






'''simple docstring'''

from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


UpperCAmelCase__						:List[Any]          =			logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase__						:List[Any]          =			"""
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")
        >>> pipe.to(\"cuda\")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save(\"cat.png\")
        ```
"""
def     __lowercase  (_lowercase,						_lowercase,						_lowercase=8     )   ->     List[Any]:


     """simple docstring"""
     __lowerCamelCase						:       int				   =				h // scale_factor**2
     if h % scale_factor**2 != 0:
          new_h += 1
     __lowerCamelCase						:       Dict				   =				w // scale_factor**2
     if w % scale_factor**2 != 0:
          new_w += 1
     return new_h * scale_factor, new_w * scale_factor







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def __init__(					self      : Any	,    A__      : MultilingualCLIP	,    A__      : XLMRobertaTokenizer	,    A__      : UNetaDConditionModel	,    A__      : Union[DDIMScheduler, DDPMScheduler]	,    A__      : VQModel	,    ):


            """simple docstring"""







            super().__init__()

            self.register_modules(
                text_encoder=A__	,    tokenizer=A__	,    unet=A__	,    scheduler=A__	,    movq=A__	,    )
            __lowerCamelCase						:       Dict				   =				2 ** (len(self.movq.config.block_out_channels					) - 1)




       def 					a_					(					self      : Optional[int]	,    A__      : Tuple	,    A__      : Union[str, Any]	,    A__      : Optional[int]	,    A__      : Any	,    A__      : List[Any]	,    A__      : List[str]					):


            """simple docstring"""







            if latents is None:
                 __lowerCamelCase						:       Optional[Any]				   =				randn_tensor(A__	,    generator=A__	,    device=A__	,    dtype=A__					)
            else:
                 if latents.shape != shape:
                      raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"					)
                 __lowerCamelCase						:       str				   =				latents.to(A__					)

            __lowerCamelCase						:       int				   =				latents * scheduler.init_noise_sigma
            return latents




       def 					a_					(					self      : List[str]	,    A__      : Any	,    A__      : List[str]	,    A__      : str	,    A__      : Any	,    A__      : Tuple=None	,    ):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				len(A__					) if isinstance(A__	,    A__					) else 1
            # get prompt text embeddings
            __lowerCamelCase						:       str				   =				self.tokenizer(
                A__	,    padding="""max_length"""	,    truncation=A__	,    max_length=77	,    return_attention_mask=A__	,    add_special_tokens=A__	,    return_tensors="""pt"""	,    )

            __lowerCamelCase						:       List[str]				   =				text_inputs.input_ids
            __lowerCamelCase						:       Any				   =				self.tokenizer(A__	,    padding="""longest"""	,    return_tensors="""pt"""					).input_ids

            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A__	,    A__					):
                 __lowerCamelCase						:       int				   =				self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]					)
                 logger.warning(
                     """The following part of your input was truncated because CLIP can only handle sequences up to"""
                     f" {self.tokenizer.model_max_length} tokens: {removed_text}"					)

            __lowerCamelCase						:       Union[str, Any]				   =				text_input_ids.to(A__					)
            __lowerCamelCase						:       Tuple				   =				text_inputs.attention_mask.to(A__					)

            __lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				self.text_encoder(
                input_ids=A__	,    attention_mask=A__					)

            __lowerCamelCase						:       str				   =				prompt_embeds.repeat_interleave(A__	,    dim=0					)
            __lowerCamelCase						:       Tuple				   =				text_encoder_hidden_states.repeat_interleave(A__	,    dim=0					)
            __lowerCamelCase						:       Union[str, Any]				   =				text_mask.repeat_interleave(A__	,    dim=0					)

            if do_classifier_free_guidance:
                 __lowerCamelCase						:       List[str]
                 if negative_prompt is None:
                      __lowerCamelCase						:       Any				   =				[""""""] * batch_size
                 elif type(A__					) is not type(A__					):
                      raise TypeError(
                          f"`negative_prompt` should be the same type to `prompt`, but got {type(A__					)} !="
                          f" {type(A__					)}."					)
                 elif isinstance(A__	,    A__					):
                      __lowerCamelCase						:       Dict				   =				[negative_prompt]
                 elif batch_size != len(A__					):
                      raise ValueError(
                          f"`negative_prompt`: {negative_prompt} has batch size {len(A__					)}, but `prompt`:"
                          f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                          """ the batch size of `prompt`."""					)
                 else:
                      __lowerCamelCase						:       List[Any]				   =				negative_prompt

                 __lowerCamelCase						:       Any				   =				self.tokenizer(
                     A__	,    padding="""max_length"""	,    max_length=77	,    truncation=A__	,    return_attention_mask=A__	,    add_special_tokens=A__	,    return_tensors="""pt"""	,    )
                 __lowerCamelCase						:       Optional[Any]				   =				uncond_input.input_ids.to(A__					)
                 __lowerCamelCase						:       int				   =				uncond_input.attention_mask.to(A__					)

                 __lowerCamelCase   ,      __lowerCamelCase						:       Tuple				   =				self.text_encoder(
                     input_ids=A__	,    attention_mask=A__					)

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

                 __lowerCamelCase						:       Union[str, Any]				   =				negative_prompt_embeds.shape[1]
                 __lowerCamelCase						:       Dict				   =				negative_prompt_embeds.repeat(1	,    A__					)
                 __lowerCamelCase						:       Optional[int]				   =				negative_prompt_embeds.view(batch_size * num_images_per_prompt	,    A__					)

                 __lowerCamelCase						:       Union[str, Any]				   =				uncond_text_encoder_hidden_states.shape[1]
                 __lowerCamelCase						:       Dict				   =				uncond_text_encoder_hidden_states.repeat(1	,    A__	,    1					)
                 __lowerCamelCase						:       Optional[int]				   =				uncond_text_encoder_hidden_states.view(
                     batch_size * num_images_per_prompt	,    A__	,    -1					)
                 __lowerCamelCase						:       Optional[Any]				   =				uncond_text_mask.repeat_interleave(A__	,    dim=0					)

                 # done duplicates

                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 __lowerCamelCase						:       int				   =				torch.cat([negative_prompt_embeds, prompt_embeds]					)
                 __lowerCamelCase						:       int				   =				torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)

                 __lowerCamelCase						:       int				   =				torch.cat([uncond_text_mask, text_mask]					)

            return prompt_embeds, text_encoder_hidden_states, text_mask




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


            """simple docstring"""







            if is_accelerate_available():
                 from accelerate import cpu_offload
            else:
                 raise ImportError("""Please install accelerate via `pip install accelerate`"""					)

            __lowerCamelCase						:       List[str]				   =				torch.device(f"cuda:{gpu_id}"					)

            __lowerCamelCase						:       Union[str, Any]				   =				[
                self.unet,
                self.text_encoder,
                self.movq,
            ]
            for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                      cpu_offload(A__	,    A__					)




       def 					a_					(					self      : Optional[int]	,    A__      : Optional[Any]=0					):


            """simple docstring"""







            if is_accelerate_available() and is_accelerate_version(""">="""	,    """0.17.0.dev0"""					):
                 from accelerate import cpu_offload_with_hook
            else:
                 raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""					)

            __lowerCamelCase						:       List[Any]				   =				torch.device(f"cuda:{gpu_id}"					)

            if self.device.type != "cpu":
                 self.to("""cpu"""	,    silence_dtype_warnings=A__					)
                 torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

            __lowerCamelCase						:       Tuple				   =				None
            for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
                 __lowerCamelCase   ,      __lowerCamelCase						:       Union[str, Any]				   =				cpu_offload_with_hook(A__	,    A__	,    prev_module_hook=A__					)

            if self.safety_checker is not None:
                 __lowerCamelCase   ,      __lowerCamelCase						:       List[str]				   =				cpu_offload_with_hook(self.safety_checker	,    A__	,    prev_module_hook=A__					)

            # We'll offload the last model manually.
            __lowerCamelCase						:       int				   =				hook




       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            if not hasattr(self.unet	,    """_hf_hook"""					):
                 return self.device
            for module in self.unet.modules():
                 if (
                     hasattr(A__	,    """_hf_hook"""					)
                     and hasattr(module._hf_hook	,    """execution_device"""					)
                     and module._hf_hook.execution_device is not None
                 ):
                      return torch.device(module._hf_hook.execution_device					)
            return self.device




       @torch.no_grad()
       @replace_example_docstring(A__					)
       def __call__(					self      : Any	,    A__      : Union[str, List[str]]	,    A__      : Union[torch.FloatTensor, List[torch.FloatTensor]]	,    A__      : Union[torch.FloatTensor, List[torch.FloatTensor]]	,    A__      : Optional[Union[str, List[str]]] = None	,    A__      : int = 512	,    A__      : int = 512	,    A__      : int = 100	,    A__      : float = 4.0	,    A__      : int = 1	,    A__      : Optional[Union[torch.Generator, List[torch.Generator]]] = None	,    A__      : Optional[torch.FloatTensor] = None	,    A__      : Optional[str] = "pil"	,    A__      : bool = True	,    ):


            """simple docstring"""







            if isinstance(A__	,    A__					):
                 __lowerCamelCase						:       Tuple				   =				1
            elif isinstance(A__	,    A__					):
                 __lowerCamelCase						:       Tuple				   =				len(A__					)
            else:
                 raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(A__					)}"					)

            __lowerCamelCase						:       Any				   =				self._execution_device

            __lowerCamelCase						:       List[Any]				   =				batch_size * num_images_per_prompt
            __lowerCamelCase						:       List[str]				   =				guidance_scale > 1.0

            __lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       str				   =				self._encode_prompt(
                A__	,    A__	,    A__	,    A__	,    A__					)

            if isinstance(A__	,    A__					):
                 __lowerCamelCase						:       Tuple				   =				torch.cat(A__	,    dim=0					)
            if isinstance(A__	,    A__					):
                 __lowerCamelCase						:       Union[str, Any]				   =				torch.cat(A__	,    dim=0					)

            if do_classifier_free_guidance:
                 __lowerCamelCase						:       List[str]				   =				image_embeds.repeat_interleave(A__	,    dim=0					)
                 __lowerCamelCase						:       int				   =				negative_image_embeds.repeat_interleave(A__	,    dim=0					)

            __lowerCamelCase						:       List[str]				   =				torch.cat([negative_image_embeds, image_embeds]	,    dim=0					).to(
                dtype=prompt_embeds.dtype	,    device=A__					)

            self.scheduler.set_timesteps(A__	,    device=A__					)
            __lowerCamelCase						:       Optional[int]				   =				self.scheduler.timesteps

            __lowerCamelCase						:       str				   =				self.unet.config.in_channels

            __lowerCamelCase   ,      __lowerCamelCase						:       List[str]				   =				get_new_h_w(A__	,    A__	,    self.movq_scale_factor					)

            # create initial latent
            __lowerCamelCase						:       Optional[int]				   =				self.prepare_latents(
                (batch_size, num_channels_latents, height, width)	,    text_encoder_hidden_states.dtype	,    A__	,    A__	,    A__	,    self.scheduler	,    )

            for i, t in enumerate(self.progress_bar(A__					)					):
                 # expand the latents if we are doing classifier free guidance
                 __lowerCamelCase						:       List[Any]				   =				torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

                 __lowerCamelCase						:       Optional[Any]				   =				{"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
                 __lowerCamelCase						:       List[str]				   =				self.unet(
                     sample=A__	,    timestep=A__	,    encoder_hidden_states=A__	,    added_cond_kwargs=A__	,    return_dict=A__	,    )[0]

                 if do_classifier_free_guidance:
                      __lowerCamelCase   ,      __lowerCamelCase						:       Optional[Any]				   =				noise_pred.split(latents.shape[1]	,    dim=1					)
                      __lowerCamelCase   ,      __lowerCamelCase						:       str				   =				noise_pred.chunk(2					)
                      __lowerCamelCase   ,      __lowerCamelCase						:       List[str]				   =				variance_pred.chunk(2					)
                      __lowerCamelCase						:       int				   =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                      __lowerCamelCase						:       Dict				   =				torch.cat([noise_pred, variance_pred_text]	,    dim=1					)

                 if not (
                     hasattr(self.scheduler.config	,    """variance_type"""					)
                     and self.scheduler.config.variance_type in ["learned", "learned_range"]
                 ):
                      __lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				noise_pred.split(latents.shape[1]	,    dim=1					)

                 # compute the previous noisy sample x_t -> x_t-1
                 __lowerCamelCase						:       Dict				   =				self.scheduler.step(
                     A__	,    A__	,    A__	,    generator=A__	,    ).prev_sample
            # post-processing
            __lowerCamelCase						:       Union[str, Any]				   =				self.movq.decode(A__	,    force_not_quantize=A__					)["""sample"""]

            if output_type not in ["pt", "np", "pil"]:
                 raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"					)

            if output_type in ["np", "pil"]:
                 __lowerCamelCase						:       List[Any]				   =				image * 0.5 + 0.5
                 __lowerCamelCase						:       Dict				   =				image.clamp(0	,    1					)
                 __lowerCamelCase						:       Optional[Any]				   =				image.cpu().permute(0	,    2	,    3	,    1					).float().numpy()

            if output_type == "pil":
                 __lowerCamelCase						:       Tuple				   =				self.numpy_to_pil(A__					)

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=A__					)











'''simple docstring'''

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     str   		=			RobertaTokenizer
       snake_case__    :     Optional[int]   		=			RobertaTokenizerFast
       snake_case__    :     Union[str, Any]   		=			True
       snake_case__    :     Tuple   		=			{'cls_token': '<s>'}




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


            """simple docstring"""







            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            __lowerCamelCase						:       Tuple				   =				[
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """<unk>""",
            ]
            __lowerCamelCase						:       Tuple				   =				dict(zip(A__	,    range(len(A__					)					)					)					)
            __lowerCamelCase						:       List[str]				   =				["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            __lowerCamelCase						:       Tuple				   =				{"""unk_token""": """<unk>"""}

            __lowerCamelCase						:       Optional[int]				   =				os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""vocab_file"""]					)
            __lowerCamelCase						:       Union[str, Any]				   =				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(A__					) + """\n"""					)
            with open(self.merges_file	,    """w"""	,    encoding="""utf-8"""					) as fp:
                 fp.write("""\n""".join(A__					)					)




       def 					a_					(					self      : Tuple	,    **A__      : Dict					):


            """simple docstring"""







            kwargs.update(self.special_tokens_map					)
            return self.tokenizer_class.from_pretrained(self.tmpdirname	,    **A__					)




       def 					a_					(					self      : str	,    **A__      : List[Any]					):


            """simple docstring"""







            kwargs.update(self.special_tokens_map					)
            return RobertaTokenizerFast.from_pretrained(self.tmpdirname	,    **A__					)




       def 					a_					(					self      : Tuple	,    A__      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				"""lower newer"""
            __lowerCamelCase						:       int				   =				"""lower newer"""
            return input_text, output_text




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


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				self.tokenizer_class(self.vocab_file	,    self.merges_file	,    **self.special_tokens_map					)
            __lowerCamelCase						:       Any				   =				"""lower newer"""
            __lowerCamelCase						:       Union[str, Any]				   =				["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            __lowerCamelCase						:       Optional[Any]				   =				tokenizer.tokenize(A__					)  # , add_prefix_space=True)
            self.assertListEqual(A__	,    A__					)

            __lowerCamelCase						:       List[str]				   =				tokens + [tokenizer.unk_token]
            __lowerCamelCase						:       str				   =				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(A__					)	,    A__					)




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.get_tokenizer()

            self.assertListEqual(tokenizer.encode("""Hello world!"""	,    add_special_tokens=A__					)	,    [0, 31414, 232, 328, 2]					)
            self.assertListEqual(
                tokenizer.encode("""Hello world! cécé herlolip 418"""	,    add_special_tokens=A__					)	,    [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]	,    )




       @slow
       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.tokenizer_class.from_pretrained("""roberta-base"""					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode("""sequence builders"""	,    add_special_tokens=A__					)
            __lowerCamelCase						:       Dict				   =				tokenizer.encode("""multi-sequence build"""	,    add_special_tokens=A__					)

            __lowerCamelCase						:       Dict				   =				tokenizer.encode(
                """sequence builders"""	,    add_special_tokens=A__	,    add_prefix_space=A__					)
            __lowerCamelCase						:       List[Any]				   =				tokenizer.encode(
                """sequence builders"""	,    """multi-sequence build"""	,    add_special_tokens=A__	,    add_prefix_space=A__					)

            __lowerCamelCase						:       List[str]				   =				tokenizer.build_inputs_with_special_tokens(A__					)
            __lowerCamelCase						:       Tuple				   =				tokenizer.build_inputs_with_special_tokens(A__	,    A__					)

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				self.get_tokenizer()

            __lowerCamelCase						:       str				   =				"""Encode this sequence."""
            __lowerCamelCase						:       str				   =				tokenizer.byte_encoder[""" """.encode("""utf-8"""					)[0]]

            # Testing encoder arguments
            __lowerCamelCase						:       Union[str, Any]				   =				tokenizer.encode(A__	,    add_special_tokens=A__	,    add_prefix_space=A__					)
            __lowerCamelCase						:       List[str]				   =				tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
            self.assertNotEqual(A__	,    A__					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode(A__	,    add_special_tokens=A__	,    add_prefix_space=A__					)
            __lowerCamelCase						:       int				   =				tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
            self.assertEqual(A__	,    A__					)

            tokenizer.add_special_tokens({"""bos_token""": """<s>"""}					)
            __lowerCamelCase						:       Tuple				   =				tokenizer.encode(A__	,    add_special_tokens=A__					)
            __lowerCamelCase						:       List[str]				   =				tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
            self.assertNotEqual(A__	,    A__					)

            # Testing spaces after special tokens
            __lowerCamelCase						:       str				   =				"""<mask>"""
            tokenizer.add_special_tokens(
                {"""mask_token""": AddedToken(A__	,    lstrip=A__	,    rstrip=A__					)}					)  # mask token has a left space
            __lowerCamelCase						:       str				   =				tokenizer.convert_tokens_to_ids(A__					)

            __lowerCamelCase						:       str				   =				"""Encode <mask> sequence"""
            __lowerCamelCase						:       Optional[int]				   =				"""Encode <mask>sequence"""

            __lowerCamelCase						:       Optional[Any]				   =				tokenizer.encode(A__					)
            __lowerCamelCase						:       str				   =				encoded.index(A__					)
            __lowerCamelCase						:       Any				   =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
            self.assertEqual(A__	,    A__					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode(A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				encoded.index(A__					)
            __lowerCamelCase						:       Optional[int]				   =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
            self.assertNotEqual(A__	,    A__					)




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


            """simple docstring"""







            pass




       def 					a_					(					self      : str					):


            """simple docstring"""







            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
                      __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(A__	,    **A__					)
                      __lowerCamelCase						:       List[Any]				   =				self.tokenizer_class.from_pretrained(A__	,    **A__					)
                      __lowerCamelCase						:       Tuple				   =				"""A, <mask> AllenNLP sentence."""
                      __lowerCamelCase						:       int				   =				tokenizer_r.encode_plus(A__	,    add_special_tokens=A__	,    return_token_type_ids=A__					)
                      __lowerCamelCase						:       List[str]				   =				tokenizer_p.encode_plus(A__	,    add_special_tokens=A__	,    return_token_type_ids=A__					)

                      # token_type_ids should put 0 everywhere
                      self.assertEqual(sum(tokens_r["""token_type_ids"""]					)	,    sum(tokens_p["""token_type_ids"""]					)					)

                      # attention_mask should put 1 everywhere, so sum over length should be 1
                      self.assertEqual(
                          sum(tokens_r["""attention_mask"""]					) / len(tokens_r["""attention_mask"""]					)	,    sum(tokens_p["""attention_mask"""]					) / len(tokens_p["""attention_mask"""]					)	,    )

                      __lowerCamelCase						:       Optional[int]				   =				tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]					)
                      __lowerCamelCase						:       Any				   =				tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]					)

                      # Rust correctly handles the space before the mask while python doesnt
                      self.assertSequenceEqual(tokens_p["""input_ids"""]	,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)
                      self.assertSequenceEqual(tokens_r["""input_ids"""]	,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)

                      self.assertSequenceEqual(
                          A__	,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)
                      self.assertSequenceEqual(
                          A__	,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            for trim_offsets, add_prefix_space in itertools.product([True, False]	,    repeat=2					):
                 __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(
                     self.tmpdirname	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)

                 __lowerCamelCase						:       Union[str, Any]				   =				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
                 __lowerCamelCase						:       Dict				   =				json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

                 self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]	,    A__					)

                 self.assertEqual(post_processor_state["""add_prefix_space"""]	,    A__					)
                 self.assertEqual(post_processor_state["""trim_offsets"""]	,    A__					)




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
                      __lowerCamelCase						:       Tuple				   =				"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                      __lowerCamelCase						:       Optional[Any]				   =				f"{text_of_1_token} {text_of_1_token}"

                      __lowerCamelCase						:       List[Any]				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       str				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					) + 1, len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       List[str]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					) + 1, len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       int				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Union[str, Any]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					), len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Optional[int]				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Tuple				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (len(A__					), len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       str				   =				f" {text}"

                      # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                      #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                      # )
                      # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                      # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                      # self.assertEqual(
                      #     encoding.offset_mapping[1],
                      #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                      # )

                      __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Optional[int]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (1, 1 + len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (1 + len(A__					) + 1, 1 + len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Dict				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Dict				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, 1 + len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (1 + len(A__					), 1 + len(A__					) + 1 + len(A__					))	,    )

                      __lowerCamelCase						:       Optional[int]				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Optional[int]				   =				tokenizer_r(A__	,    return_offsets_mapping=A__	,    add_special_tokens=A__					)
                      self.assertEqual(encoding.offset_mapping[0]	,    (0, 1 + len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (1 + len(A__					), 1 + len(A__					) + 1 + len(A__					))	,    )



