





'''simple docstring'''




import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class UpperCAmelCase							(		lowerCAmelCase__					,			lowerCAmelCase__					,			lowerCAmelCase__					,			unittest.TestCase							):
    _A :			Optional[Any]						   =       StableUnCLIPPipeline
    _A :			Any						   =       TEXT_TO_IMAGE_PARAMS
    _A :			Optional[int]						   =       TEXT_TO_IMAGE_BATCH_PARAMS
    _A :			str						   =       TEXT_TO_IMAGE_IMAGE_PARAMS
    _A :			int						   =       TEXT_TO_IMAGE_IMAGE_PARAMS

    # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
    _A :			str						   =       False



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   32
     __UpperCAmelCase					=   embedder_hidden_size

     # prior components

     torch.manual_seed(0					)
     __UpperCAmelCase					=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

     torch.manual_seed(0					)
     __UpperCAmelCase					=   CLIPTextModelWithProjection(
         CLIPTextConfig(
             bos_token_id=0     ,			eos_token_id=2     ,			hidden_size=_lowerCamelCase     ,			projection_dim=_lowerCamelCase     ,			intermediate_size=37     ,			layer_norm_eps=1E-05     ,			num_attention_heads=4     ,			num_hidden_layers=5     ,			pad_token_id=1     ,			vocab_size=1_000     ,			)					)

     torch.manual_seed(0					)
     __UpperCAmelCase					=   PriorTransformer(
         num_attention_heads=2     ,			attention_head_dim=12     ,			embedding_dim=_lowerCamelCase     ,			num_layers=1     ,			)

     torch.manual_seed(0					)
     __UpperCAmelCase					=   DDPMScheduler(
         variance_type='fixed_small_log'     ,			prediction_type='sample'     ,			num_train_timesteps=1_000     ,			clip_sample=_lowerCamelCase     ,			clip_sample_range=5.0     ,			beta_schedule='squaredcos_cap_v2'     ,			)

     # regular denoising components

     torch.manual_seed(0					)
     __UpperCAmelCase					=   StableUnCLIPImageNormalizer(embedding_dim=_lowerCamelCase					)
     __UpperCAmelCase					=   DDPMScheduler(beta_schedule='squaredcos_cap_v2'					)

     torch.manual_seed(0					)
     __UpperCAmelCase					=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

     torch.manual_seed(0					)
     __UpperCAmelCase					=   CLIPTextModel(
         CLIPTextConfig(
             bos_token_id=0     ,			eos_token_id=2     ,			hidden_size=_lowerCamelCase     ,			projection_dim=32     ,			intermediate_size=37     ,			layer_norm_eps=1E-05     ,			num_attention_heads=4     ,			num_hidden_layers=5     ,			pad_token_id=1     ,			vocab_size=1_000     ,			)					)

     torch.manual_seed(0					)
     __UpperCAmelCase					=   UNetaDConditionModel(
         sample_size=32     ,			in_channels=4     ,			out_channels=4     ,			down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')     ,			up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')     ,			block_out_channels=(32, 64)     ,			attention_head_dim=(2, 4)     ,			class_embed_type='projection'     ,			projection_class_embeddings_input_dim=embedder_projection_dim * 2     ,			cross_attention_dim=_lowerCamelCase     ,			layers_per_block=1     ,			upcast_attention=_lowerCamelCase     ,			use_linear_projection=_lowerCamelCase     ,			)

     torch.manual_seed(0					)
     __UpperCAmelCase					=   DDIMScheduler(
         beta_schedule='scaled_linear'     ,			beta_start=0.0_0_0_8_5     ,			beta_end=0.0_1_2     ,			prediction_type='v_prediction'     ,			set_alpha_to_one=_lowerCamelCase     ,			steps_offset=1     ,			)

     torch.manual_seed(0					)
     __UpperCAmelCase					=   AutoencoderKL()

     __UpperCAmelCase					=   {
         # prior components
         'prior_tokenizer': prior_tokenizer,
         'prior_text_encoder': prior_text_encoder,
         'prior': prior,
         'prior_scheduler': prior_scheduler,
         # image noising components
         'image_normalizer': image_normalizer,
         'image_noising_scheduler': image_noising_scheduler,
         # regular denoising components
         'tokenizer': tokenizer,
         'text_encoder': text_encoder,
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
     }

     return components



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A=0					):





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



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   torch_device == 'cpu'

     self._test_attention_slicing_forward_pass(test_max_difference=_lowerCamelCase					)




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   torch_device in ['cpu', 'mps']

     self._test_inference_batch_single_identical(test_max_difference=_lowerCamelCase					)






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



    def 					__lowerCamelCase						(  self					):
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'					)

     __UpperCAmelCase					=   StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'     ,			torch_dtype=torch.floataa					)
     pipe.to(_lowerCamelCase					)
     pipe.set_progress_bar_config(disable=_lowerCamelCase					)
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __UpperCAmelCase					=   torch.Generator(device='cpu'					).manual_seed(0					)
     __UpperCAmelCase					=   pipe('anime turle'     ,			generator=_lowerCamelCase     ,			output_type='np'					)

     __UpperCAmelCase					=   output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(_lowerCamelCase     ,			_lowerCamelCase					)




    def 					__lowerCamelCase						(  self					):
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     __UpperCAmelCase					=   StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'     ,			torch_dtype=torch.floataa					)
     __UpperCAmelCase					=   pipe.to(_lowerCamelCase					)
     pipe.set_progress_bar_config(disable=_lowerCamelCase					)
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __UpperCAmelCase					=   pipe(
         'anime turtle'     ,			prior_num_inference_steps=2     ,			num_inference_steps=2     ,			output_type='np'     ,			)

     __UpperCAmelCase					=   torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9







'''simple docstring'''




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class UpperCAmelCase							(		UpperCAmelCase_					,			unittest.TestCase							):
    _A :			List[str]						   =       MgpstrTokenizer
    _A :			Any						   =       False
    _A :			List[Any]						   =       {}
    _A :			int						   =       False



    def 					__lowerCamelCase						(  self					):
     super().setUp()

     # fmt: off
     __UpperCAmelCase					=   ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
     # fmt: on
     __UpperCAmelCase					=   dict(zip(__A     ,			range(len(__A					)					)					)					)

     __UpperCAmelCase					=   os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']					)
     with open(self.vocab_file     ,			'w'     ,			encoding='utf-8'					) as fp:
      fp.write(json.dumps(__A					) + '\n'					)



    def 					__lowerCamelCase						(  self     ,			**__A					):
     return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,			**__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   'tester'
     __UpperCAmelCase					=   'tester'
     return input_text, output_text



    @unittest.skip('MGP-STR always lower cases letters.'					)
    def 					__lowerCamelCase						(  self					):
     pass



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_tokenizers(do_lower_case=__A					)
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}'					):
       __UpperCAmelCase					=   '[SPECIAL_TOKEN]'

       tokenizer.add_special_tokens({'cls_token': special_token}					)
       __UpperCAmelCase					=   tokenizer.encode([special_token]     ,			add_special_tokens=__A					)
       self.assertEqual(len(__A					)     ,			1					)

       __UpperCAmelCase					=   tokenizer.decode(__A     ,			skip_special_tokens=__A					)
       self.assertTrue(special_token not in decoded					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_tokenizers()
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}'					):
       __UpperCAmelCase					,    __UpperCAmelCase					=   self.get_input_output_texts(__A					)

       __UpperCAmelCase					=   tokenizer.tokenize(__A					)
       __UpperCAmelCase					=   tokenizer.convert_tokens_to_ids(__A					)
       __UpperCAmelCase					=   tokenizer.encode(__A     ,			add_special_tokens=__A					)
       self.assertListEqual(__A     ,			__A					)

       __UpperCAmelCase					=   tokenizer.convert_ids_to_tokens(__A					)
       self.assertNotEqual(len(__A					)     ,			0					)
       __UpperCAmelCase					=   tokenizer.decode(__A					)
       self.assertIsInstance(__A     ,			__A					)

       self.assertEqual(text_a.replace(' '     ,			''					)     ,			__A					)



    @unittest.skip('MGP-STR tokenizer only handles one sequence.'					)
    def 					__lowerCamelCase						(  self					):
     pass




    @unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'					)
    def 					__lowerCamelCase						(  self					):
     pass





