


'''simple docstring'''







import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


class  lowerCAmelCase_						(       snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :Any												=ShapEPipeline
  a_       :Union[str, Any]												=["""prompt"""]
  a_       :Union[str, Any]												=["""prompt"""]
  a_       :Dict												=[
      """num_images_per_prompt""",
      """num_inference_steps""",
      """generator""",
      """latents""",
      """guidance_scale""",
      """frame_size""",
      """output_type""",
      """return_dict""",
  ]
  a_       :Optional[int]												=False

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





       '''simple docstring'''

       return 3_2

  @property
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       return 3_2

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





       '''simple docstring'''

       return self.time_input_dim * 4

  @property
  def __a						(					self     :							Any						):





       '''simple docstring'''

       return 8

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





       '''simple docstring'''

       __a       =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)
       return tokenizer

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





       '''simple docstring'''

       torch.manual_seed(0						)
       __a       =   CLIPTextConfig(
           bos_token_id=0				,							eos_token_id=2				,							hidden_size=self.text_embedder_hidden_size				,							projection_dim=self.text_embedder_hidden_size				,							intermediate_size=3_7				,							layer_norm_eps=1E-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1_0_0_0				,							)
       return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE__						)

  @property
  def __a						(					self     :							Any						):





       '''simple docstring'''

       torch.manual_seed(0						)

       __a       =   {
           """num_attention_heads""": 2,
           """attention_head_dim""": 1_6,
           """embedding_dim""": self.time_input_dim,
           """num_embeddings""": 3_2,
           """embedding_proj_dim""": self.text_embedder_hidden_size,
           """time_embed_dim""": self.time_embed_dim,
           """num_layers""": 1,
           """clip_embed_dim""": self.time_input_dim * 2,
           """additional_embeddings""": 0,
           """time_embed_act_fn""": """gelu""",
           """norm_in_type""": """layer""",
           """encoder_hid_proj_type""": None,
           """added_emb_type""": None,
       }

       __a       =   PriorTransformer(**SCREAMING_SNAKE_CASE__						)
       return model

  @property
  def __a						(					self     :							int						):





       '''simple docstring'''

       torch.manual_seed(0						)

       __a       =   {
           """param_shapes""": (
               (self.renderer_dim, 9_3),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
           ),
           """d_latent""": self.time_input_dim,
           """d_hidden""": self.renderer_dim,
           """n_output""": 1_2,
           """background""": (
               0.1,
               0.1,
               0.1,
           ),
       }
       __a       =   ShapERenderer(**SCREAMING_SNAKE_CASE__						)
       return model

  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   self.dummy_prior
       __a       =   self.dummy_text_encoder
       __a       =   self.dummy_tokenizer
       __a       =   self.dummy_renderer

       __a       =   HeunDiscreteScheduler(
           beta_schedule="""exp"""				,							num_train_timesteps=1_0_2_4				,							prediction_type="""sample"""				,							use_karras_sigmas=SCREAMING_SNAKE_CASE__				,							clip_sample=SCREAMING_SNAKE_CASE__				,							clip_sample_range=1.0				,							)
       __a       =   {
           """prior""": prior,
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """renderer""": renderer,
           """scheduler""": scheduler,
       }

       return components

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





       '''simple docstring'''

       if str(SCREAMING_SNAKE_CASE__						).startswith("""mps"""						):
            __a       =   torch.manual_seed(SCREAMING_SNAKE_CASE__						)
       else:
            __a       =   torch.Generator(device=SCREAMING_SNAKE_CASE__						).manual_seed(SCREAMING_SNAKE_CASE__						)
       __a       =   {
           """prompt""": """horse""",
           """generator""": generator,
           """num_inference_steps""": 1,
           """frame_size""": 3_2,
           """output_type""": """np""",
       }
       return inputs

  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   """cpu"""

       __a       =   self.get_dummy_components()

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

       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)						)
       __a       =   output.images[0]
       __a       =   image[0, -3:, -3:, -1]

       assert image.shape == (2_0, 3_2, 3_2, 3)

       __a       =   np.array(
           [
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
               0.0_0_0_3_9_2_1_6,
           ]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2

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





       '''simple docstring'''

       self._test_inference_batch_consistent(batch_sizes=[1, 2]						)

  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   torch_device == """cpu"""
       __a       =   True

       self._test_inference_batch_single_identical(
           batch_size=2				,							test_max_difference=SCREAMING_SNAKE_CASE__				,							relax_max_difference=SCREAMING_SNAKE_CASE__				,							)


  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   self.get_dummy_components()
       __a       =   self.pipeline_class(**SCREAMING_SNAKE_CASE__						)
       __a       =   pipe.to(SCREAMING_SNAKE_CASE__						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   1
       __a       =   2

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)

       for key in inputs.keys():
            if key in self.batch_params:
                 __a       =   batch_size * [inputs[key]]

       __a       =   pipe(**SCREAMING_SNAKE_CASE__				,							num_images_per_prompt=SCREAMING_SNAKE_CASE__						)[0]

       assert images.shape[0] == batch_size * num_images_per_prompt


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





  """simple docstring"""



  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

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


  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a       =   load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/shap_e/test_shap_e_np_out.npy"""						)
       __a       =   ShapEPipeline.from_pretrained("""openai/shap-e"""						)
       __a       =   pipe.to(SCREAMING_SNAKE_CASE__						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   torch.Generator(device=SCREAMING_SNAKE_CASE__						).manual_seed(0						)

       __a       =   pipe(
           """a shark"""				,							generator=SCREAMING_SNAKE_CASE__				,							guidance_scale=1_5.0				,							num_inference_steps=6_4				,							frame_size=6_4				,							output_type="""np"""				,							).images[0]

       assert images.shape == (2_0, 6_4, 6_4, 3)

       assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__				,							SCREAMING_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>"""]						)






