





"""simple docstring"""
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class       a							(   __SCREAMING_SNAKE_CASE   , unittest.TestCase  ):
 snake_case__             =       LDMTextToImagePipeline
 snake_case__             =       TEXT_TO_IMAGE_PARAMS - {
     '''negative_prompt''',
     '''negative_prompt_embeds''',
     '''cross_attention_kwargs''',
     '''prompt_embeds''',
 }
 snake_case__             =       PipelineTesterMixin.required_optional_params - {
     '''num_images_per_prompt''',
     '''callback''',
     '''callback_steps''',
 }
 snake_case__             =       TEXT_TO_IMAGE_BATCH_PARAMS
 snake_case__             =       False



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  torch.manual_seed(0     )
  lowerCAmelCase    				= UNetaDConditionModel(
      block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=4				,       out_channels=4				,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,       cross_attention_dim=32				,       )
  lowerCAmelCase    				= DDIMScheduler(
      beta_start=0.00_085				,       beta_end=0.012				,       beta_schedule='scaled_linear'				,       clip_sample=__lowerCamelCase				,       set_alpha_to_one=__lowerCamelCase				,       )
  torch.manual_seed(0     )
  lowerCAmelCase    				= AutoencoderKL(
      block_out_channels=(32, 64)				,       in_channels=3				,       out_channels=3				,       down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')				,       up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')				,       latent_channels=4				,       )
  torch.manual_seed(0     )
  lowerCAmelCase    				= CLIPTextConfig(
      bos_token_id=0				,       eos_token_id=2				,       hidden_size=32				,       intermediate_size=37				,       layer_norm_eps=1E-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=10_00				,       )
  lowerCAmelCase    				= CLIPTextModel(__lowerCamelCase     )
  lowerCAmelCase    				= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

  lowerCAmelCase    				= {
      'unet': unet,
      'scheduler': scheduler,
      'vqvae': vae,
      'bert': text_encoder,
      'tokenizer': tokenizer,
  }
  return components



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







  """simple docstring"""






  if str(__lowerCamelCase     ).startswith('mps'     ):
   lowerCAmelCase    				= torch.manual_seed(__lowerCamelCase     )
  else:
   lowerCAmelCase    				= torch.Generator(device=__lowerCamelCase     ).manual_seed(__lowerCamelCase     )
  lowerCAmelCase    				= {
      'prompt': 'A painting of a squirrel eating a burger',
      'generator': generator,
      'num_inference_steps': 2,
      'guidance_scale': 6.0,
      'output_type': 'numpy',
  }
  return inputs



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= 'cpu'  # ensure determinism for the device-dependent torch.Generator

  lowerCAmelCase    				= self.get_dummy_components()
  lowerCAmelCase    				= LDMTextToImagePipeline(**__lowerCamelCase     )
  pipe.to(__lowerCamelCase     )
  pipe.set_progress_bar_config(disable=__lowerCamelCase     )

  lowerCAmelCase    				= self.get_dummy_inputs(__lowerCamelCase     )
  lowerCAmelCase    				= pipe(**__lowerCamelCase     ).images
  lowerCAmelCase    				= image[0, -3:, -3:, -1]

  assert image.shape == (1, 16, 16, 3)
  lowerCAmelCase    				= np.array([0.6_101, 0.6_156, 0.5_622, 0.4_895, 0.6_661, 0.3_804, 0.5_748, 0.6_136, 0.5_014]     )

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







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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=torch.floataa				,       _snake_case=0     ):







  """simple docstring"""






  lowerCAmelCase    				= torch.manual_seed(__lowerCamelCase     )
  lowerCAmelCase    				= np.random.RandomState(__lowerCamelCase     ).standard_normal((1, 4, 32, 32)     )
  lowerCAmelCase    				= torch.from_numpy(__lowerCamelCase     ).to(device=__lowerCamelCase				,       dtype=__lowerCamelCase     )
  lowerCAmelCase    				= {
      'prompt': 'A painting of a squirrel eating a burger',
      'latents': latents,
      'generator': generator,
      'num_inference_steps': 3,
      'guidance_scale': 6.0,
      'output_type': 'numpy',
  }
  return inputs



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'     ).to(__lowerCamelCase     )
  pipe.set_progress_bar_config(disable=__lowerCamelCase     )

  lowerCAmelCase    				= self.get_inputs(__lowerCamelCase     )
  lowerCAmelCase    				= pipe(**__lowerCamelCase     ).images
  lowerCAmelCase    				= image[0, -3:, -3:, -1].flatten()

  assert image.shape == (1, 2_56, 2_56, 3)
  lowerCAmelCase    				= np.array([0.51_825, 0.52_850, 0.52_543, 0.54_258, 0.52_304, 0.52_569, 0.54_363, 0.55_276, 0.56_878]     )
  lowerCAmelCase    				= np.abs(expected_slice - image_slice     ).max()
  assert max_diff < 1E-3







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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=torch.floataa				,       _snake_case=0     ):







  """simple docstring"""






  lowerCAmelCase    				= torch.manual_seed(__lowerCamelCase     )
  lowerCAmelCase    				= np.random.RandomState(__lowerCamelCase     ).standard_normal((1, 4, 32, 32)     )
  lowerCAmelCase    				= torch.from_numpy(__lowerCamelCase     ).to(device=__lowerCamelCase				,       dtype=__lowerCamelCase     )
  lowerCAmelCase    				= {
      'prompt': 'A painting of a squirrel eating a burger',
      'latents': latents,
      'generator': generator,
      'num_inference_steps': 50,
      'guidance_scale': 6.0,
      'output_type': 'numpy',
  }
  return inputs



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'     ).to(__lowerCamelCase     )
  pipe.set_progress_bar_config(disable=__lowerCamelCase     )

  lowerCAmelCase    				= self.get_inputs(__lowerCamelCase     )
  lowerCAmelCase    				= pipe(**__lowerCamelCase     ).images[0]

  lowerCAmelCase    				= load_numpy(
      'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'     )
  lowerCAmelCase    				= np.abs(expected_image - image     ).max()
  assert max_diff < 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

snake_case   					=				'''tiny-wmt19-en-ru'''

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
     snake_case   					=				Path(tmpdirname)
     snake_case   					=				build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
     snake_case   					=				build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
     snake_case   					=				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   					=				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   					=				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   					=				FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

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





