





"""simple docstring"""





import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase       =	get_tests_dir('''fixtures/spiece.model''')





@require_sentencepiece
@require_tokenizers
class 			snake_case		(   __UpperCAmelCase ,     unittest.TestCase   ):
      lowerCamelCase__						= ErnieMTokenizer
      lowerCamelCase__						= False
      lowerCamelCase__						= True
      lowerCamelCase__						= False
      lowerCamelCase__						= False

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						):
       super().setUp()

       # We have a SentencePiece fixture for testing
       __SCREAMING_SNAKE_CASE :			Dict												=       ErnieMTokenizer(_lowerCamelCase						,							unk_token='''<unk>'''						,							pad_token='''<pad>'''						)
       tokenizer.save_pretrained(self.tmpdirname						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			int												=       '''this is a test'''
       __SCREAMING_SNAKE_CASE :			Dict												=       '''this is a test'''
       return input_text, output_text

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       '''<pad>'''
       __SCREAMING_SNAKE_CASE :			Dict												=       0

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			List[str]												=       list(self.get_tokenizer().get_vocab().keys()						)

       self.assertEqual(vocab_keys[0]						,							'''<pad>'''						)
       self.assertEqual(vocab_keys[1]						,							'''<unk>'''						)
       self.assertEqual(vocab_keys[-1]						,							'''▁eloquent'''						)
       self.assertEqual(len(_lowerCamelCase						)						,							3_0_0_0_0						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       self.assertEqual(self.get_tokenizer().vocab_size						,							3_0_0_0_0						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       if not self.test_rust_tokenizer:
        return

       __SCREAMING_SNAKE_CASE :			List[str]												=       self.get_tokenizer()
       __SCREAMING_SNAKE_CASE :			Dict												=       self.get_rust_tokenizer()

       __SCREAMING_SNAKE_CASE :			Dict												=       '''I was born in 92000, and this is falsé.'''

       __SCREAMING_SNAKE_CASE :			str												=       tokenizer.tokenize(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			int												=       rust_tokenizer.tokenize(_lowerCamelCase						)
       self.assertListEqual(_lowerCamelCase						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			List[str]												=       tokenizer.encode(_lowerCamelCase						,							add_special_tokens=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       rust_tokenizer.encode(_lowerCamelCase						,							add_special_tokens=_lowerCamelCase						)
       self.assertListEqual(_lowerCamelCase						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Dict												=       self.get_rust_tokenizer()
       __SCREAMING_SNAKE_CASE :			List[str]												=       tokenizer.encode(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       rust_tokenizer.encode(_lowerCamelCase						)
       self.assertListEqual(_lowerCamelCase						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			Dict												=       ErnieMTokenizer(_lowerCamelCase						,							do_lower_case=_lowerCamelCase						,							unk_token='''<unk>'''						,							pad_token='''<pad>'''						)

       __SCREAMING_SNAKE_CASE :			List[Any]												=       tokenizer.tokenize('''This is a test'''						)
       self.assertListEqual(_lowerCamelCase						,							['''▁this''', '''▁is''', '''▁a''', '''▁test''']						)

       self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase						)						,							[4_8, 2_5, 2_1, 1_2_8_9]						)

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       tokenizer.tokenize('''I was born in 92000, and this is falsé.'''						)
       # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
       self.assertListEqual(
           _lowerCamelCase						,							['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       tokenizer.convert_tokens_to_ids(_lowerCamelCase						)
       self.assertListEqual(_lowerCamelCase						,							[3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]						)

       __SCREAMING_SNAKE_CASE :			Dict												=       tokenizer.convert_ids_to_tokens(_lowerCamelCase						)
       self.assertListEqual(
           _lowerCamelCase						,							['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']						,							)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       ErnieMTokenizer(_lowerCamelCase						,							unk_token='''<unk>'''						,							pad_token='''<pad>'''						)

       __SCREAMING_SNAKE_CASE :			List[Any]												=       tokenizer.encode('''sequence builders'''						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       tokenizer.encode('''multi-sequence build'''						)

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       tokenizer.build_inputs_with_special_tokens(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       tokenizer.build_inputs_with_special_tokens(_lowerCamelCase						,							_lowerCamelCase						)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
           tokenizer.sep_token_id
       ] + text_a + [tokenizer.sep_token_id]



      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       # fmt: off
       __SCREAMING_SNAKE_CASE :			List[Any]												=       {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 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, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 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]]}
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_lowerCamelCase						,							model_name='''susnato/ernie-m-base_pytorch'''						,							sequences=[
               '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
               '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
               '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
               '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
               '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
               '''conditioning on both left and right context in all layers.''',
               '''The quick brown fox jumps over the lazy dog.''',
           ]						,							)



import unittest

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

from diffusers import KandinskyPriorPipeline, 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      lowerCamelCase		(  A_							, unittest.TestCase  ):
     UpperCAmelCase__				:							Tuple           =		KandinskyPriorPipeline
     UpperCAmelCase__				:							List[str]           =		["prompt"]
     UpperCAmelCase__				:							List[str]           =		["prompt", "negative_prompt"]
     UpperCAmelCase__				:							List[str]           =		[
         "num_images_per_prompt",
         "generator",
         "num_inference_steps",
         "latents",
         "negative_prompt",
         "guidance_scale",
         "output_type",
         "return_dict",
     ]
     UpperCAmelCase__				:							str           =		False





     @property
     def 		UpperCAmelCase(self      :    List[str]   )						->						Union[str, Any]:
         return 3_2





     @property
     def 		UpperCAmelCase(self      :    Any   )						->						Any:
         return 3_2





     @property
     def 		UpperCAmelCase(self      :    int   )						->						Dict:
         return self.time_input_dim





     @property
     def 		UpperCAmelCase(self      :    str   )						->						Tuple:
         return self.time_input_dim * 4





     @property
     def 		UpperCAmelCase(self      :    Dict   )						->						Dict:
         return 1_0_0





     @property
     def 		UpperCAmelCase(self      :    Any   )						->						int:
         snake_case      =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )
         return tokenizer





     @property
     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						Optional[Any]:
         torch.manual_seed(0   )
         snake_case      =					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 		UpperCAmelCase(self      :    List[Any]   )						->						Tuple:
         torch.manual_seed(0   )

         snake_case      =					{
             "num_attention_heads": 2,
             "attention_head_dim": 1_2,
             "embedding_dim": self.text_embedder_hidden_size,
             "num_layers": 1,
         }

         snake_case      =					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
         snake_case      =					nn.Parameter(torch.ones(model.clip_std.shape   )   )
         return model





     @property
     def 		UpperCAmelCase(self      :    str   )						->						Union[str, Any]:
         torch.manual_seed(0   )
         snake_case      =					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				,    )

         snake_case      =					CLIPVisionModelWithProjection(_A   )
         return model





     @property
     def 		UpperCAmelCase(self      :    List[Any]   )						->						int:
         snake_case      =					CLIPImageProcessor(
             crop_size=2_2_4				,    do_center_crop=_A				,    do_normalize=_A				,    do_resize=_A				,    image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]				,    image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]				,    resample=3				,    size=2_2_4				,    )

         return image_processor





     def 		UpperCAmelCase(self      :    int   )						->						Union[str, Any]:
         snake_case      =					self.dummy_prior
         snake_case      =					self.dummy_image_encoder
         snake_case      =					self.dummy_text_encoder
         snake_case      =					self.dummy_tokenizer
         snake_case      =					self.dummy_image_processor

         snake_case      =					UnCLIPScheduler(
             variance_type="fixed_small_log"				,    prediction_type="sample"				,    num_train_timesteps=1_0_0_0				,    clip_sample=_A				,    clip_sample_range=10.0				,    )

         snake_case      =					{
             "prior": prior,
             "image_encoder": image_encoder,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "scheduler": scheduler,
             "image_processor": image_processor,
         }

         return components





     def 		UpperCAmelCase(self      :    Optional[int]				,    _A      :    Tuple				,    _A      :    str=0   )						->						Union[str, Any]:
         if str(_A   ).startswith("mps"   ):
             snake_case      =					torch.manual_seed(_A   )
         else:
             snake_case      =					torch.Generator(device=_A   ).manual_seed(_A   )
         snake_case      =					{
             "prompt": "horse",
             "generator": generator,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs





     def 		UpperCAmelCase(self      :    List[Any]   )						->						int:
         snake_case      =					"cpu"

         snake_case      =					self.get_dummy_components()

         snake_case      =					self.pipeline_class(**_A   )
         snake_case      =					pipe.to(_A   )

         pipe.set_progress_bar_config(disable=_A   )

         snake_case      =					pipe(**self.get_dummy_inputs(_A   )   )
         snake_case      =					output.image_embeds

         snake_case      =					pipe(
             **self.get_dummy_inputs(_A   )				,    return_dict=_A				,    )[0]

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

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

         snake_case      =					np.array(
             [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56]   )

         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 		UpperCAmelCase(self      :    List[Any]   )						->						str:
         snake_case      =					torch_device == "cpu"
         snake_case      =					True
         snake_case      =					False

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





     @skip_mps
     def 		UpperCAmelCase(self      :    Optional[int]   )						->						List[str]:
         snake_case      =					torch_device == "cpu"
         snake_case      =					False

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

