





import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

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


if is_torch_available():
    import torch


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


snake_case_           =      logging.get_logger(__name__)
snake_case_           =      {'vocab_file': 'spiece.model'}

snake_case_           =      {
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

snake_case_           =      {
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    int							=   VOCAB_FILES_NAMES
      A_						:    Any							=   PRETRAINED_VOCAB_FILES_MAP
      A_						:    List[str]							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A_						:    List[Any]							=   ['input_ids', 'attention_mask']


      def __init__(self	:				Tuple    ,      a__	:				str    ,      a__	:				Any=False    ,      a__	:				Any=False    ,      a__	:				List[str]=False    ,      a__	:				List[Any]=None    ,      a__	:				List[Any]=None    ,      a__	:				Optional[Any]=None    ,      a__	:				Tuple=None    ,      a__	:				Optional[Dict[str, Any]] = None    ,      **a__	:				str    ,      ):





         """simple docstring"""



         __snake_case								=       {} if sp_model_kwargs is None else sp_model_kwargs

         __snake_case								=       kwargs.get('''name_or_path'''				)
         if name_or_path is None:
            logger.warning(
                '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
                ''' you are testing the model, this can safely be ignored'''				)
            __snake_case								=       '''None'''

         # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
         __snake_case								=       '''<|endoftext|>''' if eos_token is None else eos_token
         __snake_case								=       '''<unk>''' if unk_token is None else unk_token
         if "gpt-sw3-7b" in name_or_path:
            __snake_case								=       unk_token if pad_token is None else pad_token
            __snake_case								=       eos_token if bos_token is None else bos_token
         else:
            __snake_case								=       '''<pad>''' if pad_token is None else pad_token
            __snake_case								=       '''<s>''' if bos_token is None else bos_token

         super().__init__(
             do_lower_case=a__    ,      remove_space=a__    ,      keep_accents=a__    ,      bos_token=a__    ,      eos_token=a__    ,      unk_token=a__    ,      pad_token=a__    ,      sp_model_kwargs=self.sp_model_kwargs    ,      **a__    ,      )

         __snake_case								=       do_lower_case
         __snake_case								=       remove_space
         __snake_case								=       keep_accents
         __snake_case								=       vocab_file

         __snake_case								=       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.Load(a__				)

         # Used for whitespace normalization in input texts
         # fmt : off
         __snake_case								=       {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
         # fmt : on

         # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
         __snake_case								=       re.compile(
             f"""[{''.join(map(a__    ,      list(range(0    ,      9				)				) + list(range(11    ,      32				)				) + list(range(127    ,      160				)				) + [160, 173, 8203]				)				)}]"""				)


      def __getstate__(self	:				str				):





         """simple docstring"""



         __snake_case								=       self.__dict__.copy()
         __snake_case								=       None
         return state


      def __setstate__(self	:				Union[str, Any]    ,      a__	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       d

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

         __snake_case								=       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.Load(self.vocab_file				)


      @property
      # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         return len(self.sp_model				)


      def        a			(self	:				List[str]    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       self.non_printing_characters_re.sub(''''''    ,      a__				)

         # Normalize whitespaces
         __snake_case								=       ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]				)

         # NFC Unicode normalization
         __snake_case								=       unicodedata.normalize('''NFC'''    ,      a__				)
         return text


      def        a			(self	:				Dict    ,      a__	:				str    ,      **a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       self.preprocess_text(a__				)
         return self.sp_model.encode(a__    ,      out_type=a__				)


      def        a			(self	:				Optional[Any]    ,      a__	:				str				):





         """simple docstring"""



         return self.sp_model.PieceToId(a__				)


      def        a			(self	:				List[Any]    ,      a__	:				int				):





         """simple docstring"""



         return self.sp_model.IdToPiece(a__				)


      @staticmethod
      def        a			(a__	:				str				):





         """simple docstring"""



         return out_string


      def        a			(self	:				List[str]    ,      a__	:				List[str]				):





         """simple docstring"""



         __snake_case								=       []
         __snake_case								=       ''''''
         __snake_case								=       False
         for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
               # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
               if not prev_is_special:
                  out_string += " "

               out_string += self.sp_model.decode(a__				) + token
               __snake_case								=       True
               __snake_case								=       []
            else:
               current_sub_tokens.append(a__				)
               __snake_case								=       False
         out_string += self.sp_model.decode(a__				)

         return out_string


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





         """simple docstring"""



         __snake_case								=       {self.convert_ids_to_tokens(a__				): i for i in range(self.vocab_size				)}
         vocab.update(self.added_tokens_encoder				)
         return vocab


      def        a			(self	:				Tuple    ,      a__	:				str    ,      a__	:				Optional[str] = None				):





         """simple docstring"""



         if not os.path.isdir(a__				):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
            return
         __snake_case								=       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:
               __snake_case								=       self.sp_model.serialized_model_proto()
               fi.write(a__				)

         return (out_vocab_file,)


      def        a			(self	:				str    ,      a__	:				Union[str, List[str]]    ,      a__	:				Union[str, bool] = False				):





         """simple docstring"""



         if isinstance(a__    ,      a__				):
            __snake_case								=       self.preprocess_text(a__				)
            __snake_case								=       self.sp_model.encode(a__				)
         else:
            __snake_case								=       [self.preprocess_text(a__				) for t in text]
            __snake_case								=       self.sp_model.encode(a__				)

         if return_tensors is True or return_tensors == "pt":
            __snake_case								=       torch.tensor(a__				)

         return token_ids


      def        a			(self	:				Any    ,      a__	:				Union[int, List[int]]				):





         """simple docstring"""



         return self.sp_model.decode(a__				)


      def        a			(self	:				Dict    ,      a__	:				"Conversation"				):





         """simple docstring"""



         __snake_case								=       [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
         __snake_case								=       (
             f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(a__				) + f"""{self.bos_token}Bot:"""
         )
         return self.encode(text=a__				)








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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				int				):





         """simple docstring"""



         super().tearDown()
         gc.collect()


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-canny'''    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case	,     __snake_case								=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''    ,      controlnet=a__    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case								=       controlnet_params

         __snake_case								=       '''bird'''
         __snake_case								=       jax.device_count()
         __snake_case								=       pipe.prepare_text_inputs([prompts] * num_samples				)

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

         __snake_case								=       jax.random.PRNGKey(0				)
         __snake_case								=       jax.random.split(a__    ,      jax.device_count()				)

         __snake_case								=       replicate(a__				)
         __snake_case								=       shard(a__				)
         __snake_case								=       shard(a__				)

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

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

         __snake_case								=       jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         __snake_case								=       jnp.array(
             [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]				)
         print(f"""output_slice: {output_slice}"""				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1E-2


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-openpose'''    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case	,     __snake_case								=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''    ,      controlnet=a__    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case								=       controlnet_params

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

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

         __snake_case								=       jax.random.PRNGKey(0				)
         __snake_case								=       jax.random.split(a__    ,      jax.device_count()				)

         __snake_case								=       replicate(a__				)
         __snake_case								=       shard(a__				)
         __snake_case								=       shard(a__				)

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

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

         __snake_case								=       jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         __snake_case								=       jnp.array(
             [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]				)
         print(f"""output_slice: {output_slice}"""				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1E-2

