


'''simple docstring'''

import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin





@skip_mps
class   snake_case					(					lowercase    ,		lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						IFPipeline
  _lowerCamelCase						=						TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
  _lowerCamelCase						=						TEXT_TO_IMAGE_BATCH_PARAMS
  _lowerCamelCase						=						PipelineTesterMixin.required_optional_params - {"latents"}






  def 						snake_case	(					self  ):







   """simple docstring"""
   return self._get_dummy_components()






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=0  ):







   """simple docstring"""
   if str(UpperCamelCase  ).startswith("mps"  ):
    lowerCamelCase_  				=						torch.manual_seed(UpperCamelCase  )
   else:
    lowerCamelCase_  				=						torch.Generator(device=UpperCamelCase  ).manual_seed(UpperCamelCase  )

   lowerCamelCase_  				=						{
       "prompt": "A painting of a squirrel eating a burger",
       "generator": generator,
       "num_inference_steps": 2,
       "output_type": "numpy",
   }

   return inputs






  def 						snake_case	(					self  ):







   """simple docstring"""
   self._test_save_load_optional_components()






  @unittest.skipIf(torch_device != "cuda"    ,						reason="float16 requires CUDA"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
   super().test_save_load_floataa(expected_max_diff=1e-1  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   self._test_attention_slicing_forward_pass(expected_max_diff=1e-2  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   self._test_save_load_local()






  def 						snake_case	(					self  ):







   """simple docstring"""
   self._test_inference_batch_single_identical(
       expected_max_diff=1e-2    ,						)







  @unittest.skipIf(
      torch_device != "cuda" or not is_xformers_available()    ,						reason="XFormers attention is only available with CUDA and `xformers` installed"    ,						)
  def 						snake_case	(					self  ):







   """simple docstring"""
   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )





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





  """simple docstring"""




  def 						snake_case	(					self  ):







   """simple docstring"""
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def 						snake_case	(					self  ):







   """simple docstring"""
   # if

   lowerCamelCase_  				=						IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"    ,						variant="fp16"    ,						torch_dtype=torch.floataa  )

   lowerCamelCase_  				=						IFSuperResolutionPipeline.from_pretrained(
       "DeepFloyd/IF-II-L-v1.0"    ,						variant="fp16"    ,						torch_dtype=torch.floataa    ,						text_encoder=UpperCamelCase    ,						tokenizer=UpperCamelCase  )

   # pre compute text embeddings and remove T5 to save memory

   pipe_a.text_encoder.to("cuda"  )

   lowerCamelCase_				,lowerCamelCase_  				=						pipe_a.encode_prompt("anime turtle"    ,						device="cuda"  )

   del pipe_a.tokenizer
   del pipe_a.text_encoder
   gc.collect()

   lowerCamelCase_  				=						None
   lowerCamelCase_  				=						None

   pipe_a.enable_model_cpu_offload()
   pipe_a.enable_model_cpu_offload()

   pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )
   pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )

   self._test_if(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  )

   pipe_a.remove_all_hooks()
   pipe_a.remove_all_hooks()

   # img2img

   lowerCamelCase_  				=						IFImgaImgPipeline(**pipe_a.components  )
   lowerCamelCase_  				=						IFImgaImgSuperResolutionPipeline(**pipe_a.components  )

   pipe_a.enable_model_cpu_offload()
   pipe_a.enable_model_cpu_offload()

   pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )
   pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )

   self._test_if_imgaimg(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  )

   pipe_a.remove_all_hooks()
   pipe_a.remove_all_hooks()

   # inpainting

   lowerCamelCase_  				=						IFInpaintingPipeline(**pipe_a.components  )
   lowerCamelCase_  				=						IFInpaintingSuperResolutionPipeline(**pipe_a.components  )

   pipe_a.enable_model_cpu_offload()
   pipe_a.enable_model_cpu_offload()

   pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )
   pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()  )

   self._test_if_inpainting(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   # pipeline 1

   _start_torch_memory_measurement()

   lowerCamelCase_  				=						torch.Generator(device="cpu"  ).manual_seed(0  )
   lowerCamelCase_  				=						pipe_a(
       prompt_embeds=UpperCamelCase    ,						negative_prompt_embeds=UpperCamelCase    ,						num_inference_steps=2    ,						generator=UpperCamelCase    ,						output_type="np"    ,						)

   lowerCamelCase_  				=						output.images[0]

   assert image.shape == (64, 64, 3)

   lowerCamelCase_  				=						torch.cuda.max_memory_allocated()
   assert mem_bytes < 13 * 10**9

   lowerCamelCase_  				=						load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"  )
   assert_mean_pixel_difference(UpperCamelCase    ,						UpperCamelCase  )

   # pipeline 2

   _start_torch_memory_measurement()

   lowerCamelCase_  				=						torch.Generator(device="cpu"  ).manual_seed(0  )

   lowerCamelCase_  				=						floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0  )  ).to(UpperCamelCase  )

   lowerCamelCase_  				=						pipe_a(
       prompt_embeds=UpperCamelCase    ,						negative_prompt_embeds=UpperCamelCase    ,						image=UpperCamelCase    ,						generator=UpperCamelCase    ,						num_inference_steps=2    ,						output_type="np"    ,						)

   lowerCamelCase_  				=						output.images[0]

   assert image.shape == (256, 256, 3)

   lowerCamelCase_  				=						torch.cuda.max_memory_allocated()
   assert mem_bytes < 4 * 10**9

   lowerCamelCase_  				=						load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"  )
   assert_mean_pixel_difference(UpperCamelCase    ,						UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   # pipeline 1

   _start_torch_memory_measurement()

   lowerCamelCase_  				=						floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0  )  ).to(UpperCamelCase  )

   lowerCamelCase_  				=						torch.Generator(device="cpu"  ).manual_seed(0  )

   lowerCamelCase_  				=						pipe_a(
       prompt_embeds=UpperCamelCase    ,						negative_prompt_embeds=UpperCamelCase    ,						image=UpperCamelCase    ,						num_inference_steps=2    ,						generator=UpperCamelCase    ,						output_type="np"    ,						)

   lowerCamelCase_  				=						output.images[0]

   assert image.shape == (64, 64, 3)

   lowerCamelCase_  				=						torch.cuda.max_memory_allocated()
   assert mem_bytes < 10 * 10**9

   lowerCamelCase_  				=						load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"  )
   assert_mean_pixel_difference(UpperCamelCase    ,						UpperCamelCase  )

   # pipeline 2

   _start_torch_memory_measurement()

   lowerCamelCase_  				=						torch.Generator(device="cpu"  ).manual_seed(0  )

   lowerCamelCase_  				=						floats_tensor((1, 3, 256, 256)    ,						rng=random.Random(0  )  ).to(UpperCamelCase  )
   lowerCamelCase_  				=						floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0  )  ).to(UpperCamelCase  )

   lowerCamelCase_  				=						pipe_a(
       prompt_embeds=UpperCamelCase    ,						negative_prompt_embeds=UpperCamelCase    ,						image=UpperCamelCase    ,						original_image=UpperCamelCase    ,						generator=UpperCamelCase    ,						num_inference_steps=2    ,						output_type="np"    ,						)

   lowerCamelCase_  				=						output.images[0]

   assert image.shape == (256, 256, 3)

   lowerCamelCase_  				=						torch.cuda.max_memory_allocated()
   assert mem_bytes < 4 * 10**9

   lowerCamelCase_  				=						load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"  )
   assert_mean_pixel_difference(UpperCamelCase    ,						UpperCamelCase  )







  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   # pipeline 1

   _start_torch_memory_measurement()

   lowerCamelCase_  				=						floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0  )  ).to(UpperCamelCase  )
   lowerCamelCase_  				=						floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(1  )  ).to(UpperCamelCase  )

   lowerCamelCase_  				=						torch.Generator(device="cpu"  ).manual_seed(0  )
   lowerCamelCase_  				=						pipe_a(
       prompt_embeds=UpperCamelCase    ,						negative_prompt_embeds=UpperCamelCase    ,						image=UpperCamelCase    ,						mask_image=UpperCamelCase    ,						num_inference_steps=2    ,						generator=UpperCamelCase    ,						output_type="np"    ,						)

   lowerCamelCase_  				=						output.images[0]

   assert image.shape == (64, 64, 3)

   lowerCamelCase_  				=						torch.cuda.max_memory_allocated()
   assert mem_bytes < 10 * 10**9

   lowerCamelCase_  				=						load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"  )
   assert_mean_pixel_difference(UpperCamelCase    ,						UpperCamelCase  )

   # pipeline 2

   _start_torch_memory_measurement()

   lowerCamelCase_  				=						torch.Generator(device="cpu"  ).manual_seed(0  )

   lowerCamelCase_  				=						floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0  )  ).to(UpperCamelCase  )
   lowerCamelCase_  				=						floats_tensor((1, 3, 256, 256)    ,						rng=random.Random(0  )  ).to(UpperCamelCase  )
   lowerCamelCase_  				=						floats_tensor((1, 3, 256, 256)    ,						rng=random.Random(1  )  ).to(UpperCamelCase  )

   lowerCamelCase_  				=						pipe_a(
       prompt_embeds=UpperCamelCase    ,						negative_prompt_embeds=UpperCamelCase    ,						image=UpperCamelCase    ,						mask_image=UpperCamelCase    ,						original_image=UpperCamelCase    ,						generator=UpperCamelCase    ,						num_inference_steps=2    ,						output_type="np"    ,						)

   lowerCamelCase_  				=						output.images[0]

   assert image.shape == (256, 256, 3)

   lowerCamelCase_  				=						torch.cuda.max_memory_allocated()
   assert mem_bytes < 4 * 10**9

   lowerCamelCase_  				=						load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"  )
   assert_mean_pixel_difference(UpperCamelCase    ,						UpperCamelCase  )





def __snake_case      (   ):
 torch.cuda.empty_cache()
 torch.cuda.reset_max_memory_allocated()
 torch.cuda.reset_peak_memory_stats()







'''simple docstring'''

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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


a_						:       Dict      			=				logging.get_logger(__name__)

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

a_						:       Optional[Any]      			=				{
    """tokenizer_file""": {
        """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
    },
}

a_						:       str      			=				{
    """gpt-neox-20b""": 2048,
}





class   snake_case					(					lowercase    ):





  """simple docstring"""




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






  def __init__(					self    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase="<|endoftext|>"    ,						UpperCamelCase=False    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(
       UpperCamelCase    ,						UpperCamelCase    ,						tokenizer_file=UpperCamelCase    ,						unk_token=UpperCamelCase    ,						bos_token=UpperCamelCase    ,						eos_token=UpperCamelCase    ,						add_prefix_space=UpperCamelCase    ,						**UpperCamelCase    ,						)

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

   lowerCamelCase_  				=						add_prefix_space






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None  ):







   """simple docstring"""
   lowerCamelCase_  				=						self._tokenizer.model.save(UpperCamelCase    ,						name=UpperCamelCase  )
   return tuple(UpperCamelCase  )







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  ) + [self.eos_token_id]  )

   if len(UpperCamelCase  ) > self.model_max_length:
    lowerCamelCase_  				=						input_ids[-self.model_max_length :]
   return input_ids



