




from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


__magic_name__     :     Any  							=					logging.get_logger(__name__)

__magic_name__     :     int  							=					{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__magic_name__     :     int  							=					{
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

__magic_name__     :     str  							=					{
    """openai/clip-vit-base-patch32""": 77,
}





class 						SCREAMING_SNAKE_CASE__		(_a ):
     lowercase_   :			List[Any]   =   VOCAB_FILES_NAMES
     lowercase_   :			Optional[int]   =   PRETRAINED_VOCAB_FILES_MAP
     lowercase_   :			List[Any]   =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase_   :			Union[str, Any]   =   ["input_ids", "attention_mask"]
     lowercase_   :			Any   =   CLIPTokenizer
     def __init__(	self  :		Optional[Any]					,	__lowerCamelCase  :		List[Any]=None					,	__lowerCamelCase  :		Optional[Any]=None					,	__lowerCamelCase  :		Union[str, Any]=None					,	__lowerCamelCase  :		Union[str, Any]="<|endoftext|>"					,	__lowerCamelCase  :		Optional[int]="<|startoftext|>"					,	__lowerCamelCase  :		List[Any]="<|endoftext|>"					,	__lowerCamelCase  :		Optional[Any]="<|endoftext|>"					,	**__lowerCamelCase  :		Optional[Any]					,	):



       """simple docstring"""
       super().__init__(
           __lowerCamelCase					,	__lowerCamelCase					,	tokenizer_file=__lowerCamelCase					,	unk_token=__lowerCamelCase					,	bos_token=__lowerCamelCase					,	eos_token=__lowerCamelCase					,	pad_token=__lowerCamelCase					,	**__lowerCamelCase					,	)

       if not isinstance(self.backend_tokenizer.pre_tokenizer					,	pre_tokenizers.Sequence	):
         raise ValueError(
             '''The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'''
             ''' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'''
             ''' to be compatible with this version.The easiest way to do so is'''
             ''' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'''
             ''' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'''
             ''' transformers.'''	)

       self._wrap_decode_method_backend_tokenizer()
     def 					A__			(	self  :		Tuple	):



       """simple docstring"""
       lowerCAmelCase__								=			self.backend_tokenizer.decode

       def new_decode_method(*__lowerCamelCase  :		int					,	**__lowerCamelCase  :		int	):
         lowerCAmelCase__								=			orig_decode_method(*__lowerCamelCase					,	**__lowerCamelCase	)
         lowerCAmelCase__								=			text.replace(self.backend_tokenizer.model.end_of_word_suffix					,	''' '''	).strip()
         return text

       lowerCAmelCase__								=			new_decode_method
     def 					A__			(	self  :		List[Any]					,	__lowerCamelCase  :		List[int]					,	__lowerCamelCase  :		Optional[List[int]] = None	):



       """simple docstring"""
       lowerCAmelCase__								=			[self.bos_token_id]
       lowerCAmelCase__								=			[self.eos_token_id]

       if token_ids_a is None:
         return bos_token + token_ids_a + eos_token
       return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token
     def 					A__			(	self  :		Tuple					,	__lowerCamelCase  :		List[int]					,	__lowerCamelCase  :		Optional[List[int]] = None	):



       """simple docstring"""
       lowerCAmelCase__								=			[self.bos_token_id]
       lowerCAmelCase__								=			[self.eos_token_id]

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



     def 					A__			(	self  :		Optional[int]					,	__lowerCamelCase  :		str					,	__lowerCamelCase  :		Optional[str] = None	):



       """simple docstring"""
       lowerCAmelCase__								=			self._tokenizer.model.save(__lowerCamelCase					,	name=__lowerCamelCase	)
       return tuple(__lowerCamelCase	)







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)





@flax.struct.dataclass
class 						SCREAMING_SNAKE_CASE__		:
     lowercase_   :			CommonSchedulerState

     # setable values
     lowercase_   :			jnp.ndarray
     lowercase_   :			jnp.ndarray
     lowercase_   :			jnp.ndarray
     lowercase_   :			Optional[int]   =   None

     # running values
     lowercase_   :			Optional[jnp.ndarray]   =   None
     @classmethod
     def 					A__			(	cls  :		List[str]					,	__lowerCamelCase  :		CommonSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray	):



       """simple docstring"""
       return cls(common=__lowerCamelCase					,	init_noise_sigma=__lowerCamelCase					,	timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase	)





@dataclass
class 						SCREAMING_SNAKE_CASE__		(_a ):
     lowercase_   :			LMSDiscreteSchedulerState





class 						SCREAMING_SNAKE_CASE__		(_a						,				_a ):
     lowercase_   :			int   =   [e.name for e in FlaxKarrasDiffusionSchedulers]

     lowercase_   :			jnp.dtype
     @property
     def 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""
       return True
     @register_to_config
     def __init__(	self  :		int					,	__lowerCamelCase  :		int = 10_00					,	__lowerCamelCase  :		float = 0.0001					,	__lowerCamelCase  :		float = 0.02					,	__lowerCamelCase  :		str = "linear"					,	__lowerCamelCase  :		Optional[jnp.ndarray] = None					,	__lowerCamelCase  :		str = "epsilon"					,	__lowerCamelCase  :		jnp.dtype = jnp.floataa					,	):



       """simple docstring"""
       lowerCAmelCase__								=			dtype
     def 					A__			(	self  :		List[str]					,	__lowerCamelCase  :		Optional[CommonSchedulerState] = None	):



       """simple docstring"""
       if common is None:
         lowerCAmelCase__								=			CommonSchedulerState.create(self	)

       lowerCAmelCase__								=			jnp.arange(0					,	self.config.num_train_timesteps	).round()[::-1]
       lowerCAmelCase__								=			((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

       # standard deviation of the initial noise distribution
       lowerCAmelCase__								=			sigmas.max()

       return LMSDiscreteSchedulerState.create(
           common=__lowerCamelCase					,	init_noise_sigma=__lowerCamelCase					,	timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase					,	)
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int	):



       """simple docstring"""
       ((lowerCAmelCase__)							,			)								=			jnp.where(state.timesteps == timestep					,	size=1	)
       lowerCAmelCase__								=			step_index[0]

       lowerCAmelCase__								=			state.sigmas[step_index]
       lowerCAmelCase__								=			sample / ((sigma**2 + 1) ** 0.5)
       return sample
     def 					A__			(	self  :		List[Any]					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		Optional[int]					,	__lowerCamelCase  :		str					,	__lowerCamelCase  :		Any	):



       """simple docstring"""

       def lms_derivative(__lowerCamelCase  :		int	):
         lowerCAmelCase__								=			1.0
         for k in range(__lowerCamelCase	):
           if current_order == k:
             continue
           prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
         return prod

       lowerCAmelCase__								=			integrate.quad(__lowerCamelCase					,	state.sigmas[t]					,	state.sigmas[t + 1]					,	epsrel=1e-4	)[0]

       return integrated_coeff
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		Tuple = ()	):



       """simple docstring"""
       lowerCAmelCase__								=			jnp.linspace(self.config.num_train_timesteps - 1					,	0					,	__lowerCamelCase					,	dtype=self.dtype	)

       lowerCAmelCase__								=			jnp.floor(__lowerCamelCase	).astype(jnp.intaa	)
       lowerCAmelCase__								=			jnp.ceil(__lowerCamelCase	).astype(jnp.intaa	)

       lowerCAmelCase__								=			jnp.mod(__lowerCamelCase					,	1.0	)

       lowerCAmelCase__								=			((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
       lowerCAmelCase__								=			(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
       lowerCAmelCase__								=			jnp.concatenate([sigmas, jnp.array([0.0]					,	dtype=self.dtype	)]	)

       lowerCAmelCase__								=			timesteps.astype(jnp.intaa	)

       # initial running values
       lowerCAmelCase__								=			jnp.zeros((0,) + shape					,	dtype=self.dtype	)

       return state.replace(
           timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase					,	num_inference_steps=__lowerCamelCase					,	derivatives=__lowerCamelCase					,	)
     def 					A__			(	self  :		Union[str, Any]					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int = 4					,	__lowerCamelCase  :		bool = True					,	):



       """simple docstring"""
       if state.num_inference_steps is None:
         raise ValueError(
             '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''	)

       lowerCAmelCase__								=			state.sigmas[timestep]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
         lowerCAmelCase__								=			sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         lowerCAmelCase__								=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""	)

       # 2. Convert to an ODE derivative
       lowerCAmelCase__								=			(sample - pred_original_sample) / sigma
       lowerCAmelCase__								=			state.replace(derivatives=jnp.append(state.derivatives					,	__lowerCamelCase	)	)
       if len(state.derivatives	) > order:
         lowerCAmelCase__								=			state.replace(derivatives=jnp.delete(state.derivatives					,	0	)	)

       # 3. Compute linear multistep coefficients
       lowerCAmelCase__								=			min(timestep + 1					,	__lowerCamelCase	)
       lowerCAmelCase__								=			[self.get_lms_coefficient(__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase	) for curr_order in range(__lowerCamelCase	)]

       # 4. Compute previous sample based on the derivatives path
       lowerCAmelCase__								=			sample + sum(
           coeff * derivative for coeff, derivative in zip(__lowerCamelCase					,	reversed(state.derivatives	)	)	)

       if not return_dict:
         return (prev_sample, state)

       return FlaxLMSSchedulerOutput(prev_sample=__lowerCamelCase					,	state=__lowerCamelCase	)
     def 					A__			(	self  :		Dict					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	):



       """simple docstring"""
       lowerCAmelCase__								=			state.sigmas[timesteps].flatten()
       lowerCAmelCase__								=			broadcast_to_shape_from_left(__lowerCamelCase					,	noise.shape	)

       lowerCAmelCase__								=			original_samples + noise * sigma

       return noisy_samples



     def __len__(	self  :		str	):



       """simple docstring"""
       return self.config.num_train_timesteps

