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    A_ (       a_			,       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			IFPipeline
  _SCREAMING_SNAKE_CASE    				=			TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
  _SCREAMING_SNAKE_CASE    				=			TEXT_TO_IMAGE_BATCH_PARAMS
  _SCREAMING_SNAKE_CASE    				=			PipelineTesterMixin.required_optional_params - {"""latents"""}

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   return self._get_dummy_components()

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Dict=0       ):
   if str(__SCREAMING_SNAKE_CASE       ).startswith("mps"       ):
    __a       =  torch.manual_seed(__SCREAMING_SNAKE_CASE       )
   else:
    __a       =  torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(__SCREAMING_SNAKE_CASE       )

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

   return inputs

  def 		_UpperCAmelCase   (		self :      str       ):
   self._test_save_load_optional_components()

  @unittest.skipIf(torch_device != "cuda"     ,							reason="float16 requires CUDA"       )
  def 		_UpperCAmelCase   (		self :      Tuple       ):
   # 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 		_UpperCAmelCase   (		self :      Any       ):
   self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )

  def 		_UpperCAmelCase   (		self :      str       ):
   self._test_save_load_local()

  def 		_UpperCAmelCase   (		self :      Dict       ):
   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 		_UpperCAmelCase   (		self :      Dict       ):
   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )
@slow
@require_torch_gpu
class    A_ (       unittest.TestCase ):

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()

  def 		_UpperCAmelCase   (		self :      str       ):
   # if

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

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

   # pre compute text embeddings and remove T5 to save memory

   pipe_a.text_encoder.to("cuda"       )

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

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

   __a       =  None
   __a       =  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(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   pipe_a.remove_all_hooks()
   pipe_a.remove_all_hooks()

   # img2img

   __a       =  IFImgaImgPipeline(**pipe_a.components       )
   __a       =  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(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   pipe_a.remove_all_hooks()
   pipe_a.remove_all_hooks()

   # inpainting

   __a       =  IFInpaintingPipeline(**pipe_a.components       )
   __a       =  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(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      str       ):
   # pipeline 1

   _start_torch_memory_measurement()

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

   __a       =  output.images[0]

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

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

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

   # pipeline 2

   _start_torch_memory_measurement()

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

   __a       =  floats_tensor((1, 3, 64, 64)     ,							rng=random.Random(0       )       ).to(__SCREAMING_SNAKE_CASE       )

   __a       =  pipe_a(
       prompt_embeds=__SCREAMING_SNAKE_CASE     ,							negative_prompt_embeds=__SCREAMING_SNAKE_CASE     ,							image=__SCREAMING_SNAKE_CASE     ,							generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="np"     ,							)

   __a       =  output.images[0]

   assert image.shape == (2_56, 2_56, 3)

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

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

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
   # pipeline 1

   _start_torch_memory_measurement()

   __a       =  floats_tensor((1, 3, 64, 64)     ,							rng=random.Random(0       )       ).to(__SCREAMING_SNAKE_CASE       )

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

   __a       =  pipe_a(
       prompt_embeds=__SCREAMING_SNAKE_CASE     ,							negative_prompt_embeds=__SCREAMING_SNAKE_CASE     ,							image=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							generator=__SCREAMING_SNAKE_CASE     ,							output_type="np"     ,							)

   __a       =  output.images[0]

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

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

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

   # pipeline 2

   _start_torch_memory_measurement()

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

   __a       =  floats_tensor((1, 3, 2_56, 2_56)     ,							rng=random.Random(0       )       ).to(__SCREAMING_SNAKE_CASE       )
   __a       =  floats_tensor((1, 3, 64, 64)     ,							rng=random.Random(0       )       ).to(__SCREAMING_SNAKE_CASE       )

   __a       =  pipe_a(
       prompt_embeds=__SCREAMING_SNAKE_CASE     ,							negative_prompt_embeds=__SCREAMING_SNAKE_CASE     ,							image=__SCREAMING_SNAKE_CASE     ,							original_image=__SCREAMING_SNAKE_CASE     ,							generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="np"     ,							)

   __a       =  output.images[0]

   assert image.shape == (2_56, 2_56, 3)

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

   __a       =  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(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
   # pipeline 1

   _start_torch_memory_measurement()

   __a       =  floats_tensor((1, 3, 64, 64)     ,							rng=random.Random(0       )       ).to(__SCREAMING_SNAKE_CASE       )
   __a       =  floats_tensor((1, 3, 64, 64)     ,							rng=random.Random(1       )       ).to(__SCREAMING_SNAKE_CASE       )

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

   __a       =  output.images[0]

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

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

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

   # pipeline 2

   _start_torch_memory_measurement()

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

   __a       =  floats_tensor((1, 3, 64, 64)     ,							rng=random.Random(0       )       ).to(__SCREAMING_SNAKE_CASE       )
   __a       =  floats_tensor((1, 3, 2_56, 2_56)     ,							rng=random.Random(0       )       ).to(__SCREAMING_SNAKE_CASE       )
   __a       =  floats_tensor((1, 3, 2_56, 2_56)     ,							rng=random.Random(1       )       ).to(__SCREAMING_SNAKE_CASE       )

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

   __a       =  output.images[0]

   assert image.shape == (2_56, 2_56, 3)

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

   __a       =  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(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )



def   __A		(			):



 """simple docstring"""


 torch.cuda.empty_cache()
 torch.cuda.reset_max_memory_allocated()
 torch.cuda.reset_peak_memory_stats()



import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
  import jax.numpy as jnp

  from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class    A_ (       unittest.TestCase ):

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base"       )
   __a       =  AutoTokenizer.from_pretrained("xlm-roberta-base"       )
   __a       =  "The dog is cute and lives in the garden house"
   __a       =  jnp.array([tokenizer.encode(__SCREAMING_SNAKE_CASE       )]       )

   __a       =  (1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
   __a       =  jnp.array(
       [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]       )

   __a       =  model(__SCREAMING_SNAKE_CASE       )["last_hidden_state"]
   self.assertEqual(output.shape     ,							__SCREAMING_SNAKE_CASE       )
   # compare the actual values for a slice of last dim
   self.assertTrue(jnp.allclose(output[:, :, -1]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-3       )       )


