

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
			from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
			UpperCAmelCase_       :				int		   =      False

if is_vision_available():
			from PIL import Image

			from transformers import (
			    AutoProcessor,
			    PixaStructImageProcessor,
			    PixaStructProcessor,
			    PreTrainedTokenizerFast,
			    TaTokenizer,
			)






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11	,			reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''	,			)
@require_vision
@require_torch
class     lowerCamelCase_			(  unittest.TestCase  ):




	def 					lowerCAmelCase_					(    self	:    int       ):
		__A   : Tuple								= tempfile.mkdtemp()

		__A   : Optional[int]								= PixaStructImageProcessor()
		__A   : Dict								= TaTokenizer.from_pretrained("""t5-small"""       )

		__A   : int								= PixaStructProcessor(__A				,			__A       )

		processor.save_pretrained(self.tmpdirname       )




	def 					lowerCAmelCase_					(    self	:    int				,			**__A	:    Optional[int]       ):
		return AutoProcessor.from_pretrained(self.tmpdirname				,			**__A       ).tokenizer




	def 					lowerCAmelCase_					(    self	:    List[Any]				,			**__A	:    Tuple       ):
		return AutoProcessor.from_pretrained(self.tmpdirname				,			**__A       ).image_processor




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
		shutil.rmtree(self.tmpdirname       )




	def 					lowerCAmelCase_					(    self	:    str       ):
		__A   : Optional[int]								= [np.random.randint(255				,			size=(3, 30, 400)				,			dtype=np.uinta       )]

		__A   : List[Any]								= [Image.fromarray(np.moveaxis(__A				,			0				,			-1       )       ) for x in image_inputs]

		return image_inputs




	def 					lowerCAmelCase_					(    self	:    List[Any]       ):
		__A   : str								= PixaStructProcessor(tokenizer=self.get_tokenizer()				,			image_processor=self.get_image_processor()       )
		processor.save_pretrained(self.tmpdirname       )

		__A   : List[Any]								= self.get_tokenizer(bos_token="""(BOS)"""				,			eos_token="""(EOS)"""       )
		__A   : Optional[int]								= self.get_image_processor(do_normalize=__A				,			padding_value=1.0       )

		__A   : str								= PixaStructProcessor.from_pretrained(
		    self.tmpdirname				,			bos_token="""(BOS)"""				,			eos_token="""(EOS)"""				,			do_normalize=__A				,			padding_value=1.0       )

		self.assertEqual(processor.tokenizer.get_vocab()				,			tokenizer_add_kwargs.get_vocab()       )
		self.assertIsInstance(processor.tokenizer				,			__A       )

		self.assertEqual(processor.image_processor.to_json_string()				,			image_processor_add_kwargs.to_json_string()       )
		self.assertIsInstance(processor.image_processor				,			__A       )




	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
		__A   : Union[str, Any]								= self.get_image_processor()
		__A   : Optional[int]								= self.get_tokenizer()

		__A   : Optional[int]								= PixaStructProcessor(tokenizer=__A				,			image_processor=__A       )

		__A   : Optional[Any]								= self.prepare_image_inputs()

		__A   : str								= image_processor(__A				,			return_tensors="""np"""       )
		__A   : Optional[Any]								= processor(images=__A				,			return_tensors="""np"""       )

		for key in input_feat_extract.keys():
			self.assertAlmostEqual(input_feat_extract[key].sum()				,			input_processor[key].sum()				,			delta=1e-2       )




	def 					lowerCAmelCase_					(    self	:    List[str]       ):
		__A   : Optional[Any]								= self.get_image_processor()
		__A   : Union[str, Any]								= self.get_tokenizer()

		__A   : List[str]								= PixaStructProcessor(tokenizer=__A				,			image_processor=__A       )

		__A   : List[Any]								= """lower newer"""

		__A   : List[str]								= processor(text=__A       )

		__A   : List[Any]								= tokenizer(__A				,			return_token_type_ids=__A				,			add_special_tokens=__A       )

		for key in encoded_tok.keys():
			self.assertListEqual(encoded_tok[key]				,			encoded_processor[key]       )




	def 					lowerCAmelCase_					(    self	:    List[str]       ):
		__A   : Any								= self.get_image_processor()
		__A   : Dict								= self.get_tokenizer()

		__A   : Any								= PixaStructProcessor(tokenizer=__A				,			image_processor=__A       )

		__A   : List[str]								= """lower newer"""
		__A   : int								= self.prepare_image_inputs()

		__A   : Union[str, Any]								= processor(text=__A				,			images=__A       )

		self.assertListEqual(
		    list(inputs.keys()       )				,			["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]       )

		# test if it raises when no input is passed
		with pytest.raises(__A       ):
			processor()




	def 					lowerCAmelCase_					(    self	:    Optional[Any]       ):
		__A   : int								= self.get_image_processor()
		__A   : Tuple								= self.get_tokenizer()

		__A   : List[str]								= PixaStructProcessor(tokenizer=__A				,			image_processor=__A       )

		__A   : str								= """lower newer"""
		__A   : Optional[Any]								= self.prepare_image_inputs()

		__A   : int								= processor(text=__A				,			images=__A       )

		__A   : Any								= [512, 1024, 2048, 4096]
		__A   : List[Any]								= [770, 770, 770, 770]
		# with text
		for i, max_patch in enumerate(__A       ):
			__A   : Union[str, Any]								= processor(text=__A				,			images=__A				,			max_patches=__A       )
			self.assertEqual(inputs["""flattened_patches"""][0].shape[0]				,			__A       )
			self.assertEqual(inputs["""flattened_patches"""][0].shape[1]				,			expected_hidden_size[i]       )

		# without text input
		for i, max_patch in enumerate(__A       ):
			__A   : Union[str, Any]								= processor(images=__A				,			max_patches=__A       )
			self.assertEqual(inputs["""flattened_patches"""][0].shape[0]				,			__A       )
			self.assertEqual(inputs["""flattened_patches"""][0].shape[1]				,			expected_hidden_size[i]       )




	def 					lowerCAmelCase_					(    self	:    Optional[Any]       ):
		__A   : Tuple								= self.get_image_processor()
		__A   : List[Any]								= self.get_tokenizer()

		__A   : Any								= PixaStructProcessor(tokenizer=__A				,			image_processor=__A       )

		__A   : Union[str, Any]								= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		__A   : List[str]								= processor.batch_decode(__A       )
		__A   : str								= tokenizer.batch_decode(__A       )

		self.assertListEqual(__A				,			__A       )




	def 					lowerCAmelCase_					(    self	:    int       ):
		__A   : Union[str, Any]								= self.get_image_processor()
		__A   : Union[str, Any]								= self.get_tokenizer()

		__A   : Dict								= PixaStructProcessor(tokenizer=__A				,			image_processor=__A       )

		__A   : int								= """lower newer"""
		__A   : List[str]								= self.prepare_image_inputs()

		__A   : str								= processor(text=__A				,			images=__A       )

		# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
		self.assertListEqual(
		    list(inputs.keys()       )				,			["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]       )

		__A   : str								= processor(text=__A       )

		# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
		self.assertListEqual(list(inputs.keys()       )				,			["""input_ids""", """attention_mask"""]       )








# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

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






@flax.struct.dataclass
class     lowerCamelCase_			:
	_lowercase		:      CommonSchedulerState
	_lowercase		:      jnp.ndarray

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




	@classmethod
	def 					lowerCAmelCase_					(    cls	:    int				,			__A	:    CommonSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return cls(
		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)






@dataclass
class     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      DDIMSchedulerState






class     lowerCamelCase_			(  _lowercase	,			_lowercase  ):
	_lowercase		:      Union[str, Any]    				=							[e.name for e in FlaxKarrasDiffusionSchedulers]

	_lowercase		:      jnp.dtype




	@property
	def 					lowerCAmelCase_					(    self	:    Tuple       ):
		return True




	@register_to_config
	def __init__(    self	:    Any				,			__A	:    int = 1000				,			__A	:    float = 0.0_0_0_1				,			__A	:    float = 0.0_2				,			__A	:    str = "linear"				,			__A	:    Optional[jnp.ndarray] = None				,			__A	:    bool = True				,			__A	:    int = 0				,			__A	:    str = "epsilon"				,			__A	:    jnp.dtype = jnp.floataa				,			):
		__A   : Optional[int]								= dtype




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    Optional[CommonSchedulerState] = None       ):
		if common is None:
			__A   : Any								= CommonSchedulerState.create(self       )

		# At every step in ddim, we are looking into the previous alphas_cumprod
		# For the final step, there is no previous alphas_cumprod because we are already at 0
		# `set_alpha_to_one` decides whether we set this parameter simply to one or
		# whether we use the final alpha of the "non-previous" one.
		__A   : Tuple								= (
		    jnp.array(1.0				,			dtype=self.dtype       ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
		)

		# standard deviation of the initial noise distribution
		__A   : List[str]								= jnp.array(1.0				,			dtype=self.dtype       )

		__A   : Any								= jnp.arange(0				,			self.config.num_train_timesteps       ).round()[::-1]

		return DDIMSchedulerState.create(
		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    Optional[int] = None       ):
		return sample




	def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    DDIMSchedulerState				,			__A	:    int				,			__A	:    Tuple = ()       ):
		__A   : Union[str, Any]								= self.config.num_train_timesteps // num_inference_steps
		# creates integer timesteps by multiplying by ratio
		# rounding to avoid issues when num_inference_step is power of 3
		__A   : Optional[Any]								= (jnp.arange(0				,			__A       ) * step_ratio).round()[::-1] + self.config.steps_offset

		return state.replace(
		    num_inference_steps=__A				,			timesteps=__A				,			)




	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    DDIMSchedulerState				,			__A	:    str				,			__A	:    List[Any]       ):
		__A   : Tuple								= state.common.alphas_cumprod[timestep]
		__A   : Tuple								= jnp.where(
		    prev_timestep >= 0				,			state.common.alphas_cumprod[prev_timestep]				,			state.final_alpha_cumprod       )
		__A   : List[Any]								= 1 - alpha_prod_t
		__A   : Tuple								= 1 - alpha_prod_t_prev

		__A   : List[Any]								= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

		return variance




	def 					lowerCAmelCase_					(    self	:    List[Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    int				,			__A	:    jnp.ndarray				,			__A	:    float = 0.0				,			__A	:    bool = True				,			):
		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"""       )

		# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
		# Ideally, read DDIM paper in-detail understanding

		# Notation (<variable name> -> <name in paper>
		# - pred_noise_t -> e_theta(x_t, t)
		# - pred_original_sample -> f_theta(x_t, t) or x_0
		# - std_dev_t -> sigma_t
		# - eta -> η
		# - pred_sample_direction -> "direction pointing to x_t"
		# - pred_prev_sample -> "x_t-1"

		# 1. get previous step value (=t-1)
		__A   : Optional[Any]								= timestep - self.config.num_train_timesteps // state.num_inference_steps

		__A   : Optional[int]								= state.common.alphas_cumprod
		__A   : Optional[int]								= state.final_alpha_cumprod

		# 2. compute alphas, betas
		__A   : Optional[int]								= alphas_cumprod[timestep]
		__A   : str								= jnp.where(prev_timestep >= 0				,			alphas_cumprod[prev_timestep]				,			__A       )

		__A   : Any								= 1 - alpha_prod_t

		# 3. compute predicted original sample from predicted noise also called
		# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		if self.config.prediction_type == "epsilon":
			__A   : Any								= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			__A   : List[Any]								= model_output
		elif self.config.prediction_type == "sample":
			__A   : Dict								= model_output
			__A   : Tuple								= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
		elif self.config.prediction_type == "v_prediction":
			__A   : int								= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
			__A   : str								= (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
		else:
			raise ValueError(
			    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
			    """ `v_prediction`"""       )

		# 4. compute variance: "sigma_t(η)" -> see formula (16)
		# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
		__A   : Union[str, Any]								= self._get_variance(__A				,			__A				,			__A       )
		__A   : Any								= eta * variance ** 0.5

		# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		__A   : Any								= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

		# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		__A   : Optional[int]								= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

		if not return_dict:
			return (prev_sample, state)

		return FlaxDDIMSchedulerOutput(prev_sample=__A				,			state=__A       )




	def 					lowerCAmelCase_					(    self	:    List[str]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return add_noise_common(state.common				,			__A				,			__A				,			__A       )




	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return get_velocity_common(state.common				,			__A				,			__A				,			__A       )




	def __len__(    self	:    Union[str, Any]       ):
		return self.config.num_train_timesteps





