
"""simple docstring"""



import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort

class       lowerCAmelCase__				(						A_ ,	unittest.TestCase	):
     # FIXME: add fast tests
     pass






@nightly
@require_onnxruntime
@require_torch_gpu
class       lowerCAmelCase__				(						unittest.TestCase	):







     @property
     def      lowercase	(							self			:			int			):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )







     @property
     def      lowercase	(							self			:			str			):
       _snake_case					     =       ort.SessionOptions()
       _snake_case					     =       False
       return options







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo.png'''			)
       _snake_case					     =       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''			)
       _snake_case					     =       OnnxStableDiffusionInpaintPipeline.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''    ,      revision='''onnx'''    ,      safety_checker=_lowerCamelCase    ,      feature_extractor=_lowerCamelCase    ,      provider=self.gpu_provider    ,      sess_options=self.gpu_options    ,      )
       pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       '''A red cat sitting on a park bench'''

       _snake_case					     =       np.random.RandomState(0			)
       _snake_case					     =       pipe(
           prompt=_lowerCamelCase    ,      image=_lowerCamelCase    ,      mask_image=_lowerCamelCase    ,      guidance_scale=7.5    ,      num_inference_steps=10    ,      generator=_lowerCamelCase    ,      output_type='''np'''    ,      )
       _snake_case					     =       output.images
       _snake_case					     =       images[0, 255:258, 255:258, -1]

       assert images.shape == (1, 512, 512, 3)
       _snake_case					     =       np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3







     def      lowercase	(							self			:			int			):
       _snake_case					     =       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo.png'''			)
       _snake_case					     =       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''			)
       _snake_case					     =       LMSDiscreteScheduler.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''    ,      subfolder='''scheduler'''    ,      revision='''onnx'''			)
       _snake_case					     =       OnnxStableDiffusionInpaintPipeline.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''    ,      revision='''onnx'''    ,      scheduler=_lowerCamelCase    ,      safety_checker=_lowerCamelCase    ,      feature_extractor=_lowerCamelCase    ,      provider=self.gpu_provider    ,      sess_options=self.gpu_options    ,      )
       pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       '''A red cat sitting on a park bench'''

       _snake_case					     =       np.random.RandomState(0			)
       _snake_case					     =       pipe(
           prompt=_lowerCamelCase    ,      image=_lowerCamelCase    ,      mask_image=_lowerCamelCase    ,      guidance_scale=7.5    ,      num_inference_steps=20    ,      generator=_lowerCamelCase    ,      output_type='''np'''    ,      )
       _snake_case					     =       output.images
       _snake_case					     =       images[0, 255:258, 255:258, -1]

       assert images.shape == (1, 512, 512, 3)
       _snake_case					     =       np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3





"""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=1000,
    tgt_vocab_size=1000,
    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



