=  VQModel(**self.dummy_movq_kwargs				)
						return model



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.dummy_unet
						lowerCAmelCase__  :       Any				=  self.dummy_movq

						lowerCAmelCase__  :       Any				=  {
						    '''num_train_timesteps''': 10_00,
						    '''beta_schedule''': '''linear''',
						    '''beta_start''': 0.0_0085,
						    '''beta_end''': 0.012,
						    '''clip_sample''': False,
						    '''set_alpha_to_one''': False,
						    '''steps_offset''': 0,
						    '''prediction_type''': '''epsilon''',
						    '''thresholding''': False,
						}

						lowerCAmelCase__  :       Optional[int]				=  DDIMScheduler(**__lowerCamelCase				)

						lowerCAmelCase__  :       Tuple				=  {
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''movq''': movq,
						}

						return components



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase=0				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(__lowerCamelCase				)				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       Dict				=  floats_tensor((1, self.text_embedder_hidden_size)	,rng=random.Random(seed + 1				)				).to(
						    __lowerCamelCase				)
						# create init_image
						lowerCAmelCase__  :       Any				=  floats_tensor((1, 3, 64, 64)	,rng=random.Random(__lowerCamelCase				)				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  image.cpu().permute(0	,2	,3	,1				)[0]
						lowerCAmelCase__  :       Union[str, Any]				=  Image.fromarray(np.uinta(__lowerCamelCase				)				).convert('''RGB'''				).resize((2_56, 2_56)				)

						if str(__lowerCamelCase				).startswith('''mps'''				):
										lowerCAmelCase__  :       Optional[Any]				=  torch.manual_seed(__lowerCamelCase				)
						else:
										lowerCAmelCase__  :       List[str]				=  torch.Generator(device=__lowerCamelCase				).manual_seed(__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  {
						    '''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				)  ->    List[str]:

						"""simple docstring"""






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

						lowerCAmelCase__  :       int				=  self.get_dummy_components()

						lowerCAmelCase__  :       List[Any]				=  self.pipeline_class(**__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  pipe.to(__lowerCamelCase				)

						pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       Union[str, Any]				=  pipe(**self.get_dummy_inputs(__lowerCamelCase				)				)
						lowerCAmelCase__  :       Union[str, Any]				=  output.images

						lowerCAmelCase__  :       Tuple				=  pipe(
						    **self.get_dummy_inputs(__lowerCamelCase				)	,return_dict=__lowerCamelCase	,)[0]

						lowerCAmelCase__  :       str				=  image[0, -3:, -3:, -1]
						lowerCAmelCase__  :       Union[str, Any]				=  image_from_tuple[0, -3:, -3:, -1]

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

						lowerCAmelCase__  :       Union[str, Any]				=  np.array(
						    [0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263]				)
						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  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






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



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

						"""simple docstring"""






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

						lowerCAmelCase__  :       Optional[Any]				=  load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''				)
						lowerCAmelCase__  :       Union[str, Any]				=  '''A red cartoon frog, 4k'''

						lowerCAmelCase__  :       List[Any]				=  KandinskyVaaPriorPipeline.from_pretrained(
						    '''kandinsky-community/kandinsky-2-2-prior'''	,torch_dtype=torch.floataa				)
						pipe_prior.to(__lowerCamelCase				)

						lowerCAmelCase__  :       Union[str, Any]				=  KandinskyVaaImgaImgPipeline.from_pretrained(
						    '''kandinsky-community/kandinsky-2-2-decoder'''	,torch_dtype=torch.floataa				)
						lowerCAmelCase__  :       Any				=  pipeline.to(__lowerCamelCase				)

						pipeline.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[Any]				=  torch.Generator(device='''cpu'''				).manual_seed(0				)
						lowerCAmelCase__		,	lowerCAmelCase__  :       List[str]				=  pipe_prior(
						    __lowerCamelCase	,generator=__lowerCamelCase	,num_inference_steps=5	,negative_prompt=''''''	,).to_tuple()

						lowerCAmelCase__  :       str				=  pipeline(
						    image=__lowerCamelCase	,image_embeds=__lowerCamelCase	,negative_image_embeds=__lowerCamelCase	,generator=__lowerCamelCase	,num_inference_steps=1_00	,height=7_68	,width=7_68	,strength=0.2	,output_type='''np'''	,)

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

						assert image.shape == (7_68, 7_68, 3)

						assert_mean_pixel_difference(__lowerCamelCase	,__lowerCamelCase				)






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__snake_case       :	List[str]            ='tiny-wmt19-en-ru'

# Build

# borrowed from a test
__snake_case       :	Any            =[
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
__snake_case       :	Union[str, Any]            =dict(zip(vocab, range(len(vocab))))
__snake_case       :	List[Any]            =['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case       :	Optional[Any]            =Path(tmpdirname)
							__snake_case       :	int            =build_dir / VOCAB_FILES_NAMES['src_vocab_file']
							__snake_case       :	str            =build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
							__snake_case       :	Optional[int]            =build_dir / VOCAB_FILES_NAMES['merges_file']
							with open(src_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(tgt_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(merges_file, 'w') as fp:
														fp.write('\n'.join(merges))

							__snake_case       :	Union[str, Any]            =FSMTTokenizer(
							    langs=['en', 'ru'],
							    src_vocab_size=len(vocab),
							    tgt_vocab_size=len(vocab),
							    src_vocab_file=src_vocab_file,
							    tgt_vocab_file=tgt_vocab_file,
							    merges_file=merges_file,
							)

__snake_case       :	Dict            =FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

__snake_case       :	Optional[Any]            =FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__snake_case       :	Dict            =tokenizer(['Making tiny model'], return_tensors='pt')
__snake_case       :	Dict            =tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru

