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   lowercase      (       unittest.TestCase       ):




       def 		__snake_case(  self				:   Tuple )   ->			str:



            '''simple docstring'''

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




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



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    FlaxStableDiffusionPipeline.from_pretrained(
                "stabilityai/stable-diffusion-2"		,							revision="bf16"		,							dtype=jnp.bfloataa		,							)

            SCREAMING_SNAKE_CASE           =    """A painting of a squirrel eating a burger"""
            SCREAMING_SNAKE_CASE           =    jax.device_count()
            SCREAMING_SNAKE_CASE           =    num_samples * [prompt]
            SCREAMING_SNAKE_CASE           =    sd_pipe.prepare_inputs(lowerCamelCase_ )

            SCREAMING_SNAKE_CASE           =    replicate(lowerCamelCase_ )
            SCREAMING_SNAKE_CASE           =    shard(lowerCamelCase_ )

            SCREAMING_SNAKE_CASE           =    jax.random.PRNGKey(0 )
            SCREAMING_SNAKE_CASE           =    jax.random.split(lowerCamelCase_		,							jax.device_count() )

            SCREAMING_SNAKE_CASE           =    sd_pipe(lowerCamelCase_		,							lowerCamelCase_		,							lowerCamelCase_		,							num_inference_steps=25		,							jit=lowerCamelCase_ )[0]
            assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

            SCREAMING_SNAKE_CASE           =    jnp.asarray(jax.device_get(image_slice.flatten() ) )
            SCREAMING_SNAKE_CASE           =    jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
            print(F"output_slice: {output_slice}" )
            assert jnp.abs(output_slice - expected_slice ).max() < 1e-2





       def 		__snake_case(  self				:   Tuple )   ->			int:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE           =    """stabilityai/stable-diffusion-2"""
            SCREAMING_SNAKE_CASE           =    FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCamelCase_		,							subfolder="scheduler" )
            SCREAMING_SNAKE_CASE           =    FlaxStableDiffusionPipeline.from_pretrained(
                lowerCamelCase_		,							scheduler=lowerCamelCase_		,							revision="bf16"		,							dtype=jnp.bfloataa		,							)
            SCREAMING_SNAKE_CASE           =    scheduler_params

            SCREAMING_SNAKE_CASE           =    """A painting of a squirrel eating a burger"""
            SCREAMING_SNAKE_CASE           =    jax.device_count()
            SCREAMING_SNAKE_CASE           =    num_samples * [prompt]
            SCREAMING_SNAKE_CASE           =    sd_pipe.prepare_inputs(lowerCamelCase_ )

            SCREAMING_SNAKE_CASE           =    replicate(lowerCamelCase_ )
            SCREAMING_SNAKE_CASE           =    shard(lowerCamelCase_ )

            SCREAMING_SNAKE_CASE           =    jax.random.PRNGKey(0 )
            SCREAMING_SNAKE_CASE           =    jax.random.split(lowerCamelCase_		,							jax.device_count() )

            SCREAMING_SNAKE_CASE           =    sd_pipe(lowerCamelCase_		,							lowerCamelCase_		,							lowerCamelCase_		,							num_inference_steps=25		,							jit=lowerCamelCase_ )[0]
            assert images.shape == (jax.device_count(), 1, 768, 768, 3)

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

            SCREAMING_SNAKE_CASE           =    jnp.asarray(jax.device_get(image_slice.flatten() ) )
            SCREAMING_SNAKE_CASE           =    jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
            print(F"output_slice: {output_slice}" )
            assert jnp.abs(output_slice - expected_slice ).max() < 1e-2





from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


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

_lowerCamelCase   :				Any     			=						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				int     			=						{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

_lowerCamelCase   :				Tuple     			=						{'''allegro/herbert-base-cased''': 5_14}
_lowerCamelCase   :				Optional[int]     			=						{}


class   lowercase      (       a       ):
							lowercase__  :    List[str]									=							VOCAB_FILES_NAMES
							lowercase__  :    str									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Tuple									=							PRETRAINED_INIT_CONFIGURATION
							lowercase__  :    Optional[int]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    str									=							HerbertTokenizer




							def __init__(  self				:   Dict		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   Optional[int]="<s>"		,							_UpperCamelCase				:   Union[str, Any]="<unk>"		,							_UpperCamelCase				:   List[str]="<pad>"		,							_UpperCamelCase				:   List[str]="<mask>"		,							_UpperCamelCase				:   Tuple="</s>"		,							**_UpperCamelCase				:   Any		,							)   ->			str:



												'''simple docstring'''

												super().__init__(
												    _UpperCamelCase		,							_UpperCamelCase		,							tokenizer_file=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							mask_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							**_UpperCamelCase		,							)




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



												'''simple docstring'''

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

												return cls + token_ids_a + sep + token_ids_a + sep




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None		,							_UpperCamelCase				:   bool = False )   ->			List[int]:



												'''simple docstring'''

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

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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [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 ) * [0] + len(token_ids_a + sep ) * [1]





							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




