'''simple docstring'''

import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
 import tensorflow as tf

 tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset


_A      :				Dict 				=    5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors







def 					UpperCamelCase_						( snake_case_	:	Namespace				) ->					Any:






 '''simple docstring'''

 return PTtoTFCommand(
     args.model_name						,		args.local_dir						,		args.max_error						,		args.new_weights						,		args.no_pr						,		args.push						,		args.extra_commit_description						,		args.override_model_class						,		)







class     _lowercase					(    UpperCAmelCase__      ):


 '''simple docstring'''






 @staticmethod
 def a (				SCREAMING_SNAKE_CASE__       :     ArgumentParser							)  ->   List[str]:
  __lowerCAmelCase    							=						parser.add_parser(
      """pt-to-tf"""				,	help=(
          """CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."""
          """ Can also be used to validate existing weights without opening PRs, with --no-pr."""
      )				,	)
  train_parser.add_argument(
      """--model-name"""				,	type=SCREAMING_SNAKE_CASE__				,	required=SCREAMING_SNAKE_CASE__				,	help="""The model name, including owner/organization, as seen on the hub."""				,	)
  train_parser.add_argument(
      """--local-dir"""				,	type=SCREAMING_SNAKE_CASE__				,	default=""""""				,	help="""Optional local directory of the model repository. Defaults to /tmp/{model_name}"""				,	)
  train_parser.add_argument(
      """--max-error"""				,	type=SCREAMING_SNAKE_CASE__				,	default=SCREAMING_SNAKE_CASE__				,	help=(
          f"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
      )				,	)
  train_parser.add_argument(
      """--new-weights"""				,	action="""store_true"""				,	help="""Optional flag to create new TensorFlow weights, even if they already exist."""				,	)
  train_parser.add_argument(
      """--no-pr"""				,	action="""store_true"""				,	help="""Optional flag to NOT open a PR with converted weights."""							)
  train_parser.add_argument(
      """--push"""				,	action="""store_true"""				,	help="""Optional flag to push the weights directly to `main` (requires permissions)"""				,	)
  train_parser.add_argument(
      """--extra-commit-description"""				,	type=SCREAMING_SNAKE_CASE__				,	default=""""""				,	help="""Optional additional commit description to use when opening a PR (e.g. to tag the owner)."""				,	)
  train_parser.add_argument(
      """--override-model-class"""				,	type=SCREAMING_SNAKE_CASE__				,	default=SCREAMING_SNAKE_CASE__				,	help="""If you think you know better than the auto-detector, you can specify the model class here. """
      """Can be either an AutoModel class or a specific model class like BertForSequenceClassification."""				,	)
  train_parser.set_defaults(func=SCREAMING_SNAKE_CASE__							)
 @staticmethod
 def a (				SCREAMING_SNAKE_CASE__       :     Any				,	SCREAMING_SNAKE_CASE__       :     Any							)  ->   str:
  __lowerCAmelCase    							=						set(pt_outputs.keys()							)
  __lowerCAmelCase    							=						set(tf_outputs.keys()							)
  if pt_out_attrs != tf_out_attrs:
   raise ValueError(
       f"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
       f""" {tf_out_attrs})"""							)

  # 2. For each output attribute, computes the difference
  def _find_pt_tf_differences(SCREAMING_SNAKE_CASE__       :     Dict				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     str=""							):
   # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
   # recursivelly, keeping the name of the attribute.
   if isinstance(SCREAMING_SNAKE_CASE__				,	torch.Tensor							):
    __lowerCAmelCase    							=						np.max(np.abs(pt_out.numpy() - tf_out.numpy()							)							)
    __lowerCAmelCase    							=						tensor_difference
   else:
    __lowerCAmelCase    							=						attr_name
    for i, pt_item in enumerate(SCREAMING_SNAKE_CASE__							):
     # If it is a named attribute, we keep the name. Otherwise, just its index.
     if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							):
      __lowerCAmelCase    							=						root_name + pt_item
      __lowerCAmelCase    							=						tf_out[pt_item]
      __lowerCAmelCase    							=						pt_out[pt_item]
     else:
      __lowerCAmelCase    							=						root_name + f"""[{i}]"""
      __lowerCAmelCase    							=						tf_out[i]
     __lowerCAmelCase    							=						_find_pt_tf_differences(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

   return differences

  return _find_pt_tf_differences(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	{}							)
 def __init__(				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     float				,	SCREAMING_SNAKE_CASE__       :     bool				,	SCREAMING_SNAKE_CASE__       :     bool				,	SCREAMING_SNAKE_CASE__       :     bool				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     str				,	*SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	)  ->   Union[str, Any]:
  __lowerCAmelCase    							=						logging.get_logger("""transformers-cli/pt_to_tf"""							)
  __lowerCAmelCase    							=						model_name
  __lowerCAmelCase    							=						local_dir if local_dir else os.path.join("""/tmp"""				,	SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						max_error
  __lowerCAmelCase    							=						new_weights
  __lowerCAmelCase    							=						no_pr
  __lowerCAmelCase    							=						push
  __lowerCAmelCase    							=						extra_commit_description
  __lowerCAmelCase    							=						override_model_class
 def a (				self       :     int				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     Dict							)  ->   Union[str, Any]:

  def _get_audio_input():
   __lowerCAmelCase    							=						load_dataset("""hf-internal-testing/librispeech_asr_dummy"""				,	"""clean"""				,	split="""validation"""							)
   __lowerCAmelCase    							=						ds.sort("""id"""							).select(range(2							)							)[:2]["""audio"""]
   __lowerCAmelCase    							=						[x["""array"""] for x in speech_samples]
   return raw_samples

  __lowerCAmelCase    							=						type(pt_model.config							)
  if model_config_class in PROCESSOR_MAPPING:
   __lowerCAmelCase    							=						AutoProcessor.from_pretrained(self._local_dir							)
   if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
    __lowerCAmelCase    							=						processor.tokenizer.eos_token
  elif model_config_class in IMAGE_PROCESSOR_MAPPING:
   __lowerCAmelCase    							=						AutoImageProcessor.from_pretrained(self._local_dir							)
  elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
   __lowerCAmelCase    							=						AutoFeatureExtractor.from_pretrained(self._local_dir							)
  elif model_config_class in TOKENIZER_MAPPING:
   __lowerCAmelCase    							=						AutoTokenizer.from_pretrained(self._local_dir							)
   if processor.pad_token is None:
    __lowerCAmelCase    							=						processor.eos_token
  else:
   raise ValueError(f"""Unknown data processing type (model config type: {model_config_class})"""							)

  __lowerCAmelCase    							=						set(inspect.signature(pt_model.forward							).parameters.keys()							)
  __lowerCAmelCase    							=						{}
  if "input_ids" in model_forward_signature:
   processor_inputs.update(
       {
           """text""": ["""Hi there!""", """I am a batch with more than one row and different input lengths."""],
           """padding""": True,
           """truncation""": True,
       }							)
  if "pixel_values" in model_forward_signature:
   __lowerCAmelCase    							=						load_dataset("""cifar10"""				,	"""plain_text"""				,	split="""test"""							)[:2]["""img"""]
   processor_inputs.update({"""images""": sample_images}							)
  if "input_features" in model_forward_signature:
   __lowerCAmelCase    							=						inspect.signature(processor.feature_extractor							).parameters
   # Pad to the largest input length by default but take feature extractor default
   # padding value if it exists e.g. "max_length" and is not False or None
   if "padding" in feature_extractor_signature:
    __lowerCAmelCase    							=						feature_extractor_signature["""padding"""].default
    if default_strategy is not False and default_strategy is not None:
     __lowerCAmelCase    							=						default_strategy
    else:
     __lowerCAmelCase    							=						True
   else:
    __lowerCAmelCase    							=						True
   processor_inputs.update({"""audio""": _get_audio_input(), """padding""": padding_strategy}							)
  if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
   processor_inputs.update({"""audio""": _get_audio_input(), """padding""": True}							)
  __lowerCAmelCase    							=						processor(**SCREAMING_SNAKE_CASE__				,	return_tensors="""pt"""							)
  __lowerCAmelCase    							=						processor(**SCREAMING_SNAKE_CASE__				,	return_tensors="""tf"""							)

  # Extra input requirements, in addition to the input modality
  if (
      config.is_encoder_decoder
      or (hasattr(SCREAMING_SNAKE_CASE__				,	"""encoder"""							) and hasattr(SCREAMING_SNAKE_CASE__				,	"""decoder"""							))
      or "decoder_input_ids" in tf_dummy_inputs
  ):
   __lowerCAmelCase    							=						np.asarray([[1], [1]]				,	dtype=SCREAMING_SNAKE_CASE__							) * (pt_model.config.decoder_start_token_id or 0)
   pt_input.update({"""decoder_input_ids""": torch.tensor(SCREAMING_SNAKE_CASE__							)}							)
   tf_input.update({"""decoder_input_ids""": tf.convert_to_tensor(SCREAMING_SNAKE_CASE__							)}							)

  return pt_input, tf_input





 def a (				self       :     List[Any]							)  ->   Tuple:
  # hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
  if version.parse(huggingface_hub.__version__							) < version.parse("""0.9.0"""							):
   raise ImportError(
       """The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"""
       """ installation."""							)
  else:
   from huggingface_hub import Repository, create_commit
   from huggingface_hub._commit_api import CommitOperationAdd

  # Fetch remote data
  __lowerCAmelCase    							=						Repository(local_dir=self._local_dir				,	clone_from=self._model_name							)

  # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
  __lowerCAmelCase    							=						AutoConfig.from_pretrained(self._local_dir							)
  __lowerCAmelCase    							=						config.architectures
  if self._override_model_class is not None:
   if self._override_model_class.startswith("""TF"""							):
    __lowerCAmelCase    							=						[self._override_model_class[2:]]
   else:
    __lowerCAmelCase    							=						[self._override_model_class]
   try:
    __lowerCAmelCase    							=						getattr(import_module("""transformers"""							)				,	architectures[0]							)
   except AttributeError:
    raise ValueError(f"""Model class {self._override_model_class} not found in transformers."""							)
   try:
    __lowerCAmelCase    							=						getattr(import_module("""transformers"""							)				,	"""TF""" + architectures[0]							)
   except AttributeError:
    raise ValueError(f"""TF model class TF{self._override_model_class} not found in transformers."""							)
  elif architectures is None:  # No architecture defined -- use auto classes
   __lowerCAmelCase    							=						getattr(import_module("""transformers"""							)				,	"""AutoModel"""							)
   __lowerCAmelCase    							=						getattr(import_module("""transformers"""							)				,	"""TFAutoModel"""							)
   self._logger.warning("""No detected architecture, using AutoModel/TFAutoModel"""							)
  else:  # Architecture defined -- use it
   if len(SCREAMING_SNAKE_CASE__							) > 1:
    raise ValueError(f"""More than one architecture was found, aborting. (architectures = {architectures})"""							)
   self._logger.warning(f"""Detected architecture: {architectures[0]}"""							)
   __lowerCAmelCase    							=						getattr(import_module("""transformers"""							)				,	architectures[0]							)
   try:
    __lowerCAmelCase    							=						getattr(import_module("""transformers"""							)				,	"""TF""" + architectures[0]							)
   except AttributeError:
    raise AttributeError(f"""The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."""							)

        # Check the TF dummy inputs to see what keys we need in the forward pass
  __lowerCAmelCase    							=						tf_class.from_config(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						tf_from_pt_model.dummy_inputs

  del tf_from_pt_model  # Try to keep only one model in memory at a time

  # Load the model and get some basic inputs
  __lowerCAmelCase    							=						pt_class.from_pretrained(self._local_dir							)
  pt_model.eval()

  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.get_inputs(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

  with torch.no_grad():
   __lowerCAmelCase    							=						pt_model(**SCREAMING_SNAKE_CASE__				,	output_hidden_states=SCREAMING_SNAKE_CASE__							)
  del pt_model  # will no longer be used, and may have a large memory footprint

  __lowerCAmelCase    							=						tf_class.from_pretrained(self._local_dir				,	from_pt=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						tf_from_pt_model(**SCREAMING_SNAKE_CASE__				,	output_hidden_states=SCREAMING_SNAKE_CASE__				,	training=SCREAMING_SNAKE_CASE__							)

  # Confirms that cross loading PT weights into TF worked.
  __lowerCAmelCase    							=						self.find_pt_tf_differences(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						{k: v for k, v in crossload_differences.items() if """hidden""" not in k}
  __lowerCAmelCase    							=						{k: v for k, v in crossload_differences.items() if """hidden""" in k}
  if len(SCREAMING_SNAKE_CASE__							) == 0 and architectures is not None:
   raise ValueError(
       f"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
       """ output was found. All outputs start with 'hidden'"""							)
  __lowerCAmelCase    							=						max(output_differences.values()							) if output_differences else 0.0
  __lowerCAmelCase    							=						max(hidden_differences.values()							)
  if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
   raise ValueError(
       """The cross-loaded TensorFlow model has different outputs, something went wrong!\n"""
       + f"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
       + """\n""".join([f"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]							)
       + f"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
       + """\n""".join([f"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]							)							)

  # Save the weights in a TF format (if needed) and confirms that the results are still good
  __lowerCAmelCase    							=						os.path.join(self._local_dir				,	SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						os.path.join(self._local_dir				,	SCREAMING_SNAKE_CASE__							)
  if (not os.path.exists(SCREAMING_SNAKE_CASE__							) and not os.path.exists(SCREAMING_SNAKE_CASE__							)) or self._new_weights:
   tf_from_pt_model.save_pretrained(self._local_dir							)
  del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

  __lowerCAmelCase    							=						tf_class.from_pretrained(self._local_dir							)
  __lowerCAmelCase    							=						tf_model(**SCREAMING_SNAKE_CASE__				,	output_hidden_states=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						self.find_pt_tf_differences(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						{k: v for k, v in conversion_differences.items() if """hidden""" not in k}
  __lowerCAmelCase    							=						{k: v for k, v in conversion_differences.items() if """hidden""" in k}
  if len(SCREAMING_SNAKE_CASE__							) == 0 and architectures is not None:
   raise ValueError(
       f"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
       """ output was found. All outputs start with 'hidden'"""							)
  __lowerCAmelCase    							=						max(output_differences.values()							) if output_differences else 0.0
  __lowerCAmelCase    							=						max(hidden_differences.values()							)
  if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
   raise ValueError(
       """The converted TensorFlow model has different outputs, something went wrong!\n"""
       + f"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
       + """\n""".join([f"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]							)
       + f"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
       + """\n""".join([f"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]							)							)

  __lowerCAmelCase    							=						"""Update TF weights""" if self._new_weights else """Add TF weights"""
  if self._push:
   repo.git_add(auto_lfs_track=SCREAMING_SNAKE_CASE__							)
   repo.git_commit(SCREAMING_SNAKE_CASE__							)
   repo.git_push(blocking=SCREAMING_SNAKE_CASE__							)  # this prints a progress bar with the upload
   self._logger.warning(f"""TF weights pushed into {self._model_name}"""							)
  elif not self._no_pr:
   self._logger.warning("""Uploading the weights into a new PR..."""							)
   __lowerCAmelCase    							=						(
       """Model converted by the [`transformers`' `pt_to_tf`"""
       """ CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). """
       """All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"""
       f"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
       f"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
       f"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
       f"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
   )
   if self._max_error > MAX_ERROR:
    commit_descrition += (
        f"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
    )
   if self._extra_commit_description:
    commit_descrition += "\n\n" + self._extra_commit_description

   # sharded model -> adds all related files (index and .h5 shards)
   if os.path.exists(SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase    							=						[
        CommitOperationAdd(path_in_repo=SCREAMING_SNAKE_CASE__				,	path_or_fileobj=SCREAMING_SNAKE_CASE__							)
    ]
    for shard_path in tf.io.gfile.glob(self._local_dir + """/tf_model-*.h5"""							):
     operations += [
         CommitOperationAdd(path_in_repo=os.path.basename(SCREAMING_SNAKE_CASE__							)				,	path_or_fileobj=SCREAMING_SNAKE_CASE__							)
     ]
   else:
    __lowerCAmelCase    							=						[CommitOperationAdd(path_in_repo=SCREAMING_SNAKE_CASE__				,	path_or_fileobj=SCREAMING_SNAKE_CASE__							)]

   __lowerCAmelCase    							=						create_commit(
       repo_id=self._model_name				,	operations=SCREAMING_SNAKE_CASE__				,	commit_message=SCREAMING_SNAKE_CASE__				,	commit_description=SCREAMING_SNAKE_CASE__				,	repo_type="""model"""				,	create_pr=SCREAMING_SNAKE_CASE__				,	).pr_url
   self._logger.warning(f"""PR open in {hub_pr_url}"""							)







'''simple docstring'''

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()







@skip_mps
class     _lowercase					(    UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							unittest.TestCase      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	int				     = StableDiffusionModelEditingPipeline
 _SCREAMING_SNAKE_CASE					:	List[Any]				     = TEXT_TO_IMAGE_PARAMS
 _SCREAMING_SNAKE_CASE					:	Tuple				     = TEXT_TO_IMAGE_BATCH_PARAMS
 _SCREAMING_SNAKE_CASE					:	List[Any]				     = TEXT_TO_IMAGE_IMAGE_PARAMS
 _SCREAMING_SNAKE_CASE					:	Optional[int]				     = TEXT_TO_IMAGE_IMAGE_PARAMS
 def a (				self       :     Tuple							)  ->   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()
  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				,	)
  torch.manual_seed(0							)
  __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    							=						{
      """unet""": unet,
      """scheduler""": scheduler,
      """vae""": vae,
      """text_encoder""": text_encoder,
      """tokenizer""": tokenizer,
      """safety_checker""": None,
      """feature_extractor""": None,
  }
  return components
 def a (				self       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     Dict=0							)  ->   Tuple:
  __lowerCAmelCase    							=						torch.manual_seed(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						{
      """prompt""": """A field of roses""",
      """generator""": generator,
      # Setting height and width to None to prevent OOMs on CPU.
      """height""": None,
      """width""": None,
      """num_inference_steps""": 2,
      """guidance_scale""": 6.0,
      """output_type""": """numpy""",
  }
  return inputs
 def a (				self       :     int							)  ->   Optional[int]:
  __lowerCAmelCase    							=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  __lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
  sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__							).images
  __lowerCAmelCase    							=						image[0, -3:, -3:, -1]
  assert image.shape == (1, 64, 64, 3)

  __lowerCAmelCase    							=						np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]							)

  assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
 def a (				self       :     Dict							)  ->   Tuple:
  __lowerCAmelCase    							=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  __lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
  sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						"""french fries"""
  __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__				,	negative_prompt=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						output.images
  __lowerCAmelCase    							=						image[0, -3:, -3:, -1]

  assert image.shape == (1, 64, 64, 3)

  __lowerCAmelCase    							=						np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]							)

  assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
 def a (				self       :     Union[str, Any]							)  ->   int:
  __lowerCAmelCase    							=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  __lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						EulerAncestralDiscreteScheduler(
      beta_start=0.0_0_0_8_5				,	beta_end=0.0_1_2				,	beta_schedule="""scaled_linear"""							)
  __lowerCAmelCase    							=						StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
  sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

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

  assert image.shape == (1, 64, 64, 3)

  __lowerCAmelCase    							=						np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]							)

  assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
 def a (				self       :     Optional[int]							)  ->   Optional[int]:
  __lowerCAmelCase    							=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  __lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						PNDMScheduler()
  __lowerCAmelCase    							=						StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
  sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
  # the pipeline does not expect pndm so test if it raises error.
  with self.assertRaises(SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__							).images
 def a (				self       :     Tuple							)  ->   List[str]:
  super().test_inference_batch_single_identical(expected_max_diff=5e-3							)





 def a (				self       :     Optional[Any]							)  ->   List[str]:
  super().test_attention_slicing_forward_pass(expected_max_diff=5e-3							)







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


 '''simple docstring'''






 def a (				self       :     Optional[int]							)  ->   List[Any]:
  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()
 def a (				self       :     Dict				,	SCREAMING_SNAKE_CASE__       :     Dict=0							)  ->   int:
  __lowerCAmelCase    							=						torch.manual_seed(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						{
      """prompt""": """A field of roses""",
      """generator""": generator,
      """num_inference_steps""": 3,
      """guidance_scale""": 7.5,
      """output_type""": """numpy""",
  }
  return inputs
 def a (				self       :     str							)  ->   Tuple:
  __lowerCAmelCase    							=						"""CompVis/stable-diffusion-v1-4"""
  __lowerCAmelCase    							=						StableDiffusionModelEditingPipeline.from_pretrained(SCREAMING_SNAKE_CASE__				,	safety_checker=SCREAMING_SNAKE_CASE__							)
  pipe.to(SCREAMING_SNAKE_CASE__							)
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)
  pipe.enable_attention_slicing()

  __lowerCAmelCase    							=						self.get_inputs()
  __lowerCAmelCase    							=						pipe(**SCREAMING_SNAKE_CASE__							).images
  __lowerCAmelCase    							=						image[0, -3:, -3:, -1].flatten()

  assert image.shape == (1, 5_12, 5_12, 3)

  __lowerCAmelCase    							=						np.array(
      [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]							)

  assert np.abs(expected_slice - image_slice							).max() < 1e-2

  # make sure image changes after editing
  pipe.edit_model("""A pack of roses"""				,	"""A pack of blue roses"""							)

  __lowerCAmelCase    							=						pipe(**SCREAMING_SNAKE_CASE__							).images
  __lowerCAmelCase    							=						image[0, -3:, -3:, -1].flatten()

  assert image.shape == (1, 5_12, 5_12, 3)

  assert np.abs(expected_slice - image_slice							).max() > 1e-1





 def a (				self       :     Tuple							)  ->   Optional[Any]:
  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()

  __lowerCAmelCase    							=						"""CompVis/stable-diffusion-v1-4"""
  __lowerCAmelCase    							=						DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE__				,	subfolder="""scheduler"""							)
  __lowerCAmelCase    							=						StableDiffusionModelEditingPipeline.from_pretrained(
      SCREAMING_SNAKE_CASE__				,	scheduler=SCREAMING_SNAKE_CASE__				,	safety_checker=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						pipe.to(SCREAMING_SNAKE_CASE__							)
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)
  pipe.enable_attention_slicing(1							)
  pipe.enable_sequential_cpu_offload()

  __lowerCAmelCase    							=						self.get_inputs()
  __lowerCAmelCase    							=						pipe(**SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						torch.cuda.max_memory_allocated()
  # make sure that less than 4.4 GB is allocated
  assert mem_bytes < 4.4 * 10**9






