import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device







def 					UpperCamelCase_						( snake_case_	:	nn.Module				) ->					Any:






 '''simple docstring'''

 __lowerCAmelCase    							=						{}
 for name in unet.attn_processors.keys():
  __lowerCAmelCase    							=						None if name.endswith("""attn1.processor"""				) else unet.config.cross_attention_dim
  if name.startswith("""mid_block"""				):
   __lowerCAmelCase    							=						unet.config.block_out_channels[-1]
  elif name.startswith("""up_blocks"""				):
   __lowerCAmelCase    							=						int(name[len("""up_blocks."""				)]				)
   __lowerCAmelCase    							=						list(reversed(unet.config.block_out_channels				)				)[block_id]
  elif name.startswith("""down_blocks"""				):
   __lowerCAmelCase    							=						int(name[len("""down_blocks."""				)]				)
   __lowerCAmelCase    							=						unet.config.block_out_channels[block_id]
  __lowerCAmelCase    							=						(
      LoRAAttnProcessor2_0 if hasattr(snake_case_						,		"""scaled_dot_product_attention"""				) else LoRAAttnProcessor
  )
  __lowerCAmelCase    							=						lora_attn_processor_class(
      hidden_size=snake_case_						,		cross_attention_dim=snake_case_				)
 __lowerCAmelCase    							=						AttnProcsLayers(snake_case_				)
 return lora_attn_procs, unet_lora_layers







def 					UpperCamelCase_						( snake_case_	:	nn.Module				) ->					List[Any]:






 '''simple docstring'''

 __lowerCAmelCase    							=						{}
 __lowerCAmelCase    							=						(
     LoRAAttnProcessor2_0 if hasattr(snake_case_						,		"""scaled_dot_product_attention"""				) else LoRAAttnProcessor
 )
 for name, module in text_encoder.named_modules():
  if name.endswith(snake_case_				):
   __lowerCAmelCase    							=						lora_attn_processor_class(
       hidden_size=module.out_proj.out_features						,		cross_attention_dim=snake_case_				)
 return text_lora_attn_procs







def 					UpperCamelCase_						( snake_case_	:	nn.Module				) ->					Union[str, Any]:






 '''simple docstring'''

 __lowerCAmelCase    							=						create_text_encoder_lora_attn_procs(snake_case_				)
 __lowerCAmelCase    							=						AttnProcsLayers(snake_case_				)
 return text_encoder_lora_layers







def 					UpperCamelCase_						( snake_case_	:	Tuple						,		snake_case_	:	Optional[Any]=False				) ->					int:






 '''simple docstring'''

 for _, attn_proc in text_lora_attn_procs.items():
  # set up.weights
  for layer_name, layer_module in attn_proc.named_modules():
   if layer_name.endswith("""_lora"""				):
    __lowerCAmelCase    							=						(
        torch.randn_like(layer_module.up.weight				)
        if randn_weight
        else torch.zeros_like(layer_module.up.weight				)
    )
    __lowerCAmelCase    							=						torch.nn.Parameter(snake_case_				)







class     _lowercase					(    unittest.TestCase      ):


 '''simple docstring'''






 def a (				self       :     Optional[Any]							)  ->   List[Any]:
  torch.manual_seed(0							)
  __lowerCAmelCase    							=						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				,	)
  __lowerCAmelCase    							=						DDIMScheduler(
      beta_start=0.0_0_0_8_5				,	beta_end=0.0_1_2				,	beta_schedule="""scaled_linear"""				,	clip_sample=SCREAMING_SNAKE_CASE__				,	set_alpha_to_one=SCREAMING_SNAKE_CASE__				,	steps_offset=1				,	)
  torch.manual_seed(0							)
  __lowerCAmelCase    							=						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				,	)
  __lowerCAmelCase    							=						CLIPTextConfig(
      bos_token_id=0				,	eos_token_id=2				,	hidden_size=32				,	intermediate_size=37				,	layer_norm_eps=1e-0_5				,	num_attention_heads=4				,	num_hidden_layers=5				,	pad_token_id=1				,	vocab_size=10_00				,	)
  __lowerCAmelCase    							=						CLIPTextModel(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

  __lowerCAmelCase							,	__lowerCAmelCase    							=						create_unet_lora_layers(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						create_text_encoder_lora_layers(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						{
      """unet""": unet,
      """scheduler""": scheduler,
      """vae""": vae,
      """text_encoder""": text_encoder,
      """tokenizer""": tokenizer,
      """safety_checker""": None,
      """feature_extractor""": None,
  }
  __lowerCAmelCase    							=						{
      """unet_lora_layers""": unet_lora_layers,
      """text_encoder_lora_layers""": text_encoder_lora_layers,
      """unet_lora_attn_procs""": unet_lora_attn_procs,
  }
  return pipeline_components, lora_components
 def a (				self       :     str							)  ->   Optional[Any]:
  __lowerCAmelCase    							=						1
  __lowerCAmelCase    							=						10
  __lowerCAmelCase    							=						4
  __lowerCAmelCase    							=						(32, 32)

  __lowerCAmelCase    							=						torch.manual_seed(0							)
  __lowerCAmelCase    							=						floats_tensor((batch_size, num_channels) + sizes							)
  __lowerCAmelCase    							=						torch.randint(1				,	SCREAMING_SNAKE_CASE__				,	size=(batch_size, sequence_length)				,	generator=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						{
      """prompt""": """A painting of a squirrel eating a burger""",
      """generator""": generator,
      """num_inference_steps""": 2,
      """guidance_scale""": 6.0,
      """output_type""": """numpy""",
  }

  return noise, input_ids, pipeline_inputs

  # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
 def a (				self       :     Optional[Any]							)  ->   Union[str, Any]:
  __lowerCAmelCase    							=						77

  __lowerCAmelCase    							=						torch.randint(2				,	56				,	size=(1, max_seq_length)				,	generator=torch.manual_seed(0							)							)

  __lowerCAmelCase    							=						{}
  __lowerCAmelCase    							=						inputs
  return prepared_inputs
 def a (				self       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]							)  ->   List[Any]:
  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_components()
  LoraLoaderMixin.save_lora_weights(
      save_directory=SCREAMING_SNAKE_CASE__				,	unet_lora_layers=lora_components["""unet_lora_layers"""]				,	text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]				,	)
  self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__				,	"""pytorch_lora_weights.bin"""							)							)							)
 def a (				self       :     Dict							)  ->   int:
  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						StableDiffusionPipeline(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
  sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_inputs()

  __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__							).images
  __lowerCAmelCase    							=						original_images[0, -3:, -3:, -1]

  with tempfile.TemporaryDirectory() as tmpdirname:
   LoraLoaderMixin.save_lora_weights(
       save_directory=SCREAMING_SNAKE_CASE__				,	unet_lora_layers=lora_components["""unet_lora_layers"""]				,	text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]				,	)
   self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__				,	"""pytorch_lora_weights.bin"""							)							)							)
   sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__							).images
  __lowerCAmelCase    							=						lora_images[0, -3:, -3:, -1]

  # Outputs shouldn't match.
  self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE__							)				,	torch.from_numpy(SCREAMING_SNAKE_CASE__							)							)							)
 def a (				self       :     Optional[Any]							)  ->   Dict:
  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						StableDiffusionPipeline(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
  sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_inputs()

  __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__							).images
  __lowerCAmelCase    							=						original_images[0, -3:, -3:, -1]

  with tempfile.TemporaryDirectory() as tmpdirname:
   LoraLoaderMixin.save_lora_weights(
       save_directory=SCREAMING_SNAKE_CASE__				,	unet_lora_layers=lora_components["""unet_lora_layers"""]				,	text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]				,	safe_serialization=SCREAMING_SNAKE_CASE__				,	)
   self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__				,	"""pytorch_lora_weights.safetensors"""							)							)							)
   sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__							).images
  __lowerCAmelCase    							=						lora_images[0, -3:, -3:, -1]

  # Outputs shouldn't match.
  self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE__							)				,	torch.from_numpy(SCREAMING_SNAKE_CASE__							)							)							)
 def a (				self       :     Any							)  ->   Tuple:
  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						lora_components["""unet_lora_attn_procs"""]
  __lowerCAmelCase    							=						StableDiffusionPipeline(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
  sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_inputs()

  __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__							).images
  __lowerCAmelCase    							=						original_images[0, -3:, -3:, -1]

  with tempfile.TemporaryDirectory() as tmpdirname:
   __lowerCAmelCase    							=						sd_pipe.unet
   unet.set_attn_processor(SCREAMING_SNAKE_CASE__							)
   unet.save_attn_procs(SCREAMING_SNAKE_CASE__							)
   self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__				,	"""pytorch_lora_weights.bin"""							)							)							)
   sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__							).images
  __lowerCAmelCase    							=						lora_images[0, -3:, -3:, -1]

  # Outputs shouldn't match.
  self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE__							)				,	torch.from_numpy(SCREAMING_SNAKE_CASE__							)							)							)
 def a (				self       :     List[str]							)  ->   List[str]:
  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						StableDiffusionPipeline(**SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						self.get_dummy_tokens()

  # inference without lora
  __lowerCAmelCase    							=						pipe.text_encoder(**SCREAMING_SNAKE_CASE__							)[0]
  assert outputs_without_lora.shape == (1, 77, 32)

  # create lora_attn_procs with zeroed out up.weights
  __lowerCAmelCase    							=						create_text_encoder_lora_attn_procs(pipe.text_encoder							)
  set_lora_up_weights(SCREAMING_SNAKE_CASE__				,	randn_weight=SCREAMING_SNAKE_CASE__							)

  # monkey patch
  pipe._modify_text_encoder(SCREAMING_SNAKE_CASE__							)

  # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  del text_attn_procs
  gc.collect()

  # inference with lora
  __lowerCAmelCase    							=						pipe.text_encoder(**SCREAMING_SNAKE_CASE__							)[0]
  assert outputs_with_lora.shape == (1, 77, 32)

  assert torch.allclose(
      SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

  # create lora_attn_procs with randn up.weights
  __lowerCAmelCase    							=						create_text_encoder_lora_attn_procs(pipe.text_encoder							)
  set_lora_up_weights(SCREAMING_SNAKE_CASE__				,	randn_weight=SCREAMING_SNAKE_CASE__							)

  # monkey patch
  pipe._modify_text_encoder(SCREAMING_SNAKE_CASE__							)

  # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  del text_attn_procs
  gc.collect()

  # inference with lora
  __lowerCAmelCase    							=						pipe.text_encoder(**SCREAMING_SNAKE_CASE__							)[0]
  assert outputs_with_lora.shape == (1, 77, 32)

  assert not torch.allclose(
      SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
 def a (				self       :     Dict							)  ->   Union[str, Any]:
  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						StableDiffusionPipeline(**SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						self.get_dummy_tokens()

  # inference without lora
  __lowerCAmelCase    							=						pipe.text_encoder(**SCREAMING_SNAKE_CASE__							)[0]
  assert outputs_without_lora.shape == (1, 77, 32)

  # create lora_attn_procs with randn up.weights
  __lowerCAmelCase    							=						create_text_encoder_lora_attn_procs(pipe.text_encoder							)
  set_lora_up_weights(SCREAMING_SNAKE_CASE__				,	randn_weight=SCREAMING_SNAKE_CASE__							)

  # monkey patch
  pipe._modify_text_encoder(SCREAMING_SNAKE_CASE__							)

  # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  del text_attn_procs
  gc.collect()

  # inference with lora
  __lowerCAmelCase    							=						pipe.text_encoder(**SCREAMING_SNAKE_CASE__							)[0]
  assert outputs_with_lora.shape == (1, 77, 32)

  assert not torch.allclose(
      SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							), "lora outputs should be different to without lora outputs"

  # remove monkey patch
  pipe._remove_text_encoder_monkey_patch()

  # inference with removed lora
  __lowerCAmelCase    							=						pipe.text_encoder(**SCREAMING_SNAKE_CASE__							)[0]
  assert outputs_without_lora_removed.shape == (1, 77, 32)

  assert torch.allclose(
      SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							), "remove lora monkey patch should restore the original outputs"
 def a (				self       :     Optional[int]							)  ->   Optional[Any]:
  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						StableDiffusionPipeline(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
  sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_inputs()

  with tempfile.TemporaryDirectory() as tmpdirname:
   LoraLoaderMixin.save_lora_weights(
       save_directory=SCREAMING_SNAKE_CASE__				,	unet_lora_layers=lora_components["""unet_lora_layers"""]				,	text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]				,	)
   self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__				,	"""pytorch_lora_weights.bin"""							)							)							)
   sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__							).images
  __lowerCAmelCase    							=						lora_images[0, -3:, -3:, -1]

  __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__				,	cross_attention_kwargs={"""scale""": 0.5}							).images
  __lowerCAmelCase    							=						lora_images_with_scale[0, -3:, -3:, -1]

  # Outputs shouldn't match.
  self.assertFalse(
      torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE__							)				,	torch.from_numpy(SCREAMING_SNAKE_CASE__							)							)							)
 def a (				self       :     Dict							)  ->   Optional[int]:
  with tempfile.TemporaryDirectory() as tmpdirname:
   self.create_lora_weight_file(SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_components()
   __lowerCAmelCase    							=						StableDiffusionPipeline(**SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
   sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

   # check if vanilla attention processors are used
   for _, module in sd_pipe.unet.named_modules():
    if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							):
     self.assertIsInstance(module.processor				,	(AttnProcessor, AttnProcessor2_0)							)

            # load LoRA weight file
   sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE__							)

   # check if lora attention processors are used
   for _, module in sd_pipe.unet.named_modules():
    if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							):
     __lowerCAmelCase    							=						(
         LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE__				,	"""scaled_dot_product_attention"""							) else LoRAAttnProcessor
     )
     self.assertIsInstance(module.processor				,	SCREAMING_SNAKE_CASE__							)
 @unittest.skipIf(torch_device != """cuda"""				,	"""This test is supposed to run on GPU"""							)
 def a (				self       :     Optional[Any]							)  ->   int:
  with tempfile.TemporaryDirectory() as tmpdirname:
   self.create_lora_weight_file(SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_components()
   __lowerCAmelCase    							=						StableDiffusionPipeline(**SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
   sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

   # enable XFormers
   sd_pipe.enable_xformers_memory_efficient_attention()

   # check if xFormers attention processors are used
   for _, module in sd_pipe.unet.named_modules():
    if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							):
     self.assertIsInstance(module.processor				,	SCREAMING_SNAKE_CASE__							)

            # load LoRA weight file
   sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE__							)

   # check if lora attention processors are used
   for _, module in sd_pipe.unet.named_modules():
    if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							):
     self.assertIsInstance(module.processor				,	SCREAMING_SNAKE_CASE__							)





 @unittest.skipIf(torch_device != """cuda"""				,	"""This test is supposed to run on GPU"""							)
 def a (				self       :     Optional[Any]							)  ->   Union[str, Any]:
  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						StableDiffusionPipeline(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
  sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_dummy_inputs()

  # enable XFormers
  sd_pipe.enable_xformers_memory_efficient_attention()

  __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__							).images
  __lowerCAmelCase    							=						original_images[0, -3:, -3:, -1]

  with tempfile.TemporaryDirectory() as tmpdirname:
   LoraLoaderMixin.save_lora_weights(
       save_directory=SCREAMING_SNAKE_CASE__				,	unet_lora_layers=lora_components["""unet_lora_layers"""]				,	text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]				,	)
   self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__				,	"""pytorch_lora_weights.bin"""							)							)							)
   sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__							).images
  __lowerCAmelCase    							=						lora_images[0, -3:, -3:, -1]

  # Outputs shouldn't match.
  self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE__							)				,	torch.from_numpy(SCREAMING_SNAKE_CASE__							)							)							)







'''simple docstring'''

import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()







def 					UpperCamelCase_						( snake_case_	:	List[str]						,		snake_case_	:	Optional[Any]				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						CanineConfig()
 __lowerCAmelCase    							=						CanineModel(snake_case_				)
 model.eval()

 print(f"""Building PyTorch model from configuration: {config}"""				)

 # Load weights from tf checkpoint
 load_tf_weights_in_canine(snake_case_						,		snake_case_						,		snake_case_				)

 # Save pytorch-model (weights and configuration)
 print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
 model.save_pretrained(snake_case_				)

 # Save tokenizer files
 __lowerCAmelCase    							=						CanineTokenizer()
 print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
 tokenizer.save_pretrained(snake_case_				)


if __name__ == "__main__":
 _A      :				int 				=    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to a folder where the PyTorch model will be placed.''',
 )
 _A      :				Dict 				=    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






