


"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:str       							=				tempfile.mkdtemp()

              # fmt: off
              lowercase		:int       							=				['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
              # fmt: on
              lowercase		:Optional[Any]       							=				dict(zip(snake_case__  ,     range(len(snake_case__				)				)				)				)
              lowercase		:Any       							=				['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
              lowercase		:Tuple       							=				{'''unk_token''': '''<unk>'''}

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

              lowercase		:Union[str, Any]       							=				{
                  '''do_resize''': True,
                  '''size''': 2_0,
                  '''do_center_crop''': True,
                  '''crop_size''': 1_8,
                  '''do_normalize''': True,
                  '''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],
              }
              lowercase		:Optional[int]       							=				os.path.join(self.tmpdirname  ,     snake_case__				)
              with open(self.image_processor_file  ,     '''w'''  ,     encoding='''utf-8'''				) as fp:
                     json.dump(snake_case__  ,     snake_case__				)






       def  __snake_case						(   self  :    Tuple  ,     **snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              return CLIPTokenizer.from_pretrained(self.tmpdirname  ,     **snake_case__				)






       def  __snake_case						(   self  :    int  ,     **snake_case__  :    Optional[Any]				):

              '''simple docstring'''






              return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,     **snake_case__				)






       def  __snake_case						(   self  :    Dict  ,     **snake_case__  :    List[Any]				):

              '''simple docstring'''






              return CLIPImageProcessor.from_pretrained(self.tmpdirname  ,     **snake_case__				)






       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              shutil.rmtree(self.tmpdirname				)






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:Dict       							=				[np.random.randint(2_5_5  ,     size=(3, 3_0, 4_0_0)  ,     dtype=np.uinta				)]

              lowercase		:List[str]       							=				[Image.fromarray(np.moveaxis(snake_case__  ,     0  ,     -1				)				) for x in image_inputs]

              return image_inputs






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				self.get_tokenizer()
              lowercase		:Dict       							=				self.get_rust_tokenizer()
              lowercase		:Any       							=				self.get_image_processor()

              lowercase		:str       							=				CLIPProcessor(tokenizer=snake_case__  ,     image_processor=snake_case__				)
              processor_slow.save_pretrained(self.tmpdirname				)
              lowercase		:Optional[int]       							=				CLIPProcessor.from_pretrained(self.tmpdirname  ,     use_fast=snake_case__				)

              lowercase		:Any       							=				CLIPProcessor(tokenizer=snake_case__  ,     image_processor=snake_case__				)
              processor_fast.save_pretrained(self.tmpdirname				)
              lowercase		:Tuple       							=				CLIPProcessor.from_pretrained(self.tmpdirname				)

              self.assertEqual(processor_slow.tokenizer.get_vocab()  ,     tokenizer_slow.get_vocab()				)
              self.assertEqual(processor_fast.tokenizer.get_vocab()  ,     tokenizer_fast.get_vocab()				)
              self.assertEqual(tokenizer_slow.get_vocab()  ,     tokenizer_fast.get_vocab()				)
              self.assertIsInstance(processor_slow.tokenizer  ,     snake_case__				)
              self.assertIsInstance(processor_fast.tokenizer  ,     snake_case__				)

              self.assertEqual(processor_slow.image_processor.to_json_string()  ,     image_processor.to_json_string()				)
              self.assertEqual(processor_fast.image_processor.to_json_string()  ,     image_processor.to_json_string()				)
              self.assertIsInstance(processor_slow.image_processor  ,     snake_case__				)
              self.assertIsInstance(processor_fast.image_processor  ,     snake_case__				)






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:Tuple       							=				CLIPProcessor(tokenizer=self.get_tokenizer()  ,     image_processor=self.get_image_processor()				)
              processor.save_pretrained(self.tmpdirname				)

              lowercase		:Dict       							=				self.get_tokenizer(bos_token='''(BOS)'''  ,     eos_token='''(EOS)'''				)
              lowercase		:List[str]       							=				self.get_image_processor(do_normalize=snake_case__  ,     padding_value=1.0				)

              lowercase		:Union[str, Any]       							=				CLIPProcessor.from_pretrained(
                  self.tmpdirname  ,     bos_token='''(BOS)'''  ,     eos_token='''(EOS)'''  ,     do_normalize=snake_case__  ,     padding_value=1.0				)

              self.assertEqual(processor.tokenizer.get_vocab()  ,     tokenizer_add_kwargs.get_vocab()				)
              self.assertIsInstance(processor.tokenizer  ,     snake_case__				)

              self.assertEqual(processor.image_processor.to_json_string()  ,     image_processor_add_kwargs.to_json_string()				)
              self.assertIsInstance(processor.image_processor  ,     snake_case__				)






       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:Optional[Any]       							=				self.get_image_processor()
              lowercase		:Any       							=				self.get_tokenizer()

              lowercase		:str       							=				CLIPProcessor(tokenizer=snake_case__  ,     image_processor=snake_case__				)

              lowercase		:Any       							=				self.prepare_image_inputs()

              lowercase		:Any       							=				image_processor(snake_case__  ,     return_tensors='''np'''				)
              lowercase		:int       							=				processor(images=snake_case__  ,     return_tensors='''np'''				)

              for key in input_image_proc.keys():
                     self.assertAlmostEqual(input_image_proc[key].sum()  ,     input_processor[key].sum()  ,     delta=1e-2				)






       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				self.get_image_processor()
              lowercase		:List[str]       							=				self.get_tokenizer()

              lowercase		:Optional[int]       							=				CLIPProcessor(tokenizer=snake_case__  ,     image_processor=snake_case__				)

              lowercase		:Optional[Any]       							=				'''lower newer'''

              lowercase		:int       							=				processor(text=snake_case__				)

              lowercase		:List[str]       							=				tokenizer(snake_case__				)

              for key in encoded_tok.keys():
                     self.assertListEqual(encoded_tok[key]  ,     encoded_processor[key]				)






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              lowercase		:str       							=				self.get_image_processor()
              lowercase		:str       							=				self.get_tokenizer()

              lowercase		:Union[str, Any]       							=				CLIPProcessor(tokenizer=snake_case__  ,     image_processor=snake_case__				)

              lowercase		:List[str]       							=				'''lower newer'''
              lowercase		:List[Any]       							=				self.prepare_image_inputs()

              lowercase		:Any       							=				processor(text=snake_case__  ,     images=snake_case__				)

              self.assertListEqual(list(inputs.keys()				)  ,     ['''input_ids''', '''attention_mask''', '''pixel_values''']				)

              # test if it raises when no input is passed
              with pytest.raises(snake_case__				):
                     processor()






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				self.get_image_processor()
              lowercase		:List[Any]       							=				self.get_tokenizer()

              lowercase		:List[Any]       							=				CLIPProcessor(tokenizer=snake_case__  ,     image_processor=snake_case__				)

              lowercase		:Union[str, Any]       							=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

              lowercase		:int       							=				processor.batch_decode(snake_case__				)
              lowercase		:Any       							=				tokenizer.batch_decode(snake_case__				)

              self.assertListEqual(snake_case__  ,     snake_case__				)







       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:List[str]       							=				self.get_image_processor()
              lowercase		:Optional[int]       							=				self.get_tokenizer()

              lowercase		:List[Any]       							=				CLIPProcessor(tokenizer=snake_case__  ,     image_processor=snake_case__				)

              lowercase		:List[str]       							=				'''lower newer'''
              lowercase		:Tuple       							=				self.prepare_image_inputs()

              lowercase		:Any       							=				processor(text=snake_case__  ,     images=snake_case__				)

              self.assertListEqual(list(inputs.keys()				)  ,     processor.model_input_names				)





"""simple docstring"""



import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      Tuple        =   PriorTransformer
       __A			:      Any        =   "hidden_states"






       @property
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				4
              lowercase		:List[Any]       							=				8
              lowercase		:Any       							=				7

              lowercase		:Optional[Any]       							=				floats_tensor((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:List[Any]       							=				floats_tensor((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Union[str, Any]       							=				floats_tensor((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Optional[Any]=0				):

              '''simple docstring'''






              torch.manual_seed(snake_case__				)
              lowercase		:Union[str, Any]       							=				4
              lowercase		:Optional[int]       							=				8
              lowercase		:str       							=				7

              lowercase		:Dict       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:Union[str, Any]       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Tuple       							=				torch.randn((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }






       @property
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              return (4, 8)






       @property
       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              return (4, 8)






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				{
                  '''num_attention_heads''': 2,
                  '''attention_head_dim''': 4,
                  '''num_layers''': 2,
                  '''embedding_dim''': 8,
                  '''num_embeddings''': 7,
                  '''additional_embeddings''': 4,
              }
              lowercase		:List[str]       							=				self.dummy_input
              return init_dict, inputs_dict






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase							,  lowercase		:Optional[int]       							=				PriorTransformer.from_pretrained(
                  '''hf-internal-testing/prior-dummy'''  ,     output_loading_info=snake_case__				)
              self.assertIsNotNone(snake_case__				)
              self.assertEqual(len(loading_info['''missing_keys''']				)  ,     0				)

              model.to(snake_case__				)
              lowercase		:List[Any]       							=				model(**self.dummy_input				)[0]

              assert hidden_states is not None, "Make sure output is not None"






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

              '''simple docstring'''






              lowercase							,  lowercase		:List[Any]       							=				self.prepare_init_args_and_inputs_for_common()

              lowercase		:str       							=				self.model_class(**snake_case__				)
              lowercase		:Optional[Any]       							=				inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              lowercase		:List[Any]       							=				[*signature.parameters.keys()]

              lowercase		:Optional[Any]       							=				['''hidden_states''', '''timestep''']
              self.assertListEqual(arg_names[:2]  ,     snake_case__				)







       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''				)
              lowercase		:List[Any]       							=				model.to(snake_case__				)

              if hasattr(snake_case__  ,     '''set_default_attn_processor'''				):
                     model.set_default_attn_processor()

              lowercase		:List[str]       							=				self.get_dummy_seed_input()

              with torch.no_grad():
                     lowercase		:List[Any]       							=				model(**snake_case__				)[0]

              lowercase		:Dict       							=				output[0, :5].flatten().cpu()
              print(snake_case__				)

              # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
              # the expected output slices are not the same for CPU and GPU.
              lowercase		:str       							=				torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]				)
              self.assertTrue(torch_all_close(snake_case__  ,     snake_case__  ,     rtol=1e-2				)				)







@slow
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    int=1  ,     snake_case__  :    int=7_6_8  ,     snake_case__  :    Optional[Any]=7_7  ,     snake_case__  :    Union[str, Any]=0				):

              '''simple docstring'''






              torch.manual_seed(snake_case__				)
              lowercase		:Any       							=				batch_size
              lowercase		:Dict       							=				embedding_dim
              lowercase		:int       							=				num_embeddings

              lowercase		:int       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:List[str]       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Optional[Any]       							=				torch.randn((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






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







       @parameterized.expand(
           [
               # fmt: off
               [1_3, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
               [3_7, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
               # fmt: on
           ]				)
       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Dict  ,     snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:str       							=				PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''  ,     subfolder='''prior'''				)
              model.to(snake_case__				)
              lowercase		:Union[str, Any]       							=				self.get_dummy_seed_input(seed=snake_case__				)

              with torch.no_grad():
                     lowercase		:int       							=				model(**snake_case__				)[0]

              assert list(sample.shape				) == [1, 7_6_8]

              lowercase		:List[Any]       							=				sample[0, :8].flatten().cpu()
              print(snake_case__				)
              lowercase		:Union[str, Any]       							=				torch.tensor(snake_case__				)

              assert torch_all_close(snake_case__  ,     snake_case__  ,     atol=1e-3				)

