'''simple docstring'''

import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
  import onnxruntime as ort



class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   "ssube/stable-diffusion-x4-upscaler-onnx"

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase=0			)  ->  List[Any]:
        lowercase__    :   Dict						= floats_tensor((1, 3, 128, 128)     ,       rng=random.Random(__lowerCAmelCase			)			)
        lowercase__    :   Union[str, Any]						= torch.manual_seed(__lowerCAmelCase			)
        lowercase__    :   List[str]						= {
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''image''': image,
            '''generator''': generator,
            '''num_inference_steps''': 3,
            '''guidance_scale''': 7.5,
            '''output_type''': '''numpy''',
        }
        return inputs

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   Optional[int]						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   str						= self.get_dummy_inputs()
        lowercase__    :   int						= pipe(**__lowerCAmelCase			).images
        lowercase__    :   List[str]						= image[0, -3:, -3:, -1].flatten()

        # started as 128, should now be 512
        assert image.shape == (1, 512, 512, 3)
        lowercase__    :   Any						= np.array(
            [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]			)
        assert np.abs(image_slice - expected_slice			).max() < 1E-1

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Any						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''			)
        lowercase__    :   Optional[int]						= PNDMScheduler.from_config(pipe.scheduler.config     ,       skip_prk_steps=__lowerCAmelCase			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   List[Any]						= self.get_dummy_inputs()
        lowercase__    :   Union[str, Any]						= pipe(**__lowerCAmelCase			).images
        lowercase__    :   Optional[int]						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase__    :   Tuple						= np.array(
            [0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4]			)
        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-1

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Any						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''			)
        lowercase__    :   int						= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   int						= self.get_dummy_inputs()
        lowercase__    :   Optional[int]						= pipe(**__lowerCAmelCase			).images
        lowercase__    :   List[str]						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase__    :   Tuple						= np.array(
            [0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-1

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Dict						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''			)
        lowercase__    :   List[str]						= EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   str						= self.get_dummy_inputs()
        lowercase__    :   str						= pipe(**__lowerCAmelCase			).images
        lowercase__    :   int						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase__    :   Optional[Any]						= np.array(
            [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]			)
        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-1


       def _lowerCAmelCase(						self			)  ->  str:
        lowercase__    :   Tuple						= OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''			)
        lowercase__    :   List[str]						= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Union[str, Any]						= self.get_dummy_inputs()
        lowercase__    :   Any						= pipe(**__lowerCAmelCase			).images
        lowercase__    :   List[Any]						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase__    :   Any						= np.array(
            [0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-1



@nightly
@require_onnxruntime
@require_torch_gpu
class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       @property
       def _lowerCAmelCase(						self			)  ->  str:
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )

       @property
       def _lowerCAmelCase(						self			)  ->  str:
        lowercase__    :   Tuple						= ort.SessionOptions()
        lowercase__    :   int						= False
        return options

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   str						= load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/img2img/sketch-mountains-input.jpg'''			)
        lowercase__    :   List[Any]						= init_image.resize((128, 128)			)
        # using the PNDM scheduler by default
        lowercase__    :   List[Any]						= OnnxStableDiffusionUpscalePipeline.from_pretrained(
            '''ssube/stable-diffusion-x4-upscaler-onnx'''     ,       provider=self.gpu_provider     ,       sess_options=self.gpu_options     ,       )
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   List[str]						= '''A fantasy landscape, trending on artstation'''

        lowercase__    :   List[Any]						= torch.manual_seed(0			)
        lowercase__    :   List[Any]						= pipe(
            prompt=__lowerCAmelCase     ,       image=__lowerCAmelCase     ,       guidance_scale=7.5     ,       num_inference_steps=10     ,       generator=__lowerCAmelCase     ,       output_type='''np'''     ,       )
        lowercase__    :   int						= output.images
        lowercase__    :   Dict						= images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 512, 3)
        lowercase__    :   Optional[int]						= np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2]			)
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 2E-2


       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   str						= load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/img2img/sketch-mountains-input.jpg'''			)
        lowercase__    :   Optional[int]						= init_image.resize((128, 128)			)
        lowercase__    :   str						= LMSDiscreteScheduler.from_pretrained(
            '''ssube/stable-diffusion-x4-upscaler-onnx'''     ,       subfolder='''scheduler'''			)
        lowercase__    :   str						= OnnxStableDiffusionUpscalePipeline.from_pretrained(
            '''ssube/stable-diffusion-x4-upscaler-onnx'''     ,       scheduler=__lowerCAmelCase     ,       provider=self.gpu_provider     ,       sess_options=self.gpu_options     ,       )
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Tuple						= '''A fantasy landscape, trending on artstation'''

        lowercase__    :   Dict						= torch.manual_seed(0			)
        lowercase__    :   int						= pipe(
            prompt=__lowerCAmelCase     ,       image=__lowerCAmelCase     ,       guidance_scale=7.5     ,       num_inference_steps=20     ,       generator=__lowerCAmelCase     ,       output_type='''np'''     ,       )
        lowercase__    :   Optional[int]						= output.images
        lowercase__    :   List[Any]						= images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 512, 3)
        lowercase__    :   List[str]						= np.array(
            [0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6]			)
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 2E-2


'''simple docstring'''

import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a:			List[str]	       =       logging.get_logger(__name__)

__a:			int	       =       """▁"""

__a:			Optional[int]	       =       {"""vocab_file""": """prophetnet.tokenizer"""}

__a:			Optional[int]	       =       {
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

__a:			List[str]	       =       {
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

__a:			Tuple	       =       {
    """microsoft/xprophetnet-large-wiki100-cased""": 5_12,
}
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Dict						= collections.OrderedDict()
 with open(UpperCAmelCase		,					'''r'''		,					encoding='''utf-8'''					) as reader:
  lowercase__    :   List[Any]						= reader.readlines()
 for index, token in enumerate(UpperCAmelCase					):
  lowercase__    :   List[Any]						= token.rstrip('''\n'''					)
  lowercase__    :   Tuple						= index
 return vocab





class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE 		=   PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE 		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE 		=   ["input_ids", "attention_mask"]

       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[UNK]"     ,       __lowerCAmelCase="[PAD]"     ,       __lowerCAmelCase="[CLS]"     ,       __lowerCAmelCase="[MASK]"     ,       __lowerCAmelCase = None     ,       **__lowerCAmelCase     ,       )  ->  None:
        lowercase__    :   Dict						= {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__lowerCAmelCase     ,       eos_token=__lowerCAmelCase     ,       sep_token=__lowerCAmelCase     ,       unk_token=__lowerCAmelCase     ,       pad_token=__lowerCAmelCase     ,       cls_token=__lowerCAmelCase     ,       mask_token=__lowerCAmelCase     ,       sp_model_kwargs=self.sp_model_kwargs     ,       **__lowerCAmelCase     ,       )

        try:
         import sentencepiece as spm
        except ImportError:
         logger.warning(
             '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
             ''' pip install sentencepiece'''			)
         raise

        lowercase__    :   str						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(str(__lowerCAmelCase			)			)
        lowercase__    :   Optional[int]						= vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # put special tokens and [unused] tokens into the vocab
        lowercase__    :   str						= {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

        for i in range(10			):
         lowercase__    :   Tuple						= F"""[unused{i}]"""
         lowercase__    :   List[Any]						= 5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        lowercase__    :   Optional[Any]						= 12
        lowercase__    :   Tuple						= {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
         self.unique_no_split_tokens.append(__lowerCAmelCase			)

       def __getstate__(						self			)  ->  Union[str, Any]:
        lowercase__    :   Dict						= self.__dict__.copy()
        lowercase__    :   Optional[Any]						= None
        return state

       def __setstate__(						self     ,       __lowerCAmelCase			)  ->  Dict:
        lowercase__    :   Any						= d
        try:
         import sentencepiece as spm
        except ImportError:
         logger.warning(
             '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
             ''' pip install sentencepiece'''			)
         raise

        # for backward compatibility
        if not hasattr(self     ,       '''sp_model_kwargs'''			):
         lowercase__    :   List[Any]						= {}

        lowercase__    :   int						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(self.vocab_file			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = False			)  ->  List[int]:

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

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  List[int]:
        lowercase__    :   List[str]						= [self.sep_token_id]

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

       @property
       def _lowerCAmelCase(						self			)  ->  List[Any]:
        return len(self.sp_model			) + self.fairseq_offset

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   str						= {self.convert_ids_to_tokens(__lowerCAmelCase			): i for i in range(self.vocab_size			)}
        vocab.update(self.added_tokens_encoder			)
        return vocab

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        return self.sp_model.encode(__lowerCAmelCase     ,       out_type=__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
        lowercase__    :   Tuple						= self.sp_model.PieceToId(__lowerCAmelCase			)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Union[str, Any]:
        lowercase__    :   Optional[Any]						= ''''''.join(__lowerCAmelCase			).replace(__lowerCAmelCase     ,       ''' '''			).strip()
        return out_string

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Tuple[str]:
        if not os.path.isdir(__lowerCAmelCase			):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
         return
        lowercase__    :   str						= os.path.join(
            __lowerCAmelCase     ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

        if os.path.abspath(self.vocab_file			) != os.path.abspath(__lowerCAmelCase			) and os.path.isfile(self.vocab_file			):
         copyfile(self.vocab_file     ,       __lowerCAmelCase			)
        elif not os.path.isfile(self.vocab_file			):
         with open(__lowerCAmelCase     ,       '''wb'''			) as fi:
          lowercase__    :   List[Any]						= self.sp_model.serialized_model_proto()
          fi.write(__lowerCAmelCase			)

        return (out_vocab_file,)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  List[int]:

        if token_ids_a is None:
         return token_ids_a + [self.sep_token_id]
        lowercase__    :   str						= [self.sep_token_id]
        return token_ids_a + sep + token_ids_a + sep

