import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase       =		False
class     lowerCAmelCase_     (       unittest.TestCase ):
							pass




@nightly
@require_torch_gpu
class     lowerCAmelCase_     (       unittest.TestCase ):







							def  UpperCamelCase_						(   self      :     Tuple		):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()







							def  UpperCamelCase_						(   self      :     str		):
												_UpperCamelCase			     =     VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' ,			torch_dtype=torch.floataa		)
												pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

												_UpperCamelCase			     =     torch.manual_seed(0		)
												_UpperCamelCase			     =     pipe.dual_guided(
												    prompt='''first prompt''' ,			image=_A ,			text_to_image_strength=0.75 ,			generator=_A ,			guidance_scale=7.5 ,			num_inference_steps=2 ,			output_type='''numpy''' ,			).images

												with tempfile.TemporaryDirectory() as tmpdirname:
																	pipe.save_pretrained(_A		)
																	_UpperCamelCase			     =     VersatileDiffusionPipeline.from_pretrained(_A ,			torch_dtype=torch.floataa		)
												pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     generator.manual_seed(0		)
												_UpperCamelCase			     =     pipe.dual_guided(
												    prompt='''first prompt''' ,			image=_A ,			text_to_image_strength=0.75 ,			generator=_A ,			guidance_scale=7.5 ,			num_inference_steps=2 ,			output_type='''numpy''' ,			).images

												assert np.abs(image - new_image		).sum() < 1e-5, "Models don't have the same forward pass"







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' ,			torch_dtype=torch.floataa		)
												pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     '''cyberpunk 2077'''
												_UpperCamelCase			     =     load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
												_UpperCamelCase			     =     torch.manual_seed(0		)
												_UpperCamelCase			     =     pipe.dual_guided(
												    prompt=_A ,			image=_A ,			text_to_image_strength=0.75 ,			generator=_A ,			guidance_scale=7.5 ,			num_inference_steps=50 ,			output_type='''numpy''' ,			).images

												_UpperCamelCase			     =     image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												_UpperCamelCase			     =     np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]		)

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

												_UpperCamelCase			     =     '''A painting of a squirrel eating a burger '''
												_UpperCamelCase			     =     torch.manual_seed(0		)
												_UpperCamelCase			     =     pipe.text_to_image(
												    prompt=_A ,			generator=_A ,			guidance_scale=7.5 ,			num_inference_steps=50 ,			output_type='''numpy'''		).images

												_UpperCamelCase			     =     image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												_UpperCamelCase			     =     np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]		)

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

												_UpperCamelCase			     =     pipe.image_variation(_A ,			generator=_A ,			output_type='''numpy'''		).images

												_UpperCamelCase			     =     image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												_UpperCamelCase			     =     np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]		)

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




'''simple docstring'''






# 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

__UpperCAmelCase       							=				'''tiny-wmt19-en-ru'''

# Build

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

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

		__UpperCAmelCase       							=				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,
		)

__UpperCAmelCase       							=				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,
)

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

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