






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
     from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__				   =		get_tests_dir("fixtures/test_sentencepiece.model")

UpperCAmelCase__				   =		{"target_lang": "fi", "source_lang": "en"}
UpperCAmelCase__				   =		">>zh<<"
UpperCAmelCase__				   =		"Helsinki-NLP/"

if is_torch_available():
     UpperCAmelCase__				   =		"pt"
elif is_tf_available():
     UpperCAmelCase__				   =		"tf"
else:
     UpperCAmelCase__				   =		"jax"






@require_sentencepiece
class 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  MarianTokenizer
      __snake_case       =  False
      __snake_case       =  True







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->List[Any]:




       """simple docstring"""


       super().setUp()
       a		  =      ['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
       a		  =      dict(zip(__UpperCAmelCase ,					range(len(__UpperCAmelCase  )  )  )  )
       a		  =      Path(self.tmpdirname  )
       save_json(__UpperCAmelCase ,					save_dir / VOCAB_FILES_NAMES['''vocab''']  )
       save_json(__UpperCAmelCase ,					save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file''']  )
       if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
        copyfile(__UpperCAmelCase ,					save_dir / VOCAB_FILES_NAMES['''source_spm''']  )
        copyfile(__UpperCAmelCase ,					save_dir / VOCAB_FILES_NAMES['''target_spm''']  )

       a		  =      MarianTokenizer.from_pretrained(self.tmpdirname  )
       tokenizer.save_pretrained(self.tmpdirname  )







      def       __lowerCAmelCase					(      self      :						Optional[int] ,					**__UpperCAmelCase      :						Dict  )    ->MarianTokenizer:




       """simple docstring"""


       return MarianTokenizer.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Tuple ,					__UpperCAmelCase      :						List[Any]  )    ->List[str]:




       """simple docstring"""


       return (
           "This is a test",
           "This is a test",
       )







      def       __lowerCAmelCase					(      self      :						Dict  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      '''</s>'''
       a		  =      0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase  ) ,					__UpperCAmelCase  )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase  ) ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Dict:




       """simple docstring"""


       a		  =      list(self.get_tokenizer().get_vocab().keys()  )

       self.assertEqual(vocab_keys[0] ,					'''</s>'''  )
       self.assertEqual(vocab_keys[1] ,					'''<unk>'''  )
       self.assertEqual(vocab_keys[-1] ,					'''<pad>'''  )
       self.assertEqual(len(__UpperCAmelCase  ) ,					9  )







      def       __lowerCAmelCase					(      self      :						Dict  )    ->List[str]:




       """simple docstring"""


       self.assertEqual(self.get_tokenizer().vocab_size ,					9  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de"""  )
       a		  =      en_de_tokenizer(['''I am a small frog'''] ,					return_tensors=__UpperCAmelCase  )
       self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase  )
       a		  =      [38, 121, 14, 697, 38_848, 0]
       self.assertListEqual(__UpperCAmelCase ,					batch.input_ids[0]  )

       a		  =      tempfile.mkdtemp()
       en_de_tokenizer.save_pretrained(__UpperCAmelCase  )
       a		  =      [x.name for x in Path(__UpperCAmelCase  ).glob('''*'''  )]
       self.assertIn('''source.spm''' ,					__UpperCAmelCase  )
       MarianTokenizer.from_pretrained(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      self.get_tokenizer()

       a		  =      tok(
           ['''I am a small frog''' * 1_000, '''I am a small frog'''] ,					padding=__UpperCAmelCase ,					truncation=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase  )
       self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase  )
       self.assertEqual(batch.input_ids.shape ,					(2, 512)  )







      def       __lowerCAmelCase					(      self      :						Any  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      self.get_tokenizer()
       a		  =      tok(['''I am a tiny frog''', '''I am a small frog'''] ,					padding=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase  )
       self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase  )
       self.assertEqual(batch_smaller.input_ids.shape ,					(2, 10)  )







      @slow
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Tuple:




       """simple docstring"""


       a		  =      {'''input_ids''': [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=__UpperCAmelCase ,					model_name='''Helsinki-NLP/opus-mt-en-de''' ,					revision='''1a8c2263da11e68e50938f97e10cd57820bd504c''' ,					decode_kwargs={'''use_source_tokenizer''': True} ,					)







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Dict:




       """simple docstring"""


       a		  =      MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs'''  )

       a		  =      '''Tämä on testi'''
       a		  =      '''This is a test'''

       a		  =      [76, 7, 2_047, 2]
       a		  =      [69, 12, 11, 940, 2]

       a		  =      tokenizer(__UpperCAmelCase  ).input_ids
       self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase  )

       a		  =      tokenizer(text_target=__UpperCAmelCase  ).input_ids
       self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase  )

       a		  =      tokenizer.decode(__UpperCAmelCase ,					skip_special_tokens=__UpperCAmelCase  )
       self.assertEqual(__UpperCAmelCase ,					__UpperCAmelCase  )










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 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  ShapEPipeline
      __snake_case       =  ['''prompt''']
      __snake_case       =  ['''prompt''']
      __snake_case       =  [
          '''num_images_per_prompt''',
          '''num_inference_steps''',
          '''generator''',
          '''latents''',
          '''guidance_scale''',
          '''frame_size''',
          '''output_type''',
          '''return_dict''',
      ]
      __snake_case       =  False







      @property
      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Union[str, Any]:




       """simple docstring"""


       return 32







      @property
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->List[str]:




       """simple docstring"""


       return 32







      @property
      def       __lowerCAmelCase					(      self      :						Any  )    ->Tuple:




       """simple docstring"""


       return self.time_input_dim * 4







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Optional[Any]:




       """simple docstring"""


       return 8







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->str:




       """simple docstring"""


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







      @property
      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->List[Any]:




       """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=37 ,					layer_norm_eps=1e-0_5 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					pad_token_id=1 ,					vocab_size=1_000 ,					)
       return CLIPTextModelWithProjection(__UpperCAmelCase  )







      @property
      def       __lowerCAmelCase					(      self      :						Dict  )    ->Union[str, Any]:




       """simple docstring"""


       torch.manual_seed(0  )

       a		  =      {
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 16,
           '''embedding_dim''': self.time_input_dim,
           '''num_embeddings''': 32,
           '''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(**__UpperCAmelCase  )
       return model







      @property
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->List[str]:




       """simple docstring"""


       torch.manual_seed(0  )

       a		  =      {
           '''param_shapes''': (
               (self.renderer_dim, 93),
               (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''': 12,
           '''background''': (
               0.1,
               0.1,
               0.1,
           ),
       }
       a		  =      ShapERenderer(**__UpperCAmelCase  )
       return model







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Any:




       """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_024 ,					prediction_type='''sample''' ,					use_karras_sigmas=__UpperCAmelCase ,					clip_sample=__UpperCAmelCase ,					clip_sample_range=1.0 ,					)
       a		  =      {
           '''prior''': prior,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''renderer''': renderer,
           '''scheduler''': scheduler,
       }

       return components







      def       __lowerCAmelCase					(      self      :						Tuple ,					__UpperCAmelCase      :						Optional[Any] ,					__UpperCAmelCase      :						str=0  )    ->Optional[int]:




       """simple docstring"""


       if str(__UpperCAmelCase  ).startswith('''mps'''  ):
        a		  =      torch.manual_seed(__UpperCAmelCase  )
       else:
        a		  =      torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
       a		  =      {
           '''prompt''': '''horse''',
           '''generator''': generator,
           '''num_inference_steps''': 1,
           '''frame_size''': 32,
           '''output_type''': '''np''',
       }
       return inputs







      def       __lowerCAmelCase					(      self      :						Dict  )    ->Optional[int]:




       """simple docstring"""


       a		  =      '''cpu'''

       a		  =      self.get_dummy_components()

       a		  =      self.pipeline_class(**__UpperCAmelCase  )
       a		  =      pipe.to(__UpperCAmelCase  )

       pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

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

       assert image.shape == (20, 32, 32, 3)

       a		  =      np.array(
           [
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
           ]  )

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







      def       __lowerCAmelCase					(      self      :						Dict  )    ->Optional[Any]:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Tuple:




       """simple docstring"""


       a		  =      torch_device == '''cpu'''
       a		  =      True

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







      def       __lowerCAmelCase					(      self      :						str  )    ->Optional[int]:




       """simple docstring"""


       a		  =      self.get_dummy_components()
       a		  =      self.pipeline_class(**__UpperCAmelCase  )
       a		  =      pipe.to(__UpperCAmelCase  )
       pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

       a		  =      1
       a		  =      2

       a		  =      self.get_dummy_inputs(__UpperCAmelCase  )

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

       a		  =      pipe(**__UpperCAmelCase ,					num_images_per_prompt=__UpperCAmelCase  )[0]

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






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






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						int  )    ->Any:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Union[str, Any]:




       """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(__UpperCAmelCase  )
       pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

       a		  =      torch.Generator(device=__UpperCAmelCase  ).manual_seed(0  )

       a		  =      pipe(
           '''a shark''' ,					generator=__UpperCAmelCase ,					guidance_scale=15.0 ,					num_inference_steps=64 ,					frame_size=64 ,					output_type='''np''' ,					).images[0]

       assert images.shape == (20, 64, 64, 3)

       assert_mean_pixel_difference(__UpperCAmelCase ,					__UpperCAmelCase  )


