'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 							lowercase						(						__lowerCamelCase ,				__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=StableDiffusionSAGPipeline
   lowerCamelCase_    					=TEXT_TO_IMAGE_PARAMS
   lowerCamelCase_    					=TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCamelCase_    					=TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCamelCase_    					=TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCamelCase_    					=False


   def __UpperCAmelCase       (   self				:	Any)      ->					int:
         torch.manual_seed(0)
         lowercase_     =      UNetaDConditionModel(
             block_out_channels=(32, 64)   ,		layers_per_block=2   ,		sample_size=32   ,		in_channels=4   ,		out_channels=4   ,		down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,		up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")   ,		cross_attention_dim=32   ,		)
         lowercase_     =      DDIMScheduler(
             beta_start=0.0_0085   ,		beta_end=0.012   ,		beta_schedule="scaled_linear"   ,		clip_sample=__lowerCAmelCase   ,		set_alpha_to_one=__lowerCAmelCase   ,		)
         torch.manual_seed(0)
         lowercase_     =      AutoencoderKL(
             block_out_channels=[32, 64]   ,		in_channels=3   ,		out_channels=3   ,		down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,		up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,		latent_channels=4   ,		)
         torch.manual_seed(0)
         lowercase_     =      CLIPTextConfig(
             bos_token_id=0   ,		eos_token_id=2   ,		hidden_size=32   ,		intermediate_size=37   ,		layer_norm_eps=1e-05   ,		num_attention_heads=4   ,		num_hidden_layers=5   ,		pad_token_id=1   ,		vocab_size=1000   ,		)
         lowercase_     =      CLIPTextModel(__lowerCAmelCase)
         lowercase_     =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

         lowercase_     =      {
             "unet": unet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "safety_checker": None,
             "feature_extractor": None,
         }
         return components


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	int=0)      ->					str:
         if str(__lowerCAmelCase).startswith("mps"):
               lowercase_     =      torch.manual_seed(__lowerCAmelCase)
         else:
               lowercase_     =      torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
         lowercase_     =      {
             "prompt": ".",
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 1.0,
             "sag_scale": 1.0,
             "output_type": "numpy",
         }
         return inputs


   def __UpperCAmelCase       (   self				:	List[str])      ->					List[str]:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3)





@slow
@require_torch_gpu
class 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	int)      ->					Dict:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()


   def __UpperCAmelCase       (   self				:	Any)      ->					int:
         lowercase_     =      StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
         lowercase_     =      sag_pipe.to(__lowerCAmelCase)
         sag_pipe.set_progress_bar_config(disable=__lowerCAmelCase)

         lowercase_     =      "."
         lowercase_     =      torch.manual_seed(0)
         lowercase_     =      sag_pipe(
             [prompt]   ,		generator=__lowerCAmelCase   ,		guidance_scale=7.5   ,		sag_scale=1.0   ,		num_inference_steps=20   ,		output_type="np")

         lowercase_     =      output.images

         lowercase_     =      image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         lowercase_     =      np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949])

         assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2


   def __UpperCAmelCase       (   self				:	List[str])      ->					Optional[Any]:
         lowercase_     =      StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
         lowercase_     =      sag_pipe.to(__lowerCAmelCase)
         sag_pipe.set_progress_bar_config(disable=__lowerCAmelCase)

         lowercase_     =      "."
         lowercase_     =      torch.manual_seed(0)
         lowercase_     =      sag_pipe(
             [prompt]   ,		generator=__lowerCAmelCase   ,		guidance_scale=7.5   ,		sag_scale=1.0   ,		num_inference_steps=20   ,		output_type="np")

         lowercase_     =      output.images

         lowercase_     =      image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         lowercase_     =      np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371])

         assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2


   def __UpperCAmelCase       (   self				:	Dict)      ->					Optional[Any]:
         lowercase_     =      StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
         lowercase_     =      sag_pipe.to(__lowerCAmelCase)
         sag_pipe.set_progress_bar_config(disable=__lowerCAmelCase)

         lowercase_     =      "."
         lowercase_     =      torch.manual_seed(0)
         lowercase_     =      sag_pipe(
             [prompt]   ,		width=768   ,		height=512   ,		generator=__lowerCAmelCase   ,		guidance_scale=7.5   ,		sag_scale=1.0   ,		num_inference_steps=20   ,		output_type="np"   ,		)

         lowercase_     =      output.images

         assert image.shape == (1, 512, 768, 3)







'''simple docstring'''

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


lowerCAmelCase_ : Optional[Any]						  =      logging.get_logger(__name__)

lowerCAmelCase_ : Any						  =      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_ : Tuple						  =      {
    "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",
    },
}

lowerCAmelCase_ : Dict						  =      {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=['input_ids', 'attention_mask']
   lowerCamelCase_    					=GPTaTokenizer


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

         lowercase_     =      kwargs.pop("add_bos_token"   ,		__lowerCAmelCase)

         lowercase_     =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get("add_prefix_space"   ,		__lowerCAmelCase) != add_prefix_space:
               lowercase_     =      getattr(__lowerCAmelCase   ,		pre_tok_state.pop("type"))
               lowercase_     =      add_prefix_space
               lowercase_     =      pre_tok_class(**__lowerCAmelCase)

         lowercase_     =      add_prefix_space


   def __UpperCAmelCase       (   self				:	List[str]   ,		*__lowerCAmelCase				:	List[str]   ,		**__lowerCAmelCase				:	str)      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)
         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(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		*__lowerCAmelCase				:	Union[str, Any]   ,		**__lowerCAmelCase				:	Union[str, Any])      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)

         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(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[str] = None)      ->					Tuple[str]:
         lowercase_     =      self._tokenizer.model.save(__lowerCAmelCase   ,		name=__lowerCAmelCase)
         return tuple(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	"Conversation")      ->					List[int]:
         lowercase_     =      []
         for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase) + [self.eos_token_id])

         if len(__lowerCAmelCase) > self.model_max_length:
               lowercase_     =      input_ids[-self.model_max_length :]
         return input_ids






