


'''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"""
   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"""
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def 						snake_case	(					self  ):







   """simple docstring"""
   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"""
   _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"""
   _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"""
   _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'''

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
     from ...feature_extraction_utils import FeatureExtractionMixin
     from ...tokenization_utils_base import PreTrainedTokenizerBase
     from ...utils import TensorType

a_						:       Optional[int]      			=				logging.get_logger(__name__)

a_						:       Optional[int]      			=				{
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
a_						:       List[str]      			=				[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
a_						:       Tuple      			=				[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						"whisper"
  _lowerCamelCase						=						["past_key_values"]
  _lowerCamelCase						=						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






  def __init__(					self    ,						UpperCamelCase=5_1865    ,						UpperCamelCase=80    ,						UpperCamelCase=6    ,						UpperCamelCase=4    ,						UpperCamelCase=6    ,						UpperCamelCase=4    ,						UpperCamelCase=1536    ,						UpperCamelCase=1536    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=5_0257    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=256    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.02    ,						UpperCamelCase=False    ,						UpperCamelCase=1500    ,						UpperCamelCase=448    ,						UpperCamelCase=5_0256    ,						UpperCamelCase=5_0256    ,						UpperCamelCase=5_0256    ,						UpperCamelCase=None    ,						UpperCamelCase=[220, 5_0256]    ,						UpperCamelCase=False    ,						UpperCamelCase=256    ,						UpperCamelCase=False    ,						UpperCamelCase=0.05    ,						UpperCamelCase=10    ,						UpperCamelCase=2    ,						UpperCamelCase=0.0    ,						UpperCamelCase=10    ,						UpperCamelCase=0    ,						UpperCamelCase=7    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						vocab_size
   lowerCamelCase_  				=						num_mel_bins
   lowerCamelCase_  				=						d_model
   lowerCamelCase_  				=						encoder_layers
   lowerCamelCase_  				=						encoder_attention_heads
   lowerCamelCase_  				=						decoder_layers
   lowerCamelCase_  				=						decoder_attention_heads
   lowerCamelCase_  				=						decoder_ffn_dim
   lowerCamelCase_  				=						encoder_ffn_dim
   lowerCamelCase_  				=						dropout
   lowerCamelCase_  				=						attention_dropout
   lowerCamelCase_  				=						activation_dropout
   lowerCamelCase_  				=						activation_function
   lowerCamelCase_  				=						init_std
   lowerCamelCase_  				=						encoder_layerdrop
   lowerCamelCase_  				=						decoder_layerdrop
   lowerCamelCase_  				=						use_cache
   lowerCamelCase_  				=						encoder_layers
   lowerCamelCase_  				=						scale_embedding  # scale factor will be sqrt(d_model) if True
   lowerCamelCase_  				=						max_source_positions
   lowerCamelCase_  				=						max_target_positions

   # Audio Classification-specific parameters. Feel free to ignore for other classes.
   lowerCamelCase_  				=						classifier_proj_size
   lowerCamelCase_  				=						use_weighted_layer_sum

   # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
   lowerCamelCase_  				=						apply_spec_augment
   lowerCamelCase_  				=						mask_time_prob
   lowerCamelCase_  				=						mask_time_length
   lowerCamelCase_  				=						mask_time_min_masks
   lowerCamelCase_  				=						mask_feature_prob
   lowerCamelCase_  				=						mask_feature_length
   lowerCamelCase_  				=						mask_feature_min_masks

   lowerCamelCase_  				=						median_filter_width

   super().__init__(
       pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase    ,						is_encoder_decoder=UpperCamelCase    ,						decoder_start_token_id=UpperCamelCase    ,						suppress_tokens=UpperCamelCase    ,						begin_suppress_tokens=UpperCamelCase    ,						**UpperCamelCase    ,						)





class   snake_case					(					lowercase    ):





  """simple docstring"""




  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						OrderedDict(
       [
           ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
       ]  )
   if self.use_past:
    lowerCamelCase_  				=						{0: "batch"}
   else:
    lowerCamelCase_  				=						{0: "batch", 1: "decoder_sequence"}

   if self.use_past:
    self.fill_with_past_key_values_(UpperCamelCase    ,						direction="inputs"  )

   return common_inputs






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = -1    ,						UpperCamelCase = -1    ,						UpperCamelCase = False    ,						UpperCamelCase = None    ,						UpperCamelCase = 2_2050    ,						UpperCamelCase = 5.0    ,						UpperCamelCase = 220    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						OrderedDict()
   lowerCamelCase_  				=						OnnxConfig.generate_dummy_inputs(
       self    ,						preprocessor=preprocessor.feature_extractor    ,						batch_size=UpperCamelCase    ,						framework=UpperCamelCase    ,						sampling_rate=UpperCamelCase    ,						time_duration=UpperCamelCase    ,						frequency=UpperCamelCase    ,						)
   lowerCamelCase_  				=						encoder_inputs["input_features"].shape[2]
   lowerCamelCase_  				=						encoder_sequence_length // 2 if self.use_past else seq_length

   lowerCamelCase_  				=						super().generate_dummy_inputs(
       preprocessor.tokenizer    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  )

   lowerCamelCase_  				=						encoder_inputs.pop("input_features"  )
   lowerCamelCase_  				=						decoder_inputs.pop("decoder_input_ids"  )

   if "past_key_values" in decoder_inputs:
    lowerCamelCase_  				=						decoder_inputs.pop("past_key_values"  )

   return dummy_inputs







  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return 1e-3



