
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Union[str, Any]			  =       ["image_processor", "tokenizer"]
		__lowercase : Optional[int]			  =       "Pix2StructImageProcessor"
		__lowercase : Dict			  =       ("T5Tokenizer", "T5TokenizerFast")



		def __init__(				self       ,   A_       ,   A_ )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  False
				super().__init__(A_       ,   A_ )



		def __call__(				self       ,   A_=None       ,   A_ = None       ,   A_ = True       ,   A_ = False       ,   A_ = None       ,   A_ = None       ,   A_ = 2_048       ,   A_ = 0       ,   A_ = None       ,   A_ = None       ,   A_ = False       ,   A_ = False       ,   A_ = False       ,   A_ = False       ,   A_ = False       ,   A_ = True       ,   A_ = None       ,   **A_       ,   )						->      BatchEncoding:
				"""simple docstring"""
				if images is None and text is None:
						raise ValueError('You have to specify either images or text.' )

				# Get only text
				if images is None and not self.image_processor.is_vqa:
						UpperCamelCase						=  self.tokenizer
						UpperCamelCase						=  self.tokenizer(
						    text=A_       ,   add_special_tokens=A_       ,   padding=A_       ,   truncation=A_       ,   max_length=A_       ,   stride=A_       ,   pad_to_multiple_of=A_       ,   return_attention_mask=A_       ,   return_overflowing_tokens=A_       ,   return_special_tokens_mask=A_       ,   return_offsets_mapping=A_       ,   return_token_type_ids=A_       ,   return_length=A_       ,   verbose=A_       ,   return_tensors=A_       ,   **A_       ,   )
						return text_encoding

				if not self.image_processor.is_vqa:
						# add pixel_values
						UpperCamelCase						=  self.image_processor(
						    A_       ,   return_tensors=A_       ,   max_patches=A_       ,   **A_ )
				else:
						# add pixel_values and bbox
						UpperCamelCase						=  self.image_processor(
						    A_       ,   return_tensors=A_       ,   max_patches=A_       ,   header_text=A_       ,   **A_ )

				if text is not None and not self.image_processor.is_vqa:
						UpperCamelCase						=  self.tokenizer(
						    text=A_       ,   add_special_tokens=A_       ,   padding=A_       ,   truncation=A_       ,   max_length=A_       ,   stride=A_       ,   pad_to_multiple_of=A_       ,   return_attention_mask=A_       ,   return_overflowing_tokens=A_       ,   return_special_tokens_mask=A_       ,   return_offsets_mapping=A_       ,   return_token_type_ids=A_       ,   return_length=A_       ,   verbose=A_       ,   return_tensors=A_       ,   **A_       ,   )

						if "attention_mask" in text_encoding:
								UpperCamelCase						=  text_encoding.pop('attention_mask' )
						if "input_ids" in text_encoding:
								UpperCamelCase						=  text_encoding.pop('input_ids' )
				else:
						UpperCamelCase						=  None

				if text_encoding is not None:
						encoding_image_processor.update(A_ )

				return encoding_image_processor



		def __UpperCamelCase				(				self       ,   *A_       ,   **A_ )						->      Any:
				"""simple docstring"""
				return self.tokenizer.batch_decode(*A_       ,   **A_ )



		def __UpperCamelCase				(				self       ,   *A_       ,   **A_ )						->      Dict:
				"""simple docstring"""
				return self.tokenizer.decode(*A_       ,   **A_ )



		@property
		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.tokenizer.model_input_names
				UpperCamelCase						=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_UpperCAmelCase      :				str          = "scheduler_config.json"


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Tuple			  =       1
		__lowercase : int			  =       2
		__lowercase : List[Any]			  =       3
		__lowercase : str			  =       4
		__lowercase : Optional[Any]			  =       5






@dataclass
class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : jnp.ndarray






class        lowercase      :
		__lowercase : Union[str, Any]			  =       SCHEDULER_CONFIG_NAME
		__lowercase : Dict			  =       ["dtype"]
		__lowercase : List[Any]			  =       []
		__lowercase : Dict			  =       True



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ = None       ,   A_ = None       ,   A_=False       ,   **A_       ,   )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  cls.load_config(
				    pretrained_model_name_or_path=A_       ,   subfolder=A_       ,   return_unused_kwargs=A_       ,   **A_       ,   )
				UpperCamelCase			,						UpperCamelCase						=  cls.from_config(A_       ,   return_unused_kwargs=A_       ,   **A_ )

				if hasattr(A_       ,   'create_state' ) and getattr(A_       ,   'has_state'       ,   A_ ):
						UpperCamelCase						=  scheduler.create_state()

				if return_unused_kwargs:
						return scheduler, state, unused_kwargs

				return scheduler, state



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = False       ,   **A_ )						->      str:
				"""simple docstring"""
				self.save_config(save_directory=A_       ,   push_to_hub=A_       ,   **A_ )



		@property
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				return self._get_compatibles()



		@classmethod
		def __UpperCamelCase				(				cls )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  list(set([cls.__name__] + cls._compatibles ) )
				UpperCamelCase						=  importlib.import_module(__name__.split('.' )[0] )
				UpperCamelCase						=  [
				    getattr(A_       ,   A_ ) for c in compatible_classes_str if hasattr(A_       ,   A_ )
				]
				return compatible_classes






def    A (      lowercase					, lowercase				)     ->						jnp.ndarray:
		'''simple docstring'''

		assert len(lowercase				) >= x.ndim
		return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowercase				) - x.ndim)				)					, lowercase				)


def    A (      lowercase					, lowercase=0.9_9_9					, lowercase=jnp.floataa				)     ->						jnp.ndarray:
		'''simple docstring'''

		def alpha_bar(lowercase				):
				return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

		UpperCamelCase						=  []
		for i in range(lowercase				):
				UpperCamelCase						=  i / num_diffusion_timesteps
				UpperCamelCase						=  (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar(lowercase				) / alpha_bar(lowercase				)					, lowercase				)				)
		return jnp.array(lowercase					, dtype=lowercase				)


@flax.struct.dataclass
class        lowercase      :
		__lowercase : jnp.ndarray
		__lowercase : jnp.ndarray
		__lowercase : jnp.ndarray



		@classmethod
		def __UpperCamelCase				(				cls       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  scheduler.config

				if config.trained_betas is not None:
						UpperCamelCase						=  jnp.asarray(config.trained_betas       ,   dtype=scheduler.dtype )
				elif config.beta_schedule == "linear":
						UpperCamelCase						=  jnp.linspace(config.beta_start       ,   config.beta_end       ,   config.num_train_timesteps       ,   dtype=scheduler.dtype )
				elif config.beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						UpperCamelCase						=  (
						    jnp.linspace(
						        config.beta_start**0.5       ,   config.beta_end**0.5       ,   config.num_train_timesteps       ,   dtype=scheduler.dtype )
						    ** 2
						)
				elif config.beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						UpperCamelCase						=  betas_for_alpha_bar(config.num_train_timesteps       ,   dtype=scheduler.dtype )
				else:
						raise NotImplementedError(
						    F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )

				UpperCamelCase						=  1.0 - betas

				UpperCamelCase						=  jnp.cumprod(A_       ,   axis=0 )

				return cls(
				    alphas=A_       ,   betas=A_       ,   alphas_cumprod=A_       ,   )






def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  state.alphas_cumprod

		UpperCamelCase						=  alphas_cumprod[timesteps] ** 0.5
		UpperCamelCase						=  sqrt_alpha_prod.flatten()
		UpperCamelCase						=  broadcast_to_shape_from_left(lowercase					, original_samples.shape				)

		UpperCamelCase						=  (1 - alphas_cumprod[timesteps]) ** 0.5
		UpperCamelCase						=  sqrt_one_minus_alpha_prod.flatten()
		UpperCamelCase						=  broadcast_to_shape_from_left(lowercase					, original_samples.shape				)

		return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						Dict:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  get_sqrt_alpha_prod(lowercase					, lowercase					, lowercase					, lowercase				)
		UpperCamelCase						=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
		return noisy_samples


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						int:
		'''simple docstring'''

		UpperCamelCase			,						UpperCamelCase						=  get_sqrt_alpha_prod(lowercase					, lowercase					, lowercase					, lowercase				)
		UpperCamelCase						=  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
		return velocity
