


'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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  lowerCAmelCase_						(       _snake_case		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :int												=KandinskyVaaPipeline
  a_       :List[str]												=[
      """image_embeds""",
      """negative_image_embeds""",
  ]
  a_       :str												=["""image_embeds""", """negative_image_embeds"""]
  a_       :Optional[int]												=[
      """generator""",
      """height""",
      """width""",
      """latents""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  a_       :str												=False

  @property
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       return 3_2

  @property
  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       return 3_2

  @property
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       return self.time_input_dim

  @property
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       return self.time_input_dim * 4

  @property
  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       return 1_0_0

  @property
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       torch.manual_seed(0						)

       __a       =   {
           "in_channels": 4,
           # Out channels is double in channels because predicts mean and variance
           "out_channels": 8,
           "addition_embed_type": "image",
           "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,
       }

       __a       =   UNetaDConditionModel(**snake_case_						)
       return model

  @property
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       return {
           "block_out_channels": [3_2, 6_4],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 1_2,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }

  @property
  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

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

  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   self.dummy_unet
       __a       =   self.dummy_movq

       __a       =   DDIMScheduler(
           num_train_timesteps=1_0_0_0				,							beta_schedule="""linear"""				,							beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							clip_sample=snake_case_				,							set_alpha_to_one=snake_case_				,							steps_offset=1				,							prediction_type="""epsilon"""				,							thresholding=snake_case_				,							)

       __a       =   {
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }
       return components

  def __a						(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Any=0						):





       '''simple docstring'''

       __a       =   floats_tensor((1, self.text_embedder_hidden_size)				,							rng=random.Random(snake_case_						)						).to(snake_case_						)
       __a       =   floats_tensor((1, self.text_embedder_hidden_size)				,							rng=random.Random(seed + 1						)						).to(
           snake_case_						)
       if str(snake_case_						).startswith("""mps"""						):
            __a       =   torch.manual_seed(snake_case_						)
       else:
            __a       =   torch.Generator(device=snake_case_						).manual_seed(snake_case_						)
       __a       =   {
           "image_embeds": image_embeds,
           "negative_image_embeds": negative_image_embeds,
           "generator": generator,
           "height": 6_4,
           "width": 6_4,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs


  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a       =   "cpu"

       __a       =   self.get_dummy_components()

       __a       =   self.pipeline_class(**snake_case_						)
       __a       =   pipe.to(snake_case_						)

       pipe.set_progress_bar_config(disable=snake_case_						)

       __a       =   pipe(**self.get_dummy_inputs(snake_case_						)						)
       __a       =   output.images

       __a       =   pipe(
           **self.get_dummy_inputs(snake_case_						)				,							return_dict=snake_case_				,							)[0]

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

       assert image.shape == (1, 6_4, 6_4, 3)

       __a       =   np.array(
           [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6]						)

       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  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  def __a						(					self     :							Any						):





       '''simple docstring'''

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


  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""						)

       __a       =   KandinskyVaaPriorPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-2-prior"""				,							torch_dtype=torch.floataa						)
       pipe_prior.to(snake_case_						)

       __a       =   KandinskyVaaPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-2-decoder"""				,							torch_dtype=torch.floataa						)
       __a       =   pipeline.to(snake_case_						)
       pipeline.set_progress_bar_config(disable=snake_case_						)

       __a       =   "red cat, 4k photo"

       __a       =   torch.Generator(device="""cuda"""						).manual_seed(0						)
       __a       =   pipe_prior(
           snake_case_				,							generator=snake_case_				,							num_inference_steps=5				,							negative_prompt=""""""				,							).to_tuple()

       __a       =   torch.Generator(device="""cuda"""						).manual_seed(0						)
       __a       =   pipeline(
           image_embeds=snake_case_				,							negative_image_embeds=snake_case_				,							generator=snake_case_				,							num_inference_steps=1_0_0				,							output_type="""np"""				,							)

       __a       =   output.images[0]

       assert image.shape == (5_1_2, 5_1_2, 3)

       assert_mean_pixel_difference(snake_case_				,							snake_case_						)










'''simple docstring'''







import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class  lowerCAmelCase_						(       snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :Union[str, Any]												=LEDTokenizer
  a_       :List[Any]												=LEDTokenizerFast
  a_       :Union[str, Any]												=True

  def __a						(					self     :							Dict						):





       '''simple docstring'''

       super().setUp()
       __a       =   [
           """l""",
           """o""",
           """w""",
           """e""",
           """r""",
           """s""",
           """t""",
           """i""",
           """d""",
           """n""",
           """\u0120""",
           """\u0120l""",
           """\u0120n""",
           """\u0120lo""",
           """\u0120low""",
           """er""",
           """\u0120lowest""",
           """\u0120newer""",
           """\u0120wider""",
           """<unk>""",
       ]
       __a       =   dict(zip(SCREAMING_SNAKE_CASE__				,							range(len(SCREAMING_SNAKE_CASE__						)						)						)						)
       __a       =   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
       __a       =   {"""unk_token""": """<unk>"""}

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

  def __a						(					self     :							Optional[Any]				,							**SCREAMING_SNAKE_CASE__     :							Optional[Any]						):





       '''simple docstring'''

       kwargs.update(self.special_tokens_map						)
       return self.tokenizer_class.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							int				,							**SCREAMING_SNAKE_CASE__     :							Optional[int]						):





       '''simple docstring'''

       kwargs.update(self.special_tokens_map						)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       return "lower newer", "lower newer"

  @cached_property
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""						)

  @cached_property
  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""						)

  @require_torch
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       __a       =   [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __a       =   tokenizer(SCREAMING_SNAKE_CASE__				,							max_length=len(SCREAMING_SNAKE_CASE__						)				,							padding=SCREAMING_SNAKE_CASE__				,							return_tensors="""pt"""						)
            self.assertIsInstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

            self.assertEqual((2, 9)				,							batch.input_ids.shape						)
            self.assertEqual((2, 9)				,							batch.attention_mask.shape						)
            __a       =   batch.input_ids.tolist()[0]
            self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

  @require_torch
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __a       =   tokenizer(SCREAMING_SNAKE_CASE__				,							padding=SCREAMING_SNAKE_CASE__				,							return_tensors="""pt"""						)
            self.assertIn("""input_ids"""				,							SCREAMING_SNAKE_CASE__						)
            self.assertIn("""attention_mask"""				,							SCREAMING_SNAKE_CASE__						)
            self.assertNotIn("""labels"""				,							SCREAMING_SNAKE_CASE__						)
            self.assertNotIn("""decoder_attention_mask"""				,							SCREAMING_SNAKE_CASE__						)

  @require_torch
  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       __a       =   [
           """Summary of the text.""",
           """Another summary.""",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __a       =   tokenizer(text_target=SCREAMING_SNAKE_CASE__				,							max_length=3_2				,							padding="""max_length"""				,							return_tensors="""pt"""						)
            self.assertEqual(3_2				,							targets["""input_ids"""].shape[1]						)

  @require_torch
  def __a						(					self     :							Any						):





       '''simple docstring'''

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __a       =   tokenizer(
                ["""I am a small frog""" * 1_0_2_4, """I am a small frog"""]				,							padding=SCREAMING_SNAKE_CASE__				,							truncation=SCREAMING_SNAKE_CASE__				,							return_tensors="""pt"""						)
            self.assertIsInstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)
            self.assertEqual(batch.input_ids.shape				,							(2, 5_1_2_2)						)

  @require_torch
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   ["""A long paragraph for summarization."""]
       __a       =   [
           """Summary of the text.""",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __a       =   tokenizer(SCREAMING_SNAKE_CASE__				,							return_tensors="""pt"""						)
            __a       =   tokenizer(text_target=SCREAMING_SNAKE_CASE__				,							return_tensors="""pt"""						)
            __a       =   inputs["""input_ids"""]
            __a       =   targets["""input_ids"""]
            self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()						)
            self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()						)
            self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()						)
            self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()						)

  @require_torch
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __a       =   ["""Summary of the text.""", """Another summary."""]
            __a       =   [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

            __a       =   tokenizer(SCREAMING_SNAKE_CASE__				,							padding=SCREAMING_SNAKE_CASE__						)
            __a       =   [[0] * len(SCREAMING_SNAKE_CASE__						) for x in encoded_output["""input_ids"""]]
            __a       =   tokenizer.pad(SCREAMING_SNAKE_CASE__						)
            self.assertSequenceEqual(outputs["""global_attention_mask"""]				,							SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       pass


  def __a						(					self     :							Any						):





       '''simple docstring'''

       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
                 __a       =   self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
                 __a       =   self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
                 __a       =   """A, <mask> AllenNLP sentence."""
                 __a       =   tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__				,							add_special_tokens=SCREAMING_SNAKE_CASE__				,							return_token_type_ids=SCREAMING_SNAKE_CASE__						)
                 __a       =   tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__				,							add_special_tokens=SCREAMING_SNAKE_CASE__				,							return_token_type_ids=SCREAMING_SNAKE_CASE__						)
                 self.assertEqual(sum(tokens_r["""token_type_ids"""]						)				,							sum(tokens_p["""token_type_ids"""]						)						)
                 self.assertEqual(
                     sum(tokens_r["""attention_mask"""]						) / len(tokens_r["""attention_mask"""]						)				,							sum(tokens_p["""attention_mask"""]						) / len(tokens_p["""attention_mask"""]						)				,							)

                 __a       =   tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]						)
                 __a       =   tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]						)
                 self.assertSequenceEqual(tokens_p["""input_ids"""]				,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]						)
                 self.assertSequenceEqual(tokens_r["""input_ids"""]				,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]						)

                 self.assertSequenceEqual(
                     SCREAMING_SNAKE_CASE__				,							["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)
                 self.assertSequenceEqual(
                     SCREAMING_SNAKE_CASE__				,							["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]						)






