





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


__UpperCAmelCase	:       int    	=							logging.get_logger(__name__)

__UpperCAmelCase	:       List[str]    	=							{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase	:       Any    	=							{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

__UpperCAmelCase	:       Optional[int]    	=							{
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}







class    __snake_case	(							__lowerCamelCase      ):





							'''simple docstring'''







							lowerCAmelCase__						=					VOCAB_FILES_NAMES
							lowerCAmelCase__						=					PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__						=					["""input_ids""", """attention_mask"""]
							lowerCAmelCase__						=					GPTaTokenizer




							def __init__(  self			:   List[Any]							,    A			:   Dict=None							,    A			:   str=None							,    A			:   Optional[int]=None							,    A			:   List[str]="<|endoftext|>"							,    A			:   Union[str, Any]="<|endoftext|>"							,    A			:   Union[str, Any]="<|endoftext|>"							,    A			:   str=False							,    **A			:   Dict							,    ):
									super().__init__(
									    _a							,    _a							,    tokenizer_file=_a							,    unk_token=_a							,    bos_token=_a							,    eos_token=_a							,    add_prefix_space=_a							,    **_a							,    )

									__snake_case:						Dict			  =						kwargs.pop("""add_bos_token"""							,    _a		)

									__snake_case:						Union[str, Any]			  =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
									if pre_tok_state.get("""add_prefix_space"""							,    _a		) != add_prefix_space:
											__snake_case:						Dict			  =						getattr(_a							,    pre_tok_state.pop("""type"""		)		)
											__snake_case:						Tuple			  =						add_prefix_space
											__snake_case:						List[str]			  =						pre_tok_class(**_a		)

									__snake_case:						str			  =						add_prefix_space




							def 							UpperCAmelCase__			(  self			:   int							,    *A			:   Dict							,    **A			:   Tuple		):
									__snake_case:						str			  =						kwargs.get("""is_split_into_words"""							,    _a		)
									assert self.add_prefix_space or not is_split_into_words, (
									    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
									    "to use it with pretokenized inputs."
									)

									return super()._batch_encode_plus(*_a							,    **_a		)




							def 							UpperCAmelCase__			(  self			:   Tuple							,    *A			:   int							,    **A			:   Optional[int]		):
									__snake_case:						Union[str, Any]			  =						kwargs.get("""is_split_into_words"""							,    _a		)

									assert self.add_prefix_space or not is_split_into_words, (
									    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
									    "to use it with pretokenized inputs."
									)

									return super()._encode_plus(*_a							,    **_a		)




							def 							UpperCAmelCase__			(  self			:   List[str]							,    A			:   List[Any]							,    A			:   Optional[int] = None		):
									__snake_case:						List[Any]			  =						self._tokenizer.model.save(_a							,    name=_a		)
									return tuple(_a		)






							def 							UpperCAmelCase__			(  self			:   int							,    A			:   List[str]		):
									__snake_case:						List[str]			  =						[]
									for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(_a							,    add_special_tokens=_a		) + [self.eos_token_id]		)

									if len(_a		) > self.model_max_length:
											__snake_case:						str			  =						input_ids[-self.model_max_length :]
									return input_ids













import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       pass







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





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )




       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[int]			  =						ort.SessionOptions()
         __snake_case:						List[Any]			  =						False
         return options




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Any			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						List[str]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						int			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Any			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=10							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[Any]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Any			  =						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						Optional[Any]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
         __snake_case:						Optional[int]			  =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
         __snake_case:						Optional[int]			  =						LMSDiscreteScheduler.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    subfolder="""scheduler"""							,    revision="""onnx"""		)
         __snake_case:						List[Any]			  =						OnnxStableDiffusionInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""							,    revision="""onnx"""							,    scheduler=A							,    safety_checker=A							,    feature_extractor=A							,    provider=self.gpu_provider							,    sess_options=self.gpu_options							,    )
         pipe.set_progress_bar_config(disable=A		)

         __snake_case:						Optional[int]			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Dict			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=20							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[str]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Union[str, Any]			  =						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3






