





'''simple docstring'''

import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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					):







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self		)		->						Optional[int]:






        """simple docstring"""





        _snake_case     ,							_snake_case		:	Tuple					     =     FlaxStableDiffusionPipeline.from_pretrained(
            '''stabilityai/stable-diffusion-2'''     ,     revision='''bf16'''     ,     dtype=jnp.bfloataa     ,     )

        _snake_case		:	List[Any]					     =     '''A painting of a squirrel eating a burger'''
        _snake_case		:	List[Any]					     =     jax.device_count()
        _snake_case		:	Union[str, Any]					     =     num_samples * [prompt]
        _snake_case		:	Tuple					     =     sd_pipe.prepare_inputs(lowercase__		)

        _snake_case		:	int					     =     replicate(lowercase__		)
        _snake_case		:	List[Any]					     =     shard(lowercase__		)

        _snake_case		:	List[str]					     =     jax.random.PRNGKey(0		)
        _snake_case		:	List[Any]					     =     jax.random.split(lowercase__     ,     jax.device_count()		)

        _snake_case		:	Optional[Any]					     =     sd_pipe(lowercase__     ,     lowercase__     ,     lowercase__     ,     num_inference_steps=25     ,     jit=lowercase__		)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

        _snake_case		:	List[str]					     =     jnp.asarray(jax.device_get(image_slice.flatten()		)		)
        _snake_case		:	Dict					     =     jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]		)
        print(F'''output_slice: {output_slice}'''		)
        assert jnp.abs(output_slice - expected_slice		).max() < 1E-2







  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     '''stabilityai/stable-diffusion-2'''
        _snake_case     ,							_snake_case		:	Any					     =     FlaxDPMSolverMultistepScheduler.from_pretrained(lowercase__     ,     subfolder='''scheduler'''		)
        _snake_case     ,							_snake_case		:	List[str]					     =     FlaxStableDiffusionPipeline.from_pretrained(
            lowercase__     ,     scheduler=lowercase__     ,     revision='''bf16'''     ,     dtype=jnp.bfloataa     ,     )
        _snake_case		:	Optional[Any]					     =     scheduler_params

        _snake_case		:	Optional[int]					     =     '''A painting of a squirrel eating a burger'''
        _snake_case		:	List[Any]					     =     jax.device_count()
        _snake_case		:	Tuple					     =     num_samples * [prompt]
        _snake_case		:	Optional[Any]					     =     sd_pipe.prepare_inputs(lowercase__		)

        _snake_case		:	Tuple					     =     replicate(lowercase__		)
        _snake_case		:	Any					     =     shard(lowercase__		)

        _snake_case		:	str					     =     jax.random.PRNGKey(0		)
        _snake_case		:	List[Any]					     =     jax.random.split(lowercase__     ,     jax.device_count()		)

        _snake_case		:	str					     =     sd_pipe(lowercase__     ,     lowercase__     ,     lowercase__     ,     num_inference_steps=25     ,     jit=lowercase__		)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        _snake_case		:	int					     =     images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
        _snake_case		:	List[Any]					     =     images[0, 253:256, 253:256, -1]

        _snake_case		:	Tuple					     =     jnp.asarray(jax.device_get(image_slice.flatten()		)		)
        _snake_case		:	str					     =     jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]		)
        print(F'''output_slice: {output_slice}'''		)
        assert jnp.abs(output_slice - expected_slice		).max() < 1E-2









'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


UpperCAmelCase					:		Optional[int]		=							logging.get_logger(__name__)

UpperCAmelCase					:		List[Any]		=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase					:		Any		=							{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase					:		Optional[Any]		=							{
    'gpt-neox-20b': 2_0_4_8,
}





class lowerCamelCase						(a__					):
  _lowercase  :	Optional[int]       					=				VOCAB_FILES_NAMES
  _lowercase  :	str       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Dict       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	Optional[int]       					=				["""input_ids""", """attention_mask"""]







  def __init__(		self     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__=False     ,     **lowercase__     ,     )		->						List[Any]:






        """simple docstring"""





        super().__init__(
            lowercase__     ,     lowercase__     ,     tokenizer_file=lowercase__     ,     unk_token=lowercase__     ,     bos_token=lowercase__     ,     eos_token=lowercase__     ,     add_prefix_space=lowercase__     ,     **lowercase__     ,     )

        _snake_case		:	Optional[int]					     =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
        if pre_tok_state.get('''add_prefix_space'''     ,     lowercase__		) != add_prefix_space:
              _snake_case		:	int					     =     getattr(lowercase__     ,     pre_tok_state.pop('''type'''		)		)
              _snake_case		:	int					     =     add_prefix_space
              _snake_case		:	Optional[Any]					     =     pre_tok_class(**lowercase__		)

        _snake_case		:	List[str]					     =     add_prefix_space







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[str]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     self._tokenizer.model.save(lowercase__     ,     name=lowercase__		)
        return tuple(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[int]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     []
        for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowercase__     ,     add_special_tokens=lowercase__		) + [self.eos_token_id]		)

        if len(lowercase__		) > self.model_max_length:
              _snake_case		:	Dict					     =     input_ids[-self.model_max_length :]
        return input_ids


