"""simple docstring"""





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class  _snake_case     (					a__		):
						snake_case__			  =			42
						snake_case__			  =			42

class  _snake_case     (					a__				, a__		):
						snake_case__			  =			1






						@register_to_config
						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			int = 2000							,					UpperCAmelCase      :			float = 0.1_5							,					UpperCAmelCase      :			float = 0.0_1							,					UpperCAmelCase      :			float = 1_3_4_8.0							,					UpperCAmelCase      :			float = 1E-5							,					UpperCAmelCase      :			int = 1							,					):
													# standard deviation of the initial noise distribution
													__lowerCamelCase					:		List[str]		   =     sigma_max

													# setable values
													__lowerCamelCase					:		Optional[int]		   =     None

													self.set_sigmas(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			Optional[int] = None    ):
													return sample






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			float = None							,					UpperCAmelCase      :			Union[str, torch.device] = None    ):
													__lowerCamelCase					:		Optional[int]		   =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

													__lowerCamelCase					:		List[str]		   =     torch.linspace(1							,					UpperCAmelCase							,					UpperCAmelCase							,					device=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			float = None							,					UpperCAmelCase      :			float = None							,					UpperCAmelCase      :			float = None    ):
													__lowerCamelCase					:		Dict		   =     sigma_min if sigma_min is not None else self.config.sigma_min
													__lowerCamelCase					:		Any		   =     sigma_max if sigma_max is not None else self.config.sigma_max
													__lowerCamelCase					:		Dict		   =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
													if self.timesteps is None:
																				self.set_timesteps(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
													__lowerCamelCase					:		Tuple		   =     torch.exp(torch.linspace(math.log(UpperCAmelCase    )							,					math.log(UpperCAmelCase    )							,					UpperCAmelCase    )    )
													__lowerCamelCase					:		Optional[Any]		   =     torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Optional[int]    ):
													return torch.where(
													    timesteps == 0							,					torch.zeros_like(t.to(timesteps.device    )    )							,					self.discrete_sigmas[timesteps - 1].to(timesteps.device    )							,					)






						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			Optional[torch.Generator] = None							,					UpperCAmelCase      :			bool = True							,					):
													if self.timesteps is None:
																				raise ValueError(
																				    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

													__lowerCamelCase					:		str		   =     timestep * torch.ones(
													    sample.shape[0]							,					device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
													__lowerCamelCase					:		Union[str, Any]		   =     (timestep * (len(self.timesteps    ) - 1)).long()

													# mps requires indices to be in the same device, so we use cpu as is the default with cuda
													__lowerCamelCase					:		List[Any]		   =     timesteps.to(self.discrete_sigmas.device    )

													__lowerCamelCase					:		Dict		   =     self.discrete_sigmas[timesteps].to(sample.device    )
													__lowerCamelCase					:		Tuple		   =     self.get_adjacent_sigma(UpperCAmelCase							,					UpperCAmelCase    ).to(sample.device    )
													__lowerCamelCase					:		Optional[int]		   =     torch.zeros_like(UpperCAmelCase    )
													__lowerCamelCase					:		Union[str, Any]		   =     (sigma**2 - adjacent_sigma**2) ** 0.5

													# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
													# also equation 47 shows the analog from SDE models to ancestral sampling methods
													__lowerCamelCase					:		List[str]		   =     diffusion.flatten()
													while len(diffusion.shape    ) < len(sample.shape    ):
																				__lowerCamelCase					:		Union[str, Any]		   =     diffusion.unsqueeze(-1    )
													__lowerCamelCase					:		int		   =     drift - diffusion**2 * model_output

													#  equation 6: sample noise for the diffusion term of
													__lowerCamelCase					:		str		   =     randn_tensor(
													    sample.shape							,					layout=sample.layout							,					generator=UpperCAmelCase							,					device=sample.device							,					dtype=sample.dtype    )
													__lowerCamelCase					:		Tuple		   =     sample - drift  # subtract because `dt` is a small negative timestep
													# TODO is the variable diffusion the correct scaling term for the noise?
													__lowerCamelCase					:		Tuple		   =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

													if not return_dict:
																				return (prev_sample, prev_sample_mean)

													return SdeVeOutput(prev_sample=UpperCAmelCase							,					prev_sample_mean=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			Optional[torch.Generator] = None							,					UpperCAmelCase      :			bool = True							,					):
													if self.timesteps is None:
																				raise ValueError(
																				    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

													# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
													# sample noise for correction
													__lowerCamelCase					:		List[Any]		   =     randn_tensor(sample.shape							,					layout=sample.layout							,					generator=UpperCAmelCase    ).to(sample.device    )

													# compute step size from the model_output, the noise, and the snr
													__lowerCamelCase					:		str		   =     torch.norm(model_output.reshape(model_output.shape[0]							,					-1    )							,					dim=-1    ).mean()
													__lowerCamelCase					:		int		   =     torch.norm(noise.reshape(noise.shape[0]							,					-1    )							,					dim=-1    ).mean()
													__lowerCamelCase					:		Tuple		   =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
													__lowerCamelCase					:		List[str]		   =     step_size * torch.ones(sample.shape[0]    ).to(sample.device    )
													# self.repeat_scalar(step_size, sample.shape[0])

													# compute corrected sample: model_output term and noise term
													__lowerCamelCase					:		List[str]		   =     step_size.flatten()
													while len(step_size.shape    ) < len(sample.shape    ):
																				__lowerCamelCase					:		str		   =     step_size.unsqueeze(-1    )
													__lowerCamelCase					:		List[str]		   =     sample + step_size * model_output
													__lowerCamelCase					:		int		   =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

													if not return_dict:
																				return (prev_sample,)

													return SchedulerOutput(prev_sample=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			torch.FloatTensor							,					UpperCAmelCase      :			torch.FloatTensor							,					):
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													__lowerCamelCase					:		Any		   =     timesteps.to(original_samples.device    )
													__lowerCamelCase					:		Optional[int]		   =     self.discrete_sigmas.to(original_samples.device    )[timesteps]
													__lowerCamelCase					:		Any		   =     (
													    noise * sigmas[:, None, None, None]
													    if noise is not None
													    else torch.randn_like(UpperCAmelCase    ) * sigmas[:, None, None, None]
													)
													__lowerCamelCase					:		Tuple		   =     noise + original_samples
													return noisy_samples







						def __len__(       self      :			List[str]    ):
													return self.config.num_train_timesteps
"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
				import torch


if is_vision_available():
				from PIL import Image

				from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class  _snake_case     (					unittest.TestCase		):
						snake_case__			  =			ViTImageProcessor if is_vision_available() else None






						@property
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		str		   =     (3, 32, 128)
													__lowerCamelCase					:		Optional[int]		   =     tempfile.mkdtemp()

													# fmt: off
													__lowerCamelCase					:		Tuple		   =     ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
													# fmt: on
													__lowerCamelCase					:		Dict		   =     dict(zip(UpperCAmelCase							,					range(len(UpperCAmelCase    )    )    )    )

													__lowerCamelCase					:		List[Any]		   =     os.path.join(self.tmpdirname							,					VOCAB_FILES_NAMES["vocab_file"]    )
													with open(self.vocab_file							,					"w"							,					encoding="utf-8"    ) as fp:
																				fp.write(json.dumps(UpperCAmelCase    ) + "\n"    )

													__lowerCamelCase					:		int		   =     {
													    "do_normalize": False,
													    "do_resize": True,
													    "image_processor_type": "ViTImageProcessor",
													    "resample": 3,
													    "size": {"height": 32, "width": 128},
													}
													__lowerCamelCase					:		Dict		   =     os.path.join(self.tmpdirname							,					UpperCAmelCase    )
													with open(self.image_processor_file							,					"w"							,					encoding="utf-8"    ) as fp:
																				json.dump(UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple							,					**UpperCAmelCase      :			Optional[int]    ):
													return MgpstrTokenizer.from_pretrained(self.tmpdirname							,					**UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					**UpperCAmelCase      :			List[str]    ):
													return ViTImageProcessor.from_pretrained(self.tmpdirname							,					**UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple    ):
													shutil.rmtree(self.tmpdirname    )






						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		Any		   =     np.random.randint(255							,					size=(3, 30, 400)							,					dtype=np.uinta    )

													__lowerCamelCase					:		Union[str, Any]		   =     Image.fromarray(np.moveaxis(UpperCAmelCase							,					0							,					-1    )    )

													return image_input






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.get_tokenizer()
													__lowerCamelCase					:		Any		   =     self.get_image_processor()

													__lowerCamelCase					:		List[Any]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )
													processor.save_pretrained(self.tmpdirname    )
													__lowerCamelCase					:		str		   =     MgpstrProcessor.from_pretrained(self.tmpdirname							,					use_fast=UpperCAmelCase    )

													self.assertEqual(processor.char_tokenizer.get_vocab()							,					tokenizer.get_vocab()    )
													self.assertIsInstance(processor.char_tokenizer							,					UpperCAmelCase    )

													self.assertEqual(processor.image_processor.to_json_string()							,					image_processor.to_json_string()    )
													self.assertIsInstance(processor.image_processor							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Any		   =     self.get_tokenizer()
													__lowerCamelCase					:		Dict		   =     self.get_image_processor()

													__lowerCamelCase					:		Optional[int]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )
													processor.save_pretrained(self.tmpdirname    )

													__lowerCamelCase					:		Dict		   =     self.get_tokenizer(bos_token="(BOS)"							,					eos_token="(EOS)"    )
													__lowerCamelCase					:		Any		   =     self.get_image_processor(do_normalize=UpperCAmelCase							,					padding_value=1.0    )

													__lowerCamelCase					:		List[str]		   =     MgpstrProcessor.from_pretrained(
													    self.tmpdirname							,					bos_token="(BOS)"							,					eos_token="(EOS)"							,					do_normalize=UpperCAmelCase							,					padding_value=1.0    )

													self.assertEqual(processor.char_tokenizer.get_vocab()							,					tokenizer_add_kwargs.get_vocab()    )
													self.assertIsInstance(processor.char_tokenizer							,					UpperCAmelCase    )

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






						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		List[str]		   =     self.get_image_processor()
													__lowerCamelCase					:		Dict		   =     self.get_tokenizer()

													__lowerCamelCase					:		Optional[int]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[int]		   =     self.prepare_image_inputs()

													__lowerCamelCase					:		Optional[int]		   =     image_processor(UpperCAmelCase							,					return_tensors="np"    )
													__lowerCamelCase					:		Tuple		   =     processor(images=UpperCAmelCase							,					return_tensors="np"    )

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






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

													__lowerCamelCase					:		int		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     "test"

													__lowerCamelCase					:		List[Any]		   =     processor(text=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[int]		   =     tokenizer(UpperCAmelCase    )
													for key in encoded_tok.keys():
																				self.assertListEqual(encoded_tok[key]							,					encoded_processor[key]    )






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.get_image_processor()
													__lowerCamelCase					:		Optional[Any]		   =     self.get_tokenizer()

													__lowerCamelCase					:		Optional[int]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     "test"
													__lowerCamelCase					:		Union[str, Any]		   =     self.prepare_image_inputs()

													__lowerCamelCase					:		Optional[Any]		   =     processor(text=UpperCAmelCase							,					images=UpperCAmelCase    )

													self.assertListEqual(list(inputs.keys()    )							,					["pixel_values", "labels"]    )

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






						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		Any		   =     self.get_image_processor()
													__lowerCamelCase					:		Optional[Any]		   =     self.get_tokenizer()

													__lowerCamelCase					:		Dict		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

													__lowerCamelCase					:		List[Any]		   =     processor.char_decode(UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     tokenizer.batch_decode(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     [seq.replace(" "							,					""    ) for seq in decoded_tok]

													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		str		   =     self.get_image_processor()
													__lowerCamelCase					:		str		   =     self.get_tokenizer()

													__lowerCamelCase					:		Union[str, Any]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Tuple		   =     None
													__lowerCamelCase					:		int		   =     self.prepare_image_inputs()

													__lowerCamelCase					:		List[str]		   =     processor(text=UpperCAmelCase							,					images=UpperCAmelCase    )

													self.assertListEqual(list(inputs.keys()    )							,					processor.model_input_names    )







						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		Optional[Any]		   =     self.get_image_processor()
													__lowerCamelCase					:		Tuple		   =     self.get_tokenizer()

													__lowerCamelCase					:		List[Any]		   =     MgpstrProcessor(tokenizer=UpperCAmelCase							,					image_processor=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[int]		   =     torch.randn(1							,					27							,					38    )
													__lowerCamelCase					:		List[str]		   =     torch.randn(1							,					27							,					50257    )
													__lowerCamelCase					:		Union[str, Any]		   =     torch.randn(1							,					27							,					30522    )

													__lowerCamelCase					:		str		   =     processor.batch_decode([char_input, bpe_input, wp_input]    )

													self.assertListEqual(list(results.keys()    )							,					["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]    )