


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():
  SCREAMING_SNAKE_CASE							    =							"""pt"""
elif is_tf_available():
  SCREAMING_SNAKE_CASE							    =							"""tf"""
else:
  SCREAMING_SNAKE_CASE							    =							"""jax"""


class     __UpperCAmelCase							(       __A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      PerceiverTokenizer
     _lowerCamelCase   =      False


     def 	snake_case_  (	self    ):
        super().setUp()
        __a					=  PerceiverTokenizer()
        tokenizer.save_pretrained(self.tmpdirname    )


     @cached_property
     def 	snake_case_  (	self    ):
        return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""    )


     def 	snake_case_  (	self  ,       **__A    ):
        return self.tokenizer_class.from_pretrained(self.tmpdirname  ,       **__lowerCAmelCase    )


     def 	snake_case_  (	self  ,       __A  ,       __A=False  ,       __A=20  ,       __A=5    ):
        __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 __A    : re.match(R"""^[ a-zA-Z]+$"""  ,       t[1]    )  ,       __lowerCAmelCase    )    )
        __a					=  list(filter(lambda __A    : [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 	snake_case_  (	self    ):
        __a					=  self.perceiver_tokenizer
        __a					=  """Unicode €."""
        __a					=  tokenizer(__lowerCAmelCase    )
        __a					=  [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 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, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 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 	snake_case_  (	self    ):
        __a					=  self.perceiver_tokenizer
        __a					=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
        # fmt: off
        __a					=  [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 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, 38)  ,       batch.input_ids.shape    )
        self.assertEqual((2, 38)  ,       batch.attention_mask.shape    )


     def 	snake_case_  (	self    ):
        __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 	snake_case_  (	self    ):
        __a					=  self.perceiver_tokenizer
        __a					=  [
            """Summary of the text.""",
            """Another summary.""",
        ]
        __a					=  tokenizer(
            text_target=__lowerCAmelCase  ,       max_length=32  ,       padding="""max_length"""  ,       truncation=__lowerCAmelCase  ,       return_tensors=__lowerCAmelCase    )
        self.assertEqual(32  ,       targets["""input_ids"""].shape[1]    )


     def 	snake_case_  (	self    ):
        __a					=  self.get_tokenizers()
        for tokenizer in tokenizers:
           with self.subTest(f'''{tokenizer.__class__.__name__}'''    ):
              self.assertNotEqual(tokenizer.model_max_length  ,       42    )

        # 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=42    )
        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  ,       42    )

              __a					=  tokenizer.__class__.from_pretrained(__lowerCAmelCase  ,       model_max_length=43    )
              self.assertEqual(tokenizer.model_max_length  ,       43    )

              shutil.rmtree(__lowerCAmelCase    )


     def 	snake_case_  (	self    ):
        __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(125    )]

              __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 	snake_case_  (	self    ):
        __a					=  self.perceiver_tokenizer
        self.assertEqual(tokenizer.decode([178]    )  ,       """�"""    )


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        __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    )




"""simple docstring"""






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


__lowerCAmelCase       :			Tuple   	=8


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:int=BITS						)					->       Optional[Any]:
      '''simple docstring'''


      lowercase								=      x.device

      lowercase								=      (x * 2_5_5).int().clamp(0   ,						2_5_5						)

      lowercase								=      2 ** torch.arange(bits - 1   ,						-1   ,						-1   ,						device=lowerCAmelCase__						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""d -> d 1 1"""						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""b c h w -> b c 1 h w"""						)

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


def      UpperCAmelCase__			(							lowerCAmelCase__	:Dict   ,						lowerCAmelCase__	:int=BITS						)					->       List[Any]:
      '''simple docstring'''


      lowercase								=      x.device

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

      lowercase								=      rearrange(lowerCAmelCase__   ,						"""d -> d 1 1"""						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""b (c d) h w -> b c d h w"""   ,						d=8						)
      lowercase								=      reduce(x * mask   ,						"""b c d h w -> b c h w"""   ,						"""sum"""						)
      return (dec / 2_5_5).clamp(0.0   ,						1.0						)


def      UpperCAmelCase__			(							self	:Union[str, Any]   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:float = 0.0   ,						lowerCAmelCase__	:bool = True   ,						lowerCAmelCase__	:Optional[Any]=None   ,						lowerCAmelCase__	: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)
      lowercase								=      timestep - self.config.num_train_timesteps // self.num_inference_steps

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

      lowercase								=      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
      lowercase								=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

      # 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)
      lowercase								=      self._get_variance(lowerCAmelCase__   ,						lowerCAmelCase__						)
      lowercase								=      eta * variance ** 0.5

      if use_clipped_model_output:
            # the model_output is always re-derived from the clipped x_0 in Glide
            lowercase								=      (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
      lowercase								=      (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
      lowercase								=      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
            lowercase								=      model_output.device if torch.is_tensor(lowerCAmelCase__						) else """cpu"""
            lowercase								=      torch.randn(model_output.shape   ,						dtype=model_output.dtype   ,						generator=lowerCAmelCase__						).to(lowerCAmelCase__						)
            lowercase								=      self._get_variance(lowerCAmelCase__   ,						lowerCAmelCase__						) ** 0.5 * eta * noise

            lowercase								=      prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=lowerCAmelCase__   ,						pred_original_sample=lowerCAmelCase__						)


def      UpperCAmelCase__			(							self	:Any   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:List[str]="epsilon"   ,						lowerCAmelCase__	:Optional[int]=None   ,						lowerCAmelCase__	:bool = True   ,						)					->       Union[DDPMSchedulerOutput, Tuple]:
      '''simple docstring'''


      lowercase								=      timestep

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

      # 1. compute alphas, betas
      lowercase								=      self.alphas_cumprod[t]
      lowercase								=      self.alphas_cumprod[t - 1] if t > 0 else self.one
      lowercase								=      1 - alpha_prod_t
      lowercase								=      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":
            lowercase								=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif prediction_type == "sample":
            lowercase								=      model_output
      else:
            raise ValueError(f'Unsupported prediction_type {prediction_type}.'						)

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

      # 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
      lowercase								=      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
      lowercase								=      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
      lowercase								=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

      lowercase								=      pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=lowerCAmelCase__   ,						pred_original_sample=lowerCAmelCase__						)






class 					_A		(			lowerCAmelCase      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = 1.0						,			):







            """simple docstring"""



            super().__init__()
            lowercase								=      bit_scale
            lowercase								=      (
                ddim_bit_scheduler_step if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else ddpm_bit_scheduler_step
            )

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







      @torch.no_grad()
      def __call__(		self						,			__lowerCAmelCase = 256						,			__lowerCAmelCase = 256						,			__lowerCAmelCase = 50						,			__lowerCAmelCase = None						,			__lowerCAmelCase = 1						,			__lowerCAmelCase = "pil"						,			__lowerCAmelCase = True						,			**__lowerCAmelCase						,			):







            """simple docstring"""



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

            self.scheduler.set_timesteps(__lowerCAmelCase		)

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

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

            lowercase								=      bits_to_decimal(__lowerCAmelCase		)

            if output_type == "pil":
                  lowercase								=      self.numpy_to_pil(__lowerCAmelCase		)

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=__lowerCAmelCase		)



