





"""simple docstring"""





import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors

@require_tokenizers
class 		lowercase					(					_UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      MvpTokenizer
      _SCREAMING_SNAKE_CASE					=      MvpTokenizerFast
      _SCREAMING_SNAKE_CASE					=      True
      _SCREAMING_SNAKE_CASE					=      filter_roberta_detectors





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

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





      def     _snake_case			(       self      ,						**lowercase )		->     List[Any]:
            kwargs.update(self.special_tokens_map )
            return self.tokenizer_class.from_pretrained(self.tmpdirname      ,						**lowercase )





      def     _snake_case			(       self      ,						**lowercase )		->     Dict:
            kwargs.update(self.special_tokens_map )
            return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     Any:
            return "lower newer", "lower newer"





      @cached_property
      def     _snake_case			(       self )		->     int:
            return MvpTokenizer.from_pretrained("""RUCAIBox/mvp""" )





      @cached_property
      def     _snake_case			(       self )		->     int:
            return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp""" )





      @require_torch
      def     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
            lowerCAmelCase  						=       [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

                  self.assertEqual((2, 9)      ,						batch.input_ids.shape )
                  self.assertEqual((2, 9)      ,						batch.attention_mask.shape )
                  lowerCAmelCase  						=       batch.input_ids.tolist()[0]
                  self.assertListEqual(lowercase      ,						lowercase )
                  # Test that special tokens are reset





      @require_torch
      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase  						=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  lowerCAmelCase  						=       tokenizer(lowercase      ,						padding=lowercase      ,						return_tensors="""pt""" )
                  # check if input_ids are returned and no labels
                  self.assertIn("""input_ids"""      ,						lowercase )
                  self.assertIn("""attention_mask"""      ,						lowercase )
                  self.assertNotIn("""labels"""      ,						lowercase )
                  self.assertNotIn("""decoder_attention_mask"""      ,						lowercase )





      @require_torch
      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       [
                """Summary of the text.""",
                """Another summary.""",
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  lowerCAmelCase  						=       tokenizer(text_target=lowercase      ,						max_length=32      ,						padding="""max_length"""      ,						return_tensors="""pt""" )
                  self.assertEqual(32      ,						targets["""input_ids"""].shape[1] )





      @require_torch
      def     _snake_case			(       self )		->     Tuple:
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  lowerCAmelCase  						=       tokenizer(
                      ["""I am a small frog""" * 1_024, """I am a small frog"""]      ,						padding=lowercase      ,						truncation=lowercase      ,						return_tensors="""pt""" )
                  self.assertIsInstance(lowercase      ,						lowercase )
                  self.assertEqual(batch.input_ids.shape      ,						(2, 1_024) )





      @require_torch
      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       ["""A long paragraph for summarization."""]
            lowerCAmelCase  						=       [
                """Summary of the text.""",
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                  lowerCAmelCase  						=       tokenizer(lowercase      ,						text_target=lowercase      ,						return_tensors="""pt""" )
                  lowerCAmelCase  						=       inputs["""input_ids"""]
                  lowerCAmelCase  						=       inputs["""labels"""]
                  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() )





      def     _snake_case			(       self )		->     Any:
            pass





      def     _snake_case			(       self )		->     Any:
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
                        lowerCAmelCase  						=       self.rust_tokenizer_class.from_pretrained(lowercase      ,						**lowercase )
                        lowerCAmelCase  						=       self.tokenizer_class.from_pretrained(lowercase      ,						**lowercase )
                        lowerCAmelCase  						=       """A, <mask> AllenNLP sentence."""
                        lowerCAmelCase  						=       tokenizer_r.encode_plus(lowercase      ,						add_special_tokens=lowercase      ,						return_token_type_ids=lowercase )
                        lowerCAmelCase  						=       tokenizer_p.encode_plus(lowercase      ,						add_special_tokens=lowercase      ,						return_token_type_ids=lowercase )

                        # token_type_ids should put 0 everywhere
                        self.assertEqual(sum(tokens_r["""token_type_ids"""] )      ,						sum(tokens_p["""token_type_ids"""] ) )

                        # attention_mask should put 1 everywhere, so sum over length should be 1
                        self.assertEqual(
                            sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] )      ,						sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] )      ,						)

                        lowerCAmelCase  						=       tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
                        lowerCAmelCase  						=       tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )

                        # Rust correctly handles the space before the mask while python doesnt
                        self.assertSequenceEqual(tokens_p["""input_ids"""]      ,						[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
                        self.assertSequenceEqual(tokens_r["""input_ids"""]      ,						[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )

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








import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Any       					=KandinskyVaaPriorPipeline
							lowercase_       :       str       					=['''prompt''']
							lowercase_       :       Union[str, Any]       					=['''prompt''', '''negative_prompt''']
							lowercase_       :       Dict       					=[
							    '''num_images_per_prompt''',
							    '''generator''',
							    '''num_inference_steps''',
							    '''latents''',
							    '''negative_prompt''',
							    '''guidance_scale''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_       :       List[str]       					=False




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return self.time_input_dim




							@property
							def  A__      (				self):
												return self.time_input_dim * 4




							@property
							def  A__      (				self):
												return 1_0_0




							@property
							def  A__      (				self):
												lowercase							=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
												return tokenizer




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						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(A__)




							@property
							def  A__      (				self):
												torch.manual_seed(0)

												lowercase							=						{
												    '''num_attention_heads''': 2,
												    '''attention_head_dim''': 1_2,
												    '''embedding_dim''': self.text_embedder_hidden_size,
												    '''num_layers''': 1,
												}

												lowercase							=						PriorTransformer(**A__)
												# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
												lowercase							=						nn.Parameter(torch.ones(model.clip_std.shape))
												return model




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						CLIPVisionConfig(
												    hidden_size=self.text_embedder_hidden_size   ,image_size=2_2_4   ,projection_dim=self.text_embedder_hidden_size   ,intermediate_size=3_7   ,num_attention_heads=4   ,num_channels=3   ,num_hidden_layers=5   ,patch_size=1_4   ,)

												lowercase							=						CLIPVisionModelWithProjection(A__)
												return model




							@property
							def  A__      (				self):
												lowercase							=						CLIPImageProcessor(
												    crop_size=2_2_4   ,do_center_crop=A__   ,do_normalize=A__   ,do_resize=A__   ,image_mean=[0.48145466, 0.4578275, 0.40821073]   ,image_std=[0.26862954, 0.26130258, 0.27577711]   ,resample=3   ,size=2_2_4   ,)

												return image_processor




							def  A__      (				self):
												lowercase							=						self.dummy_prior
												lowercase							=						self.dummy_image_encoder
												lowercase							=						self.dummy_text_encoder
												lowercase							=						self.dummy_tokenizer
												lowercase							=						self.dummy_image_processor

												lowercase							=						UnCLIPScheduler(
												    variance_type='''fixed_small_log'''   ,prediction_type='''sample'''   ,num_train_timesteps=1_0_0_0   ,clip_sample=A__   ,clip_sample_range=10.0   ,)

												lowercase							=						{
												    '''prior''': prior,
												    '''image_encoder''': image_encoder,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''scheduler''': scheduler,
												    '''image_processor''': image_processor,
												}

												return components




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)
												lowercase							=						{
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''guidance_scale''': 4.0,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''np''',
												}
												return inputs




							def  A__      (				self):
												lowercase							=						'''cpu'''

												lowercase							=						self.get_dummy_components()

												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)

												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						pipe(**self.get_dummy_inputs(A__))
												lowercase							=						output.image_embeds

												lowercase							=						pipe(
												    **self.get_dummy_inputs(A__)   ,return_dict=A__   ,)[0]

												lowercase							=						image[0, -1_0:]
												lowercase							=						image_from_tuple[0, -1_0:]

												assert image.shape == (1, 3_2)

												lowercase							=						np.array(
												    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156])

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




							@skip_mps
							def  A__      (				self):
												lowercase							=						torch_device == '''cpu'''
												lowercase							=						True
												lowercase							=						False

												self._test_inference_batch_single_identical(
												    test_max_difference=A__   ,relax_max_difference=A__   ,test_mean_pixel_difference=A__   ,)





							@skip_mps
							def  A__      (				self):
												lowercase							=						torch_device == '''cpu'''
												lowercase							=						False

												self._test_attention_slicing_forward_pass(
												    test_max_difference=A__   ,test_mean_pixel_difference=A__   ,)




