



import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 		_lowercase		(							UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      HerbertTokenizer
   _UpperCAmelCase  						=      HerbertTokenizerFast
   _UpperCAmelCase  						=      True

   def 		A				(   self      :							Any				)							->					List[str]:


    """simple docstring"""






    super().setUp()

    # Use a simpler test file without japanese/chinese characters
    with open(f"""{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"""				, encoding="utf-8"				) as f_data:
     a							=		f_data.read().replace("\n\n"				, "\n"				).strip()

    a							=		[
        "<s>",
        "</s>",
        "l",
        "o",
        "w",
        "e",
        "r",
        "s",
        "t",
        "i",
        "d",
        "n",
        "w</w>",
        "r</w>",
        "t</w>",
        "lo",
        "low",
        "er</w>",
        "low</w>",
        "lowest</w>",
        "newer</w>",
        "wider</w>",
        ",</w>",
        "<unk>",
    ]
    a							=		dict(zip(__lowerCAmelCase				, range(len(__lowerCAmelCase				)				)				)				)
    a							=		["l o 123", "lo w 1456", "e r</w> 1789", ""]

    a							=		os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES["vocab_file"]				)
    a							=		os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES["merges_file"]				)
    with open(self.vocab_file				, "w"				) as fp:
     fp.write(json.dumps(__lowerCAmelCase				)				)
    with open(self.merges_file				, "w"				) as fp:
     fp.write("\n".join(__lowerCAmelCase				)				)

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


    """simple docstring"""






    a							=		"lower newer"
    a							=		"lower newer"
    return input_text, output_text

   def 		A				(   self      :							Tuple				)							->					int:


    """simple docstring"""






    a							=		self.tokenizer_class(vocab_file=self.vocab_file				, merges_file=self.merges_file				)

    a							=		"lower"
    a							=		["low", "er</w>"]
    a							=		tokenizer.tokenize(__lowerCAmelCase				)
    self.assertListEqual(__lowerCAmelCase				, __lowerCAmelCase				)

    a							=		tokens + ["<unk>"]
    a							=		[16, 17, 23]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase				)				, __lowerCAmelCase				)

   def 		A				(   self      :							Optional[Any]				)							->					List[str]:


    """simple docstring"""






    if not self.test_rust_tokenizer:
     return

    a							=		self.get_tokenizer()
    a							=		self.get_rust_tokenizer()

    a							=		"lower,newer"

    a							=		tokenizer.tokenize(__lowerCAmelCase				)
    a							=		rust_tokenizer.tokenize(__lowerCAmelCase				)
    self.assertListEqual(__lowerCAmelCase				, __lowerCAmelCase				)

    a							=		tokenizer.encode(__lowerCAmelCase				, add_special_tokens=__lowerCAmelCase				)
    a							=		rust_tokenizer.encode(__lowerCAmelCase				, add_special_tokens=__lowerCAmelCase				)
    self.assertListEqual(__lowerCAmelCase				, __lowerCAmelCase				)

    a							=		self.get_rust_tokenizer()
    a							=		tokenizer.encode(__lowerCAmelCase				)
    a							=		rust_tokenizer.encode(__lowerCAmelCase				)
    self.assertListEqual(__lowerCAmelCase				, __lowerCAmelCase				)




   @slow
   def 		A				(   self      :							Union[str, Any]				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		self.tokenizer_class.from_pretrained("allegro/herbert-base-cased"				)

    a							=		tokenizer.encode("konstruowanie sekwencji"				, add_special_tokens=__lowerCAmelCase				)
    a							=		tokenizer.encode("konstruowanie wielu sekwencji"				, add_special_tokens=__lowerCAmelCase				)

    a							=		tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase				)
    a							=		tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase				, __lowerCAmelCase				)

    assert encoded_sentence == [0] + text + [2]
    assert encoded_pair == [0] + text + [2] + text_a + [2]





'''simple docstring'''

import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       List[Any]            =       KandinskyVaaPriorEmbaEmbPipeline
 _lowerCamelCase			:       Tuple            =       ['prompt', 'image']
 _lowerCamelCase			:       List[str]            =       ['prompt', 'image']
 _lowerCamelCase			:       List[str]            =       [
     'num_images_per_prompt',
     'strength',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 _lowerCamelCase			:       List[str]            =       False

 @property
 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    return 32

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





    """simple docstring"""



    return 32

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    return self.time_input_dim

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    return self.time_input_dim * 4

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    return 100

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



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

 @property
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    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=1000,       )
    return CLIPTextModelWithProjection(a_       )

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    torch.manual_seed(0       )

    UpperCamelCase__			    =    {
        "num_attention_heads": 2,
        "attention_head_dim": 12,
        "embedding_dim": self.text_embedder_hidden_size,
        "num_layers": 1,
    }

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

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    CLIPVisionConfig(
        hidden_size=self.text_embedder_hidden_size,       image_size=224,       projection_dim=self.text_embedder_hidden_size,       intermediate_size=37,       num_attention_heads=4,       num_channels=3,       num_hidden_layers=5,       patch_size=14,       )

    UpperCamelCase__			    =    CLIPVisionModelWithProjection(a_       )
    return model

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    CLIPImageProcessor(
        crop_size=224,       do_center_crop=a_,       do_normalize=a_,       do_resize=a_,       image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073],       image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711],       resample=3,       size=224,       )

    return image_processor

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



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

    UpperCamelCase__			    =    UnCLIPScheduler(
        variance_type="fixed_small_log",       prediction_type="sample",       num_train_timesteps=1000,       clip_sample=a_,       clip_sample_range=10.0,       )

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

    return components

 def 		lowercase_    (			self			:						Union[str, Any],       a_			:						List[str],       a_			:						Union[str, Any]=0       ):





    """simple docstring"""



    if str(a_       ).startswith("mps"       ):
       UpperCamelCase__			    =    torch.manual_seed(a_       )
    else:
       UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(a_       )

    UpperCamelCase__			    =    floats_tensor((1, 3, 64, 64),       rng=random.Random(a_       )       ).to(a_       )
    UpperCamelCase__			    =    image.cpu().permute(0,       2,       3,       1       )[0]
    UpperCamelCase__			    =    Image.fromarray(np.uinta(a_       )       ).convert("RGB"       ).resize((256, 256)       )

    UpperCamelCase__			    =    {
        "prompt": "horse",
        "image": init_image,
        "strength": 0.5,
        "generator": generator,
        "guidance_scale": 4.0,
        "num_inference_steps": 2,
        "output_type": "np",
    }
    return inputs

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





    """simple docstring"""



    UpperCamelCase__			    =    "cpu"

    UpperCamelCase__			    =    self.get_dummy_components()

    UpperCamelCase__			    =    self.pipeline_class(**a_       )
    UpperCamelCase__			    =    pipe.to(a_       )

    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    pipe(**self.get_dummy_inputs(a_       )       )
    UpperCamelCase__			    =    output.image_embeds

    UpperCamelCase__			    =    pipe(
        **self.get_dummy_inputs(a_       ),       return_dict=a_,       )[0]

    UpperCamelCase__			    =    image[0, -10:]
    UpperCamelCase__			    =    image_from_tuple[0, -10:]

    assert image.shape == (1, 32)

    UpperCamelCase__			    =    np.array(
        [
            0.1_071_284,
            1.3_330_271,
            0.61_260_223,
            -0.6_691_065,
            -0.3_846_852,
            -1.0_303_661,
            0.22_716_111,
            0.03_348_901,
            0.30_040_675,
            -0.24_805_029,
        ]       )

    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 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch_device == "cpu"
    UpperCamelCase__			    =    True
    UpperCamelCase__			    =    False

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

 @skip_mps
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch_device == "cpu"
    UpperCamelCase__			    =    False

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