





'''simple docstring'''

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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__						:      Optional[Any]	=    logging.get_logger(__name__)

lowercase__						:      Optional[int]	=    '▁'

lowercase__						:      int	=    {'vocab_file': 'prophetnet.tokenizer'}

lowercase__						:      Dict	=    {
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

lowercase__						:      Tuple	=    {
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

lowercase__						:      int	=    {
    'microsoft/xprophetnet-large-wiki100-cased': 5_12,
}







def  a__    (    lowercase :					Optional[Any]				)		->		List[str]:





     """simple docstring"""

     _UpperCamelCase       =	collections.OrderedDict()
     with open(lowercase, '''r''', encoding='''utf-8'''				) as reader:
          _UpperCamelCase       =	reader.readlines()
     for index, token in enumerate(lowercase				):
          _UpperCamelCase       =	token.rstrip('''\n'''				)
          _UpperCamelCase       =	index
     return vocab




class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					str						    =   VOCAB_FILES_NAMES
       _snake_case     :					Optional[Any]						    =   PRETRAINED_VOCAB_FILES_MAP
       _snake_case     :					Optional[int]						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _snake_case     :					Any						    =   ['input_ids', 'attention_mask']





       def __init__(							self       :		str	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		List[Any]="[SEP]"	,			lowerCAmelCase__       :		Dict="[SEP]"	,			lowerCAmelCase__       :		Optional[int]="[SEP]"	,			lowerCAmelCase__       :		str="[UNK]"	,			lowerCAmelCase__       :		Optional[Any]="[PAD]"	,			lowerCAmelCase__       :		Optional[int]="[CLS]"	,			lowerCAmelCase__       :		Dict="[MASK]"	,			lowerCAmelCase__       :		Optional[Dict[str, Any]] = None	,			**lowerCAmelCase__       :		Any	,			)   ->					None:

            '''simple docstring'''




            _UpperCamelCase       =	{} if sp_model_kwargs is None else sp_model_kwargs

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

            try:
                 import sentencepiece as spm
            except ImportError:
                 logger.warning(
                     '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                     ''' pip install sentencepiece'''					)
                 raise

            _UpperCamelCase       =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(str(lowerCAmelCase__					)					)
            _UpperCamelCase       =	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'

            # put special tokens and [unused] tokens into the vocab
            _UpperCamelCase       =	{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

            for i in range(10					):
                 _UpperCamelCase       =	f"""[unused{i}]"""
                 _UpperCamelCase       =	5 + i

            # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
            _UpperCamelCase       =	12
            _UpperCamelCase       =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}
            for k in self.fairseq_tokens_to_ids.keys():
                 self.unique_no_split_tokens.append(lowerCAmelCase__					)





       def __getstate__(							self       :		Union[str, Any]					)   ->					Tuple:

            '''simple docstring'''




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





       def __setstate__(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		int					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	d
            try:
                 import sentencepiece as spm
            except ImportError:
                 logger.warning(
                     '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                     ''' pip install sentencepiece'''					)
                 raise

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

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





       def snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		List[int]	,			lowerCAmelCase__       :		Optional[List[int]] = None	,			lowerCAmelCase__       :		bool = False					)   ->					List[int]:

            '''simple docstring'''




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

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





       def snake_case__						(							self       :		Union[str, Any]	,			lowerCAmelCase__       :		List[int]	,			lowerCAmelCase__       :		Optional[List[int]] = None					)   ->					List[int]:

            '''simple docstring'''




            _UpperCamelCase       =	[self.sep_token_id]

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





       @property
       def snake_case__						(							self       :		Any					)   ->					Optional[int]:

            '''simple docstring'''




            return len(self.sp_model					) + self.fairseq_offset





       def snake_case__						(							self       :		str					)   ->					Optional[int]:

            '''simple docstring'''




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





       def snake_case__						(							self       :		Tuple	,			lowerCAmelCase__       :		str					)   ->					str:

            '''simple docstring'''




            return self.sp_model.encode(lowerCAmelCase__	,			out_type=lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		Optional[int]					)   ->					Optional[Any]:

            '''simple docstring'''




            if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
            _UpperCamelCase       =	self.sp_model.PieceToId(lowerCAmelCase__					)

            # 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 snake_case__						(							self       :		List[str]	,			lowerCAmelCase__       :		List[Any]					)   ->					Optional[Any]:

            '''simple docstring'''




            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 snake_case__						(							self       :		Any	,			lowerCAmelCase__       :		int					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	''''''.join(lowerCAmelCase__					).replace(lowerCAmelCase__	,			''' '''					).strip()
            return out_string





       def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		Optional[str] = None					)   ->					Tuple[str]:

            '''simple docstring'''




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

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

            return (out_vocab_file,)







       def snake_case__						(							self       :		Any	,			lowerCAmelCase__       :		List[int]	,			lowerCAmelCase__       :		Optional[List[int]] = None					)   ->					List[int]:

            '''simple docstring'''




            if token_ids_a is None:
                 return token_ids_a + [self.sep_token_id]
            _UpperCamelCase       =	[self.sep_token_id]
            return token_ids_a + sep + token_ids_a + sep













'''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     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		List[str]					)   ->					Optional[int]:

            '''simple docstring'''




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





       def snake_case__						(							self       :		int					)   ->					Union[str, Any]:

            '''simple docstring'''




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

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

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

            _UpperCamelCase       =	jax.random.PRNGKey(0					)
            _UpperCamelCase       =	jax.random.split(lowerCAmelCase__	,			jax.device_count()					)

            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

            _UpperCamelCase       =	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)

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

            _UpperCamelCase       =	jnp.asarray(jax.device_get(image_slice.flatten()					)					)
            _UpperCamelCase       =	jnp.array(
                [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]					)
            print(f"""output_slice: {output_slice}"""					)
            assert jnp.abs(output_slice - expected_slice					).max() < 1e-2







       def snake_case__						(							self       :		Union[str, Any]					)   ->					Union[str, Any]:

            '''simple docstring'''




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

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

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

            _UpperCamelCase       =	jax.random.PRNGKey(0					)
            _UpperCamelCase       =	jax.random.split(lowerCAmelCase__	,			jax.device_count()					)

            _UpperCamelCase       =	replicate(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)
            _UpperCamelCase       =	shard(lowerCAmelCase__					)

            _UpperCamelCase       =	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)

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

            _UpperCamelCase       =	jnp.asarray(jax.device_get(image_slice.flatten()					)					)
            _UpperCamelCase       =	jnp.array(
                [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]					)
            print(f"""output_slice: {output_slice}"""					)
            assert jnp.abs(output_slice - expected_slice					).max() < 1e-2



