

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				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Dict  					=    KandinskyVaaPriorPipeline
   _lowercase     :       Dict  					=    ['prompt']
   _lowercase     :       Optional[int]  					=    ['prompt', 'negative_prompt']
   _lowercase     :       Union[str, Any]  					=    [
       'num_images_per_prompt',
       'generator',
       'num_inference_steps',
       'latents',
       'negative_prompt',
       'guidance_scale',
       'output_type',
       'return_dict',
   ]
   _lowercase     :       str  					=    False

   @property
   def     UpperCamelCase					(				self	:    Dict       ) ->			Union[str, Any]:
         """simple docstring"""





         return 3_2

   @property
   def     UpperCamelCase					(				self	:    int       ) ->			Optional[int]:
         """simple docstring"""





         return 3_2

   @property
   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Optional[Any]:
         """simple docstring"""





         return self.time_input_dim

   @property
   def     UpperCamelCase					(				self	:    str       ) ->			List[Any]:
         """simple docstring"""





         return self.time_input_dim * 4

   @property
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			List[str]:
         """simple docstring"""





         return 1_0_0

   @property
   def     UpperCamelCase					(				self	:    List[str]       ) ->			List[str]:
         """simple docstring"""





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

   @property
   def     UpperCamelCase					(				self	:    int       ) ->			List[str]:
         """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(lowerCamelCase__       )

   @property
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			int:
         """simple docstring"""





         torch.manual_seed(0       )

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

         A_							       =		PriorTransformer(**lowerCamelCase__       )
         # 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
         A_							       =		nn.Parameter(torch.ones(model.clip_std.shape       )       )
         return model

   @property
   def     UpperCamelCase					(				self	:    str       ) ->			int:
         """simple docstring"""





         torch.manual_seed(0       )
         A_							       =		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			,						)

         A_							       =		CLIPVisionModelWithProjection(lowerCamelCase__       )
         return model

   @property
   def     UpperCamelCase					(				self	:    List[str]       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		CLIPImageProcessor(
             crop_size=2_2_4			,						do_center_crop=lowerCamelCase__			,						do_normalize=lowerCamelCase__			,						do_resize=lowerCamelCase__			,						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     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Any:
         """simple docstring"""





         A_							       =		self.dummy_prior
         A_							       =		self.dummy_image_encoder
         A_							       =		self.dummy_text_encoder
         A_							       =		self.dummy_tokenizer
         A_							       =		self.dummy_image_processor

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

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

         return components

   def     UpperCamelCase					(				self	:    Any			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    Dict=0       ) ->			Dict:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Dict       ) ->			int:
         """simple docstring"""





         A_							       =		'''cpu'''

         A_							       =		self.get_dummy_components()

         A_							       =		self.pipeline_class(**lowerCamelCase__       )
         A_							       =		pipe.to(lowerCamelCase__       )

         pipe.set_progress_bar_config(disable=lowerCamelCase__       )

         A_							       =		pipe(**self.get_dummy_inputs(lowerCamelCase__       )       )
         A_							       =		output.image_embeds

         A_							       =		pipe(
             **self.get_dummy_inputs(lowerCamelCase__       )			,						return_dict=lowerCamelCase__			,						)[0]

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

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

         A_							       =		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     UpperCamelCase					(				self	:    List[str]       ) ->			str:
         """simple docstring"""





         A_							       =		torch_device == '''cpu'''
         A_							       =		True
         A_							       =		False

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





   @skip_mps
   def     UpperCamelCase					(				self	:    Tuple       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		torch_device == '''cpu'''
         A_							       =		False

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








import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


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

class    _lowercase				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Dict  					=    PerceiverTokenizer
   _lowercase     :       Dict  					=    False

   def     UpperCamelCase					(				self	:    Tuple       ) ->			Any:
         """simple docstring"""





         super().setUp()
         A_							       =		PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname       )

   @cached_property
   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[str]:
         """simple docstring"""





         return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''       )

   def     UpperCamelCase					(				self	:    List[str]			,						**lowerCamelCase__	:    str       ) ->			PerceiverTokenizer:
         """simple docstring"""





         return self.tokenizer_class.from_pretrained(self.tmpdirname			,						**lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Optional[Any]=False			,						lowerCamelCase__	:    List[str]=2_0			,						lowerCamelCase__	:    Union[str, Any]=5       ) ->			Tuple[str, list]:
         """simple docstring"""





         A_							       =		[]
         for i in range(len(lowerCamelCase__       )       ):
               try:
                     A_							       =		tokenizer.decode([i]			,						clean_up_tokenization_spaces=lowerCamelCase__       )
               except UnicodeDecodeError:
                     pass
               toks.append((i, tok)       )

         A_							       =		list(filter(lambda lowerCamelCase__       : re.match(R'''^[ a-zA-Z]+$'''			,						t[1]       )			,						lowerCamelCase__       )       )
         A_							       =		list(filter(lambda lowerCamelCase__       : [t[0]] == tokenizer.encode(t[1]			,						add_special_tokens=lowerCamelCase__       )			,						lowerCamelCase__       )       )
         if max_length is not None and len(lowerCamelCase__       ) > max_length:
               A_							       =		toks[:max_length]
         if min_length is not None and len(lowerCamelCase__       ) < min_length and len(lowerCamelCase__       ) > 0:
               while len(lowerCamelCase__       ) < min_length:
                     A_							       =		toks + toks
        # toks_str = [t[1] for t in toks]
         A_							       =		[t[0] for t in toks]

         # Ensure consistency
         A_							       =		tokenizer.decode(lowerCamelCase__			,						clean_up_tokenization_spaces=lowerCamelCase__       )
         if " " not in output_txt and len(lowerCamelCase__       ) > 1:
               A_							       =		(
                   tokenizer.decode([toks_ids[0]]			,						clean_up_tokenization_spaces=lowerCamelCase__       )
                   + ''' '''
                   + tokenizer.decode(toks_ids[1:]			,						clean_up_tokenization_spaces=lowerCamelCase__       )
               )
         if with_prefix_space:
               A_							       =		''' ''' + output_txt
         A_							       =		tokenizer.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__       )
         return output_txt, output_ids

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		'''Unicode €.'''
         A_							       =		tokenizer(lowerCamelCase__       )
         A_							       =		[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
         self.assertEqual(encoded['''input_ids''']			,						lowerCamelCase__       )

         # decoding
         A_							       =		tokenizer.decode(lowerCamelCase__       )
         self.assertEqual(lowerCamelCase__			,						'''[CLS]Unicode €.[SEP]'''       )

         A_							       =		tokenizer('''e è é ê ë'''       )
         A_							       =		[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
         self.assertEqual(encoded['''input_ids''']			,						lowerCamelCase__       )
         # decoding
         A_							       =		tokenizer.decode(lowerCamelCase__       )
         self.assertEqual(lowerCamelCase__			,						'''[CLS]e è é ê ë[SEP]'''       )

         # encode/decode, but with `encode` instead of `__call__`
         self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''       )       )			,						'''[CLS]e è é ê ë[SEP]'''       )

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         # fmt: off
         A_							       =		[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
         # fmt: on
         A_							       =		tokenizer(lowerCamelCase__			,						padding=lowerCamelCase__			,						return_tensors=lowerCamelCase__       )
         self.assertIsInstance(lowerCamelCase__			,						lowerCamelCase__       )

         if FRAMEWORK != "jax":
               A_							       =		list(batch.input_ids.numpy()[0]       )
         else:
               A_							       =		list(batch.input_ids.tolist()[0]       )

         self.assertListEqual(lowerCamelCase__			,						lowerCamelCase__       )

         self.assertEqual((2, 3_8)			,						batch.input_ids.shape       )
         self.assertEqual((2, 3_8)			,						batch.attention_mask.shape       )

   def     UpperCamelCase					(				self	:    str       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         A_							       =		tokenizer(lowerCamelCase__			,						padding=lowerCamelCase__			,						return_tensors=lowerCamelCase__       )
         # check if input_ids are returned and no decoder_input_ids
         self.assertIn('''input_ids'''			,						lowerCamelCase__       )
         self.assertIn('''attention_mask'''			,						lowerCamelCase__       )
         self.assertNotIn('''decoder_input_ids'''			,						lowerCamelCase__       )
         self.assertNotIn('''decoder_attention_mask'''			,						lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Any       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		[
             '''Summary of the text.''',
             '''Another summary.''',
         ]
         A_							       =		tokenizer(
             text_target=lowerCamelCase__			,						max_length=3_2			,						padding='''max_length'''			,						truncation=lowerCamelCase__			,						return_tensors=lowerCamelCase__       )
         self.assertEqual(3_2			,						targets['''input_ids'''].shape[1]       )

   def     UpperCamelCase					(				self	:    Dict       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.get_tokenizers()
         for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"       ):
                     self.assertNotEqual(tokenizer.model_max_length			,						4_2       )

        # Now let's start the test
         A_							       =		self.get_tokenizers()
         for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"       ):
                     # Isolate this from the other tests because we save additional tokens/etc
                     A_							       =		tempfile.mkdtemp()

                     A_							       =		''' He is very happy, UNwant\u00E9d,running'''
                     A_							       =		tokenizer.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__       )
                     tokenizer.save_pretrained(lowerCamelCase__       )

                     A_							       =		tokenizer.__class__.from_pretrained(lowerCamelCase__       )
                     A_							       =		after_tokenizer.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__       )
                     self.assertListEqual(lowerCamelCase__			,						lowerCamelCase__       )

                     shutil.rmtree(lowerCamelCase__       )

         A_							       =		self.get_tokenizers(model_max_length=4_2       )
         for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"       ):
                     # Isolate this from the other tests because we save additional tokens/etc
                     A_							       =		tempfile.mkdtemp()

                     A_							       =		''' He is very happy, UNwant\u00E9d,running'''
                     tokenizer.add_tokens(['''bim''', '''bambam''']       )
                     A_							       =		tokenizer.additional_special_tokens
                     additional_special_tokens.append('''new_additional_special_token'''       )
                     tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}       )
                     A_							       =		tokenizer.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__       )
                     tokenizer.save_pretrained(lowerCamelCase__       )

                     A_							       =		tokenizer.__class__.from_pretrained(lowerCamelCase__       )
                     A_							       =		after_tokenizer.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__       )
                     self.assertListEqual(lowerCamelCase__			,						lowerCamelCase__       )
                     self.assertIn('''new_additional_special_token'''			,						after_tokenizer.additional_special_tokens       )
                     self.assertEqual(after_tokenizer.model_max_length			,						4_2       )

                     A_							       =		tokenizer.__class__.from_pretrained(lowerCamelCase__			,						model_max_length=4_3       )
                     self.assertEqual(tokenizer.model_max_length			,						4_3       )

                     shutil.rmtree(lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Any:
         """simple docstring"""





         A_							       =		[]
         if self.test_slow_tokenizer:
               tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

         if self.test_rust_tokenizer:
               tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

         for tokenizer_class, tokenizer_utils in tokenizer_list:
               with tempfile.TemporaryDirectory() as tmp_dir:
                     tokenizer_utils.save_pretrained(lowerCamelCase__       )

                     with open(os.path.join(lowerCamelCase__			,						'''special_tokens_map.json'''       )			,						encoding='''utf-8'''       ) as json_file:
                           A_							       =		json.load(lowerCamelCase__       )

                     with open(os.path.join(lowerCamelCase__			,						'''tokenizer_config.json'''       )			,						encoding='''utf-8'''       ) as json_file:
                           A_							       =		json.load(lowerCamelCase__       )

                     A_							       =		[F"<extra_id_{i}>" for i in range(1_2_5       )]

                     A_							       =		added_tokens_extra_ids + [
                         '''an_additional_special_token'''
                     ]
                     A_							       =		added_tokens_extra_ids + [
                         '''an_additional_special_token'''
                     ]

                     with open(os.path.join(lowerCamelCase__			,						'''special_tokens_map.json'''       )			,						'''w'''			,						encoding='''utf-8'''       ) as outfile:
                           json.dump(lowerCamelCase__			,						lowerCamelCase__       )
                     with open(os.path.join(lowerCamelCase__			,						'''tokenizer_config.json'''       )			,						'''w'''			,						encoding='''utf-8'''       ) as outfile:
                           json.dump(lowerCamelCase__			,						lowerCamelCase__       )

                     # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                     # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                     # "special_tokens_map.json" files
                     A_							       =		tokenizer_class.from_pretrained(
                         lowerCamelCase__			,						)
                     self.assertIn(
                         '''an_additional_special_token'''			,						tokenizer_without_change_in_init.additional_special_tokens       )
                     self.assertEqual(
                         ['''an_additional_special_token''']			,						tokenizer_without_change_in_init.convert_ids_to_tokens(
                             tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']       )       )			,						)

                     # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                     A_							       =		added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''			,						lstrip=lowerCamelCase__       )]
                     A_							       =		tokenizer_class.from_pretrained(
                         lowerCamelCase__			,						additional_special_tokens=lowerCamelCase__			,						)

                     self.assertIn('''a_new_additional_special_token'''			,						tokenizer.additional_special_tokens       )
                     self.assertEqual(
                         ['''a_new_additional_special_token''']			,						tokenizer.convert_ids_to_tokens(
                             tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']       )       )			,						)

   def     UpperCamelCase					(				self	:    Dict       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([1_7_8]       )			,						'''�'''       )

   def     UpperCamelCase					(				self	:    int       ) ->			Any:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    int       ) ->			int:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Dict:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    int       ) ->			Any:
         """simple docstring"""





         pass





   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.get_tokenizers(fast=lowerCamelCase__			,						do_lower_case=lowerCamelCase__       )
         for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"       ):
                     A_							       =		['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
                     A_							       =		tokenizer.convert_tokens_to_string(lowerCamelCase__       )

                     self.assertIsInstance(lowerCamelCase__			,						lowerCamelCase__       )





