




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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		(  __UpperCAmelCase      ,				unittest.TestCase     ):
		__snake_case									=   KandinskyVaaControlnetImgaImgPipeline
		__snake_case									=   ['image_embeds', 'negative_image_embeds', 'image', 'hint']
		__snake_case									=   ['image_embeds', 'negative_image_embeds', 'image', 'hint']
		__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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return 32



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return 32



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self.time_input_dim



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self.time_input_dim * 4



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return 100



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								torch.manual_seed(0   )

								lowerCAmelCase_      			=  {
								    '''in_channels''': 8,
								    # Out channels is double in channels because predicts mean and variance
								    '''out_channels''': 8,
								    '''addition_embed_type''': '''image_hint''',
								    '''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_      			=  UNetaDConditionModel(**UpperCamelCase__   )
								return model



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return {
								    "block_out_channels": [32, 32, 64, 64],
								    "down_block_types": [
								        "DownEncoderBlock2D",
								        "DownEncoderBlock2D",
								        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
								    "vq_embed_dim": 4,
								}



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								torch.manual_seed(0   )
								lowerCAmelCase_      			=  VQModel(**self.dummy_movq_kwargs   )
								return model



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.dummy_unet
								lowerCAmelCase_      			=  self.dummy_movq

								lowerCAmelCase_      			=  {
								    '''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,
								}

								lowerCAmelCase_      			=  DDIMScheduler(**UpperCamelCase__   )

								lowerCAmelCase_      			=  {
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}

								return components



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__=0   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  floats_tensor((1, self.text_embedder_hidden_size),	rng=random.Random(UpperCamelCase__   )   ).to(UpperCamelCase__   )
								lowerCAmelCase_      			=  floats_tensor((1, self.text_embedder_hidden_size),	rng=random.Random(seed + 1   )   ).to(
								    UpperCamelCase__   )
								# create init_image
								lowerCAmelCase_      			=  floats_tensor((1, 3, 64, 64),	rng=random.Random(UpperCamelCase__   )   ).to(UpperCamelCase__   )
								lowerCAmelCase_      			=  image.cpu().permute(0,	2,	3,	1   )[0]
								lowerCAmelCase_      			=  Image.fromarray(np.uinta(UpperCamelCase__   )   ).convert('''RGB'''   ).resize((256, 256)   )
								# create hint
								lowerCAmelCase_      			=  floats_tensor((1, 3, 64, 64),	rng=random.Random(UpperCamelCase__   )   ).to(UpperCamelCase__   )

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







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  '''cpu'''

								lowerCAmelCase_      			=  self.get_dummy_components()

								lowerCAmelCase_      			=  self.pipeline_class(**UpperCamelCase__   )
								lowerCAmelCase_      			=  pipe.to(UpperCamelCase__   )

								pipe.set_progress_bar_config(disable=UpperCamelCase__   )

								lowerCAmelCase_      			=  pipe(**self.get_dummy_inputs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  output.images

								lowerCAmelCase_      			=  pipe(
								    **self.get_dummy_inputs(UpperCamelCase__   ),	return_dict=UpperCamelCase__,	)[0]

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

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

								lowerCAmelCase_      			=  np.array(
								    [0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736]   )
								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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''   )

								lowerCAmelCase_      			=  load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''   )
								lowerCAmelCase_      			=  init_image.resize((512, 512)   )

								lowerCAmelCase_      			=  load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/hint_image_cat.png'''   )
								lowerCAmelCase_      			=  torch.from_numpy(np.array(UpperCamelCase__   )   ).float() / 255.0
								lowerCAmelCase_      			=  hint.permute(2,	0,	1   ).unsqueeze(0   )

								lowerCAmelCase_      			=  '''A robot, 4k photo'''

								lowerCAmelCase_      			=  KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-prior''',	torch_dtype=torch.floataa   )
								pipe_prior.to(UpperCamelCase__   )

								lowerCAmelCase_      			=  KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-controlnet-depth''',	torch_dtype=torch.floataa   )
								lowerCAmelCase_      			=  pipeline.to(UpperCamelCase__   )

								pipeline.set_progress_bar_config(disable=UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.Generator(device='''cpu'''   ).manual_seed(0   )

								lowerCAmelCase_			,					lowerCAmelCase_      			=  pipe_prior(
								    UpperCamelCase__,	image=UpperCamelCase__,	strength=0.85,	generator=UpperCamelCase__,	negative_prompt='''''',	).to_tuple()

								lowerCAmelCase_      			=  pipeline(
								    image=UpperCamelCase__,	image_embeds=UpperCamelCase__,	negative_image_embeds=UpperCamelCase__,	hint=UpperCamelCase__,	generator=UpperCamelCase__,	num_inference_steps=100,	height=512,	width=512,	strength=0.5,	output_type='''np''',	)

								lowerCAmelCase_      			=  output.images[0]

								assert image.shape == (512, 512, 3)

								assert_mean_pixel_difference(UpperCamelCase__,	UpperCamelCase__   )










# 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

_A     =			'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A     =			[
    '''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>''',
]
_A     =			dict(zip(vocab, range(len(vocab))))
_A     =			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
						_A     =			Path(tmpdirname)
						_A     =			build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
						_A     =			build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
						_A     =			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))

						_A     =			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,
						)

_A     =			FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    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,
)

_A     =			FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")

# Test
_A     =			tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A     =			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




