'''simple docstring'''

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_ : List[Any]						  =      logging.get_logger(__name__)

lowerCAmelCase_ : Optional[int]						  =      "▁"

lowerCAmelCase_ : Optional[Any]						  =      {"vocab_file": "sentencepiece.bpe.model"}

lowerCAmelCase_ : Union[str, Any]						  =      {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

lowerCAmelCase_ : List[str]						  =      {
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}





class 							lowercase						(						a__ ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=['input_ids', 'attention_mask']


   def __init__(   self				:	Any   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	int="<s>"   ,		__lowerCAmelCase				:	Optional[Any]="</s>"   ,		__lowerCAmelCase				:	int="</s>"   ,		__lowerCAmelCase				:	Any="<s>"   ,		__lowerCAmelCase				:	Any="<unk>"   ,		__lowerCAmelCase				:	List[Any]="<pad>"   ,		__lowerCAmelCase				:	Optional[Any]="<mask>"   ,		__lowerCAmelCase				:	int = None   ,		**__lowerCAmelCase				:	Any   ,		)      ->					Optional[int]:
         lowercase_     =      AddedToken(_A   ,		lstrip=_A   ,		rstrip=_A) if isinstance(_A   ,		_A) else mask_token

         lowercase_     =      {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_A   ,		eos_token=_A   ,		unk_token=_A   ,		sep_token=_A   ,		cls_token=_A   ,		pad_token=_A   ,		mask_token=_A   ,		sp_model_kwargs=self.sp_model_kwargs   ,		**_A   ,		)

         lowercase_     =      spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(str(_A))
         lowercase_     =      vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # Mimic fairseq token-to-id alignment for the first 4 token
         lowercase_     =      {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         lowercase_     =      1

         lowercase_     =      len(self.sp_model) + self.fairseq_offset
         lowercase_     =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def __getstate__(   self				:	Any)      ->					List[Any]:
         lowercase_     =      self.__dict__.copy()
         lowercase_     =      None
         lowercase_     =      self.sp_model.serialized_model_proto()
         return state


   def __setstate__(   self				:	str   ,		__lowerCAmelCase				:	Optional[int])      ->					Optional[Any]:
         lowercase_     =      d

         # for backward compatibility
         if not hasattr(self   ,		"sp_model_kwargs"):
               lowercase_     =      {}

         lowercase_     =      spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	List[str] = None)      ->					Optional[int]:

         if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowercase_     =      [self.cls_token_id]
         lowercase_     =      [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	Any = None   ,		__lowerCAmelCase				:	Dict = False)      ->					str:

         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_A   ,		token_ids_a=_A   ,		already_has_special_tokens=_A)

         if token_ids_a is None:
               return [1] + ([0] * len(_A)) + [1]
         return [1] + ([0] * len(_A)) + [1, 1] + ([0] * len(_A)) + [1]


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	Optional[int] = None)      ->					Optional[Any]:
         lowercase_     =      [self.sep_token_id]
         lowercase_     =      [self.cls_token_id]

         if token_ids_a is None:
               return len(cls + token_ids_a + sep) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


   @property
   def __UpperCAmelCase       (   self				:	List[Any])      ->					Tuple:
         return len(self.sp_model) + self.fairseq_offset + 1  # Add the <mask> token


   def __UpperCAmelCase       (   self				:	Tuple)      ->					int:
         lowercase_     =      {self.convert_ids_to_tokens(_A): i for i in range(self.vocab_size)}
         vocab.update(self.added_tokens_encoder)
         return vocab


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	Any)      ->					Optional[int]:
         return self.sp_model.encode(_A   ,		out_type=_A)


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

         if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
         lowercase_     =      self.sp_model.PieceToId(_A)

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


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

         if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset)


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	List[Any])      ->					Dict:
         lowercase_     =      ''.join(_A).replace(_A   ,		" ").strip()
         return out_string


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	List[str] = None)      ->					Dict:

         if not os.path.isdir(_A):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
         lowercase_     =      os.path.join(
             _A   ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

         if os.path.abspath(self.vocab_file) != os.path.abspath(_A) and os.path.isfile(self.vocab_file):
               copyfile(self.vocab_file   ,		_A)
         elif not os.path.isfile(self.vocab_file):
               with open(_A   ,		"wb") as fi:
                     lowercase_     =      self.sp_model.serialized_model_proto()
                     fi.write(_A)

         return (out_vocab_file,)







'''simple docstring'''

import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard





@slow
@require_flax
class 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	int)      ->					List[str]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()


   def __UpperCAmelCase       (   self				:	Dict)      ->					Any:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_     =      controlnet_params

         lowercase_     =      "bird"
         lowercase_     =      jax.device_count()
         lowercase_     =      pipe.prepare_text_inputs([prompts] * num_samples)

         lowercase_     =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png")
         lowercase_     =      pipe.prepare_image_inputs([canny_image] * num_samples)

         lowercase_     =      jax.random.PRNGKey(0)
         lowercase_     =      jax.random.split(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      pipe(
             prompt_ids=__lowerCAmelCase   ,		image=__lowerCAmelCase   ,		params=__lowerCAmelCase   ,		prng_seed=__lowerCAmelCase   ,		num_inference_steps=50   ,		jit=__lowerCAmelCase   ,		).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowercase_     =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
         lowercase_     =      images[0, 253:256, 253:256, -1]

         lowercase_     =      jnp.asarray(jax.device_get(image_slice.flatten()))
         lowercase_     =      jnp.array(
             [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2


   def __UpperCAmelCase       (   self				:	Tuple)      ->					List[str]:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_     =      controlnet_params

         lowercase_     =      "Chef in the kitchen"
         lowercase_     =      jax.device_count()
         lowercase_     =      pipe.prepare_text_inputs([prompts] * num_samples)

         lowercase_     =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png")
         lowercase_     =      pipe.prepare_image_inputs([pose_image] * num_samples)

         lowercase_     =      jax.random.PRNGKey(0)
         lowercase_     =      jax.random.split(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      pipe(
             prompt_ids=__lowerCAmelCase   ,		image=__lowerCAmelCase   ,		params=__lowerCAmelCase   ,		prng_seed=__lowerCAmelCase   ,		num_inference_steps=50   ,		jit=__lowerCAmelCase   ,		).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowercase_     =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
         lowercase_     =      images[0, 253:256, 253:256, -1]

         lowercase_     =      jnp.asarray(jax.device_get(image_slice.flatten()))
         lowercase_     =      jnp.array(
             [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2






