




import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class __snake_case			(			a ,  unittest.TestCase		):
   UpperCAmelCase__  :    int          =  GPTSanJapaneseTokenizer
   UpperCAmelCase__  :    Optional[int]          =  False
   UpperCAmelCase__  :    List[Any]          =  {'''do_clean_text''': False, '''add_prefix_space''': False}







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        super().setUp()

        # fmt: off
        UpperCAmelCase_          =							['''こん''', '''こんに''', '''にちは''', '''ばんは''', '''世界,㔺界''', '''、''', '''。''', '''<BR>''', '''<SP>''', '''<TAB>''', '''<URL>''', '''<EMAIL>''', '''<TEL>''', '''<DATE>''', '''<PRICE>''', '''<BLOCK>''', '''<KIGOU>''', '''<U2000U2BFF>''', '''<|emoji1|>''', '''<unk>''', '''<|bagoftoken|>''', '''<|endoftext|>''']
        # fmt: on
        UpperCAmelCase_          =							{'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
        UpperCAmelCase_          =							{'''unk_token''': '''<unk>'''}

        UpperCAmelCase_          =							os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['''vocab_file'''])
        UpperCAmelCase_          =							os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['''emoji_file'''])
        with open(self.vocab_file					,						'''w'''					,						encoding='''utf-8''') as vocab_writer:
             vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))
        with open(self.emoji_file					,						'''w''') as emoji_writer:
             emoji_writer.write(json.dumps(_snake_case))







   def lowerCamelCase						(      self		:						int					,						**_snake_case		:						Any):

        """simple docstring"""


        kwargs.update(self.special_tokens_map)
        return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname					,						**_snake_case)







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							'''こんにちは、世界。 \nこんばんは、㔺界。😀'''
        UpperCAmelCase_          =							'''こんにちは、世界。 \nこんばんは、世界。😀'''
        return input_text, output_text







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.get_input_output_texts(_snake_case)
        UpperCAmelCase_          =							tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case)
        UpperCAmelCase_          =							tokenizer.decode(_snake_case					,						clean_up_tokenization_spaces=_snake_case)
        return text, ids







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        pass  # TODO add if relevant







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

        """simple docstring"""


        pass  # TODO add if relevant







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        pass  # TODO add if relevant







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_tokenizer()

        # Testing tokenization
        UpperCAmelCase_          =							'''こんにちは、世界。　こんばんは、㔺界。'''
        UpperCAmelCase_          =							['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
        UpperCAmelCase_          =							tokenizer.tokenize(_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)

        # Testing conversion to ids without special tokens
        UpperCAmelCase_          =							[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
        UpperCAmelCase_          =							tokenizer.convert_tokens_to_ids(_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)

        # Testing conversion to ids with special tokens
        UpperCAmelCase_          =							tokens + [tokenizer.unk_token]
        UpperCAmelCase_          =							[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
        UpperCAmelCase_          =							tokenizer.convert_tokens_to_ids(_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_tokenizer()

        # Testing tokenization
        UpperCAmelCase_          =							'''こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'''
        UpperCAmelCase_          =							'''こんにちは、、、、世界。こんばんは、、、、世界。'''
        UpperCAmelCase_          =							tokenizer.encode(_snake_case)
        UpperCAmelCase_          =							tokenizer.decode(_snake_case)
        self.assertEqual(_snake_case					,						_snake_case)







   @slow
   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''')

        # Testing tokenization
        UpperCAmelCase_          =							'''こんにちは、世界。'''
        UpperCAmelCase_          =							'''こんばんは、㔺界。😀'''
        UpperCAmelCase_          =							'''こんにちは、世界。こんばんは、世界。😀'''
        UpperCAmelCase_          =							tokenizer.encode(prefix_text + input_text)
        UpperCAmelCase_          =							tokenizer.encode(''''''					,						prefix_text=prefix_text + input_text)
        UpperCAmelCase_          =							tokenizer.encode(_snake_case					,						prefix_text=_snake_case)
        UpperCAmelCase_          =							tokenizer.decode(_snake_case)
        UpperCAmelCase_          =							tokenizer.decode(_snake_case)
        UpperCAmelCase_          =							tokenizer.decode(_snake_case)
        self.assertEqual(_snake_case					,						_snake_case)
        self.assertEqual(_snake_case					,						_snake_case)
        self.assertEqual(_snake_case					,						_snake_case)







   @slow
   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''')

        # Testing tokenization
        UpperCAmelCase_          =							'''こんにちは、世界。'''
        UpperCAmelCase_          =							'''こんばんは、㔺界。😀'''

        UpperCAmelCase_          =							len(tokenizer.encode(_snake_case)) - 2
        UpperCAmelCase_          =							len(tokenizer.encode(_snake_case)) - 2

        UpperCAmelCase_          =							[1] + [0] * (len_prefix + len_text + 1)
        UpperCAmelCase_          =							[1] * (len_prefix + len_text + 1) + [0]
        UpperCAmelCase_          =							[1] + [1] * (len_prefix) + [0] * (len_text + 1)

        UpperCAmelCase_          =							tokenizer(prefix_text + input_text).token_type_ids
        UpperCAmelCase_          =							tokenizer(''''''					,						prefix_text=prefix_text + input_text).token_type_ids
        UpperCAmelCase_          =							tokenizer(_snake_case					,						prefix_text=_snake_case).token_type_ids
        self.assertListEqual(_snake_case					,						_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)







   @slow
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''')

        UpperCAmelCase_          =							tokenizer.encode('''あンいワ''')
        UpperCAmelCase_          =							tokenizer.encode(''''''					,						prefix_text='''あンいワ''')
        UpperCAmelCase_          =							tokenizer.encode('''いワ'''					,						prefix_text='''あン''')

        self.assertEqual(tokenizer.decode(_snake_case)					,						tokenizer.decode(_snake_case))
        self.assertEqual(tokenizer.decode(_snake_case)					,						tokenizer.decode(_snake_case))
        self.assertNotEqual(_snake_case					,						_snake_case)
        self.assertNotEqual(_snake_case					,						_snake_case)
        self.assertEqual(x_token_a[1]					,						x_token_a[-1])  # SEG token
        self.assertEqual(x_token_a[1]					,						x_token_a[3])  # SEG token







   @slow
   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							self.tokenizer_class.from_pretrained('''Tanrei/GPTSAN-japanese''')

        UpperCAmelCase_          =							[['''武田信玄''', '''は、'''], ['''織田信長''', '''の配下の、''']]
        UpperCAmelCase_          =							tokenizer(_snake_case					,						padding=_snake_case)
        UpperCAmelCase_          =							tokenizer.batch_encode_plus(_snake_case					,						padding=_snake_case)

        # fmt: off
        UpperCAmelCase_          =							[[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
        UpperCAmelCase_          =							[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
        UpperCAmelCase_          =							[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
        # fmt: on
        self.assertListEqual(x_token.input_ids					,						_snake_case)
        self.assertListEqual(x_token.token_type_ids					,						_snake_case)
        self.assertListEqual(x_token.attention_mask					,						_snake_case)
        self.assertListEqual(x_token_a.input_ids					,						_snake_case)
        self.assertListEqual(x_token_a.token_type_ids					,						_snake_case)
        self.assertListEqual(x_token_a.attention_mask					,						_snake_case)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        pass







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        pass












from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


snake_case_ : List[str]    		=       8


def    A						(__A   :    Union[str, Any]   ,				__A   :    List[Any]=BITS       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							x.device

     UpperCAmelCase_          =							(x * 255).int().clamp(0   ,				255       )

     UpperCAmelCase_          =							2 ** torch.arange(bits - 1   ,				-1   ,				-1   ,				device=__A       )
     UpperCAmelCase_          =							rearrange(__A   ,				'''d -> d 1 1'''       )
     UpperCAmelCase_          =							rearrange(__A   ,				'''b c h w -> b c 1 h w'''       )

     UpperCAmelCase_          =							((x & mask) != 0).float()
     UpperCAmelCase_          =							rearrange(__A   ,				'''b c d h w -> b (c d) h w'''       )
     UpperCAmelCase_          =							bits * 2 - 1
     return bits


def    A						(__A   :    Dict   ,				__A   :    Tuple=BITS       )   ->      List[str]:







     """simple docstring"""


     UpperCAmelCase_          =							x.device

     UpperCAmelCase_          =							(x > 0).int()
     UpperCAmelCase_          =							2 ** torch.arange(bits - 1   ,				-1   ,				-1   ,				device=__A   ,				dtype=torch.intaa       )

     UpperCAmelCase_          =							rearrange(__A   ,				'''d -> d 1 1'''       )
     UpperCAmelCase_          =							rearrange(__A   ,				'''b (c d) h w -> b c d h w'''   ,				d=8       )
     UpperCAmelCase_          =							reduce(x * mask   ,				'''b c d h w -> b c h w'''   ,				'''sum'''       )
     return (dec / 255).clamp(0.0   ,				1.0       )


def    A						(self   :    List[Any]   ,				__A   :    torch.FloatTensor   ,				__A   :    int   ,				__A   :    torch.FloatTensor   ,				__A   :    float = 0.0   ,				__A   :    bool = True   ,				__A   :    Tuple=None   ,				__A   :    bool = True   ,				)   ->      Union[DDIMSchedulerOutput, Tuple]:







     """simple docstring"""


     if self.num_inference_steps is None:
          raise ValueError(
              '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''       )

     # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
     # Ideally, read DDIM paper in-detail understanding

     # Notation (<variable name> -> <name in paper>
     # - pred_noise_t -> e_theta(x_t, t)
     # - pred_original_sample -> f_theta(x_t, t) or x_0
     # - std_dev_t -> sigma_t
     # - eta -> η
     # - pred_sample_direction -> "direction pointing to x_t"
     # - pred_prev_sample -> "x_t-1"

     # 1. get previous step value (=t-1)
     UpperCAmelCase_          =							timestep - self.config.num_train_timesteps // self.num_inference_steps

     # 2. compute alphas, betas
     UpperCAmelCase_          =							self.alphas_cumprod[timestep]
     UpperCAmelCase_          =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

     UpperCAmelCase_          =							1 - alpha_prod_t

     # 3. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     UpperCAmelCase_          =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

     # 4. Clip "predicted x_0"
     UpperCAmelCase_          =							self.bit_scale
     if self.config.clip_sample:
          UpperCAmelCase_          =							torch.clamp(__A   ,				-scale   ,				__A       )

     # 5. compute variance: "sigma_t(η)" -> see formula (16)
     # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
     UpperCAmelCase_          =							self._get_variance(__A   ,				__A       )
     UpperCAmelCase_          =							eta * variance ** 0.5

     if use_clipped_model_output:
          # the model_output is always re-derived from the clipped x_0 in Glide
          UpperCAmelCase_          =							(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

     # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     UpperCAmelCase_          =							(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

     # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     UpperCAmelCase_          =							alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

     if eta > 0:
          # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
          UpperCAmelCase_          =							model_output.device if torch.is_tensor(__A       ) else '''cpu'''
          UpperCAmelCase_          =							torch.randn(model_output.shape   ,				dtype=model_output.dtype   ,				generator=__A       ).to(__A       )
          UpperCAmelCase_          =							self._get_variance(__A   ,				__A       ) ** 0.5 * eta * noise

          UpperCAmelCase_          =							prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=__A   ,				pred_original_sample=__A       )


def    A						(self   :    Optional[int]   ,				__A   :    torch.FloatTensor   ,				__A   :    int   ,				__A   :    torch.FloatTensor   ,				__A   :    int="epsilon"   ,				__A   :    Optional[Any]=None   ,				__A   :    bool = True   ,				)   ->      Union[DDPMSchedulerOutput, Tuple]:







     """simple docstring"""


     UpperCAmelCase_          =							timestep

     if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
          UpperCAmelCase_     ,       UpperCAmelCase_          =							torch.split(__A   ,				sample.shape[1]   ,				dim=1       )
     else:
          UpperCAmelCase_          =							None

     # 1. compute alphas, betas
     UpperCAmelCase_          =							self.alphas_cumprod[t]
     UpperCAmelCase_          =							self.alphas_cumprod[t - 1] if t > 0 else self.one
     UpperCAmelCase_          =							1 - alpha_prod_t
     UpperCAmelCase_          =							1 - alpha_prod_t_prev

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     if prediction_type == "epsilon":
          UpperCAmelCase_          =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif prediction_type == "sample":
          UpperCAmelCase_          =							model_output
     else:
          raise ValueError(F"""Unsupported prediction_type {prediction_type}."""       )

     # 3. Clip "predicted x_0"
     UpperCAmelCase_          =							self.bit_scale
     if self.config.clip_sample:
          UpperCAmelCase_          =							torch.clamp(__A   ,				-scale   ,				__A       )

     # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     UpperCAmelCase_          =							(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
     UpperCAmelCase_          =							self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

     # 5. Compute predicted previous sample µ_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     UpperCAmelCase_          =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     UpperCAmelCase_          =							0
     if t > 0:
          UpperCAmelCase_          =							torch.randn(
              model_output.size()   ,				dtype=model_output.dtype   ,				layout=model_output.layout   ,				generator=__A       ).to(model_output.device       )
          UpperCAmelCase_          =							(self._get_variance(__A   ,				predicted_variance=__A       ) ** 0.5) * noise

     UpperCAmelCase_          =							pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=__A   ,				pred_original_sample=__A       )







class __snake_case			(			a		):







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						UNetaDConditionModel					,						_snake_case		:						Union[DDIMScheduler, DDPMScheduler]					,						_snake_case		:						Optional[float] = 1.0					,						):

        """simple docstring"""


        super().__init__()
        UpperCAmelCase_          =							bit_scale
        UpperCAmelCase_          =							(
            ddim_bit_scheduler_step if isinstance(_snake_case					,						_snake_case) else ddpm_bit_scheduler_step
        )

        self.register_modules(unet=_snake_case					,						scheduler=_snake_case)







   @torch.no_grad()
   def __call__(      self		:						Union[str, Any]					,						_snake_case		:						Optional[int] = 256					,						_snake_case		:						Optional[int] = 256					,						_snake_case		:						Optional[int] = 50					,						_snake_case		:						Optional[torch.Generator] = None					,						_snake_case		:						Optional[int] = 1					,						_snake_case		:						Optional[str] = "pil"					,						_snake_case		:						bool = True					,						**_snake_case		:						Optional[Any]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							torch.randn(
            (batch_size, self.unet.config.in_channels, height, width)					,						generator=_snake_case					,						)
        UpperCAmelCase_          =							decimal_to_bits(_snake_case) * self.bit_scale
        UpperCAmelCase_          =							latents.to(self.device)

        self.scheduler.set_timesteps(_snake_case)

        for t in self.progress_bar(self.scheduler.timesteps):
             # predict the noise residual
             UpperCAmelCase_          =							self.unet(_snake_case					,						_snake_case).sample

             # compute the previous noisy sample x_t -> x_t-1
             UpperCAmelCase_          =							self.scheduler.step(_snake_case					,						_snake_case					,						_snake_case).prev_sample

        UpperCAmelCase_          =							bits_to_decimal(_snake_case)

        if output_type == "pil":
             UpperCAmelCase_          =							self.numpy_to_pil(_snake_case)

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=_snake_case)






