

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      for attribute in key.split('''.'''						):
            __lowerCAmelCase					:						Union[str, Any]					=					getattr(lowercase__   ,	lowercase__						)

      if weight_type is not None:
            __lowerCAmelCase					:						int					=					getattr(lowercase__   ,	lowercase__						).shape
      else:
            __lowerCAmelCase					:						Dict					=					hf_pointer.shape

      assert hf_shape == value.shape, (
          f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
          f""" {value.shape} for {full_name}"""
      )

      if weight_type == "weight":
            __lowerCAmelCase					:						Optional[int]					=					value
      elif weight_type == "weight_g":
            __lowerCAmelCase					:						Optional[int]					=					value
      elif weight_type == "weight_v":
            __lowerCAmelCase					:						List[str]					=					value
      elif weight_type == "bias":
            __lowerCAmelCase					:						Dict					=					value
      else:
            __lowerCAmelCase					:						Dict					=					value

      logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						int					=					[]
      __lowerCAmelCase					:						Optional[Any]					=					fairseq_model.state_dict()

      __lowerCAmelCase					:						Any					=					hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            __lowerCAmelCase					:						int					=					False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	hf_model.config.feat_extract_norm == '''group'''   ,	)
                  __lowerCAmelCase					:						int					=					True
            else:
                  for key, mapped_key in MAPPING.items():
                        __lowerCAmelCase					:						str					=					'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                        if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
                              __lowerCAmelCase					:						Union[str, Any]					=					True
                              if "*" in mapped_key:
                                    __lowerCAmelCase					:						int					=					name.split(lowercase__						)[0].split('''.'''						)[-2]
                                    __lowerCAmelCase					:						List[Any]					=					mapped_key.replace('''*'''   ,	lowercase__						)
                              if "weight_g" in name:
                                    __lowerCAmelCase					:						Optional[int]					=					'''weight_g'''
                              elif "weight_v" in name:
                                    __lowerCAmelCase					:						Union[str, Any]					=					'''weight_v'''
                              elif "weight" in name:
                                    __lowerCAmelCase					:						Any					=					'''weight'''
                              elif "bias" in name:
                                    __lowerCAmelCase					:						Any					=					'''bias'''
                              else:
                                    __lowerCAmelCase					:						Optional[Any]					=					None
                              set_recursively(lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						)
                        continue
            if not is_used:
                  unused_weights.append(lowercase__						)

      logger.warning(f"""Unused weights: {unused_weights}"""						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Tuple					=					full_name.split('''conv_layers.'''						)[-1]
      __lowerCAmelCase					:						Dict					=					name.split('''.'''						)
      __lowerCAmelCase					:						Any					=					int(items[0]						)
      __lowerCAmelCase					:						Union[str, Any]					=					int(items[1]						)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      f"""{full_name} has size {value.shape}, but"""
                      f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
                  )
                  __lowerCAmelCase					:						Optional[int]					=					value
                  logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      f"""{full_name} has size {value.shape}, but"""
                      f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
                  )
                  __lowerCAmelCase					:						Optional[int]					=					value
                  logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                      " found."
                  )
                  __lowerCAmelCase					:						Any					=					value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      f"""{full_name} has size {value.shape}, but"""
                      f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
                  )
                  __lowerCAmelCase					:						int					=					value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
      else:
            unused_weights.append(lowercase__						)







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						int					=					SEWConfig()
      if is_finetuned:
            __lowerCAmelCase					:						Optional[int]					=					model.wav_encoder.wav_model.cfg
      else:
            __lowerCAmelCase					:						List[str]					=					model.cfg

      __lowerCAmelCase					:						Dict					=					fs_config.conv_bias
      __lowerCAmelCase					:						Any					=					eval(fs_config.conv_feature_layers						)
      __lowerCAmelCase					:						Optional[Any]					=					[x[0] for x in conv_layers]
      __lowerCAmelCase					:						int					=					[x[1] for x in conv_layers]
      __lowerCAmelCase					:						List[Any]					=					[x[2] for x in conv_layers]
      __lowerCAmelCase					:						str					=					'''gelu'''
      __lowerCAmelCase					:						List[Any]					=					'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
      __lowerCAmelCase					:						Any					=					0.0
      __lowerCAmelCase					:						str					=					fs_config.activation_fn.name
      __lowerCAmelCase					:						Tuple					=					fs_config.encoder_embed_dim
      __lowerCAmelCase					:						List[Any]					=					0.0_2
      __lowerCAmelCase					:						List[Any]					=					fs_config.encoder_ffn_embed_dim
      __lowerCAmelCase					:						int					=					1E-5
      __lowerCAmelCase					:						List[str]					=					fs_config.encoder_layerdrop
      __lowerCAmelCase					:						Tuple					=					fs_config.encoder_attention_heads
      __lowerCAmelCase					:						Union[str, Any]					=					fs_config.conv_pos_groups
      __lowerCAmelCase					:						int					=					fs_config.conv_pos
      __lowerCAmelCase					:						Union[str, Any]					=					len(lowercase__						)
      __lowerCAmelCase					:						Optional[int]					=					fs_config.encoder_layers
      __lowerCAmelCase					:						List[str]					=					fs_config.squeeze_factor

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            __lowerCAmelCase					:						Dict					=					model.cfg
            __lowerCAmelCase					:						List[Any]					=					fs_config.final_dropout
            __lowerCAmelCase					:						List[str]					=					fs_config.layerdrop
      __lowerCAmelCase					:						Optional[Any]					=					fs_config.activation_dropout
      __lowerCAmelCase					:						Optional[int]					=					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      __lowerCAmelCase					:						List[str]					=					fs_config.attention_dropout
      __lowerCAmelCase					:						List[Any]					=					fs_config.dropout_input
      __lowerCAmelCase					:						List[str]					=					fs_config.dropout
      __lowerCAmelCase					:						Dict					=					fs_config.mask_channel_length
      __lowerCAmelCase					:						Optional[Any]					=					fs_config.mask_channel_prob
      __lowerCAmelCase					:						str					=					fs_config.mask_length
      __lowerCAmelCase					:						Dict					=					fs_config.mask_prob

      __lowerCAmelCase					:						Optional[int]					=					'''Wav2Vec2FeatureExtractor'''
      __lowerCAmelCase					:						Optional[Any]					=					'''Wav2Vec2CTCTokenizer'''

      return config







@torch.no_grad()
def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=None   ,	lowercase__=None   ,	lowercase__=True						):

      if is_finetuned:
            __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						int					=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]   ,	arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
      else:
            __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						Optional[Any]					=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

      if config_path is not None:
            __lowerCAmelCase					:						Union[str, Any]					=					SEWConfig.from_pretrained(lowercase__						)
      else:
            __lowerCAmelCase					:						Tuple					=					convert_config(model[0]   ,	lowercase__						)
      __lowerCAmelCase					:						Optional[int]					=					model[0].eval()

      __lowerCAmelCase					:						Dict					=					True if config.feat_extract_norm == '''layer''' else False
      __lowerCAmelCase					:						Dict					=					WavaVecaFeatureExtractor(
          feature_size=1   ,	sampling_rate=1_6_0_0_0   ,	padding_value=0   ,	do_normalize=lowercase__   ,	return_attention_mask=lowercase__   ,	)

      if is_finetuned:
            if dict_path:
                  __lowerCAmelCase					:						Optional[int]					=					Dictionary.load(lowercase__						)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  __lowerCAmelCase					:						Any					=					target_dict.pad_index
                  __lowerCAmelCase					:						Tuple					=					target_dict.bos_index
                  __lowerCAmelCase					:						Any					=					target_dict.pad_index
                  __lowerCAmelCase					:						Optional[Any]					=					target_dict.bos_index
                  __lowerCAmelCase					:						Dict					=					target_dict.eos_index
                  __lowerCAmelCase					:						List[str]					=					len(target_dict.symbols						)
                  __lowerCAmelCase					:						Any					=					os.path.join(lowercase__   ,	'''vocab.json'''						)
                  if not os.path.isdir(lowercase__						):
                        logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowercase__						)						)
                        return
                  os.makedirs(lowercase__   ,	exist_ok=lowercase__						)
                  with open(lowercase__   ,	'''w'''   ,	encoding='''utf-8'''						) as vocab_handle:
                        json.dump(target_dict.indices   ,	lowercase__						)
                  __lowerCAmelCase					:						Dict					=					WavaVecaCTCTokenizer(
                      lowercase__   ,	unk_token=target_dict.unk_word   ,	pad_token=target_dict.pad_word   ,	bos_token=target_dict.bos_word   ,	eos_token=target_dict.eos_word   ,	word_delimiter_token='''|'''   ,	do_lower_case=lowercase__   ,	)
                  __lowerCAmelCase					:						Any					=					WavaVecaProcessor(feature_extractor=lowercase__   ,	tokenizer=lowercase__						)
                  processor.save_pretrained(lowercase__						)

            __lowerCAmelCase					:						Optional[int]					=					SEWForCTC(lowercase__						)
      else:
            __lowerCAmelCase					:						Optional[Any]					=					SEWModel(lowercase__						)
            feature_extractor.save_pretrained(lowercase__						)

      recursively_load_weights(lowercase__   ,	lowercase__   ,	lowercase__						)

      hf_model.save_pretrained(lowercase__						)


if __name__ == "__main__":
 _UpperCamelCase 			=		argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
 parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 parser.add_argument(
     "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
 )
 _UpperCamelCase 			=		parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )





import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class   __lowercase      (_UpperCAmelCase		,   unittest.TestCase ):
 _UpperCamelCase			  =  ConsistencyModelPipeline
 _UpperCamelCase			  =  UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 _UpperCamelCase			  =  UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

 # Override required_optional_params to remove num_images_per_prompt
 _UpperCamelCase			  =  frozenset(
     [
         """num_inference_steps""",
         """generator""",
         """latents""",
         """output_type""",
         """return_dict""",
         """callback""",
         """callback_steps""",
     ] )
 @property
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					UNetaDModel.from_pretrained(
           '''diffusers/consistency-models-test'''      ,							subfolder='''test_unet'''      ,							)
       return unet
 @property
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					UNetaDModel.from_pretrained(
           '''diffusers/consistency-models-test'''      ,							subfolder='''test_unet_class_cond'''      ,							)
       return unet
 def 	UpperCamelCase__  (							self      ,							A_=False					)					->Dict:



       '''simple docstring'''





       if class_cond:
             __lowerCAmelCase					:						List[str]					=					self.dummy_cond_unet
       else:
             __lowerCAmelCase					:						Optional[Any]					=					self.dummy_uncond_unet

       # Default to CM multistep sampler
       __lowerCAmelCase					:						List[str]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)

       __lowerCAmelCase					:						Dict					=					{
           '''unet''': unet,
           '''scheduler''': scheduler,
       }

       return components
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_=0					)					->Tuple:



       '''simple docstring'''





       if str(A_					).startswith('''mps'''					):
             __lowerCAmelCase					:						str					=					torch.manual_seed(A_					)
       else:
             __lowerCAmelCase					:						Dict					=					torch.Generator(device=A_					).manual_seed(A_					)

       __lowerCAmelCase					:						Tuple					=					{
           '''batch_size''': 1,
           '''num_inference_steps''': None,
           '''timesteps''': [22, 0],
           '''generator''': generator,
           '''output_type''': '''np''',
       }

       return inputs
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       __lowerCAmelCase					:						Tuple					=					self.get_dummy_components()
       __lowerCAmelCase					:						Union[str, Any]					=					ConsistencyModelPipeline(**A_					)
       __lowerCAmelCase					:						List[str]					=					pipe.to(A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Any					=					self.get_dummy_inputs(A_					)
       __lowerCAmelCase					:						int					=					pipe(**A_					).images
       assert image.shape == (1, 32, 32, 3)

       __lowerCAmelCase					:						Tuple					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						str					=					np.array([0.3_572, 0.6_273, 0.4_031, 0.3_961, 0.4_321, 0.5_730, 0.5_266, 0.4_780, 0.5_004]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       __lowerCAmelCase					:						str					=					self.get_dummy_components(class_cond=A_					)
       __lowerCAmelCase					:						List[str]					=					ConsistencyModelPipeline(**A_					)
       __lowerCAmelCase					:						List[Any]					=					pipe.to(A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Optional[Any]					=					self.get_dummy_inputs(A_					)
       __lowerCAmelCase					:						Optional[int]					=					0
       __lowerCAmelCase					:						int					=					pipe(**A_					).images
       assert image.shape == (1, 32, 32, 3)

       __lowerCAmelCase					:						Optional[int]					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						List[str]					=					np.array([0.3_572, 0.6_273, 0.4_031, 0.3_961, 0.4_321, 0.5_730, 0.5_266, 0.4_780, 0.5_004]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       __lowerCAmelCase					:						Union[str, Any]					=					self.get_dummy_components()
       __lowerCAmelCase					:						List[Any]					=					ConsistencyModelPipeline(**A_					)
       __lowerCAmelCase					:						int					=					pipe.to(A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Tuple					=					self.get_dummy_inputs(A_					)
       __lowerCAmelCase					:						Any					=					1
       __lowerCAmelCase					:						List[Any]					=					None
       __lowerCAmelCase					:						Dict					=					pipe(**A_					).images
       assert image.shape == (1, 32, 32, 3)

       __lowerCAmelCase					:						Any					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						List[Any]					=					np.array([0.5_004, 0.5_004, 0.4_994, 0.5_008, 0.4_976, 0.5_018, 0.4_990, 0.4_982, 0.4_987]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3





 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       __lowerCAmelCase					:						Optional[Any]					=					self.get_dummy_components(class_cond=A_					)
       __lowerCAmelCase					:						List[str]					=					ConsistencyModelPipeline(**A_					)
       __lowerCAmelCase					:						Union[str, Any]					=					pipe.to(A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Any					=					self.get_dummy_inputs(A_					)
       __lowerCAmelCase					:						List[str]					=					1
       __lowerCAmelCase					:						Dict					=					None
       __lowerCAmelCase					:						Tuple					=					0
       __lowerCAmelCase					:						Dict					=					pipe(**A_					).images
       assert image.shape == (1, 32, 32, 3)

       __lowerCAmelCase					:						List[Any]					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						Any					=					np.array([0.5_004, 0.5_004, 0.4_994, 0.5_008, 0.4_976, 0.5_018, 0.4_990, 0.4_982, 0.4_987]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3







@slow
@require_torch_gpu
class   __lowercase      (unittest.TestCase ):
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 def 	UpperCamelCase__  (							self      ,							A_=0      ,							A_=False      ,							A_="cpu"      ,							A_=torch.floataa      ,							A_=(1, 3, 64, 64)					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					torch.manual_seed(A_					)

       __lowerCAmelCase					:						Tuple					=					{
           '''num_inference_steps''': None,
           '''timesteps''': [22, 0],
           '''class_labels''': 0,
           '''generator''': generator,
           '''output_type''': '''np''',
       }

       if get_fixed_latents:
             __lowerCAmelCase					:						List[str]					=					self.get_fixed_latents(seed=A_      ,							device=A_      ,							dtype=A_      ,							shape=A_					)
             __lowerCAmelCase					:						Union[str, Any]					=					latents

       return inputs
 def 	UpperCamelCase__  (							self      ,							A_=0      ,							A_="cpu"      ,							A_=torch.floataa      ,							A_=(1, 3, 64, 64)					)					->Optional[int]:



       '''simple docstring'''





       if type(A_					) == str:
             __lowerCAmelCase					:						int					=					torch.device(A_					)
       __lowerCAmelCase					:						Optional[Any]					=					torch.Generator(device=A_					).manual_seed(A_					)
       __lowerCAmelCase					:						Union[str, Any]					=					randn_tensor(A_      ,							generator=A_      ,							device=A_      ,							dtype=A_					)
       return latents
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					UNetaDModel.from_pretrained('''diffusers/consistency_models'''      ,							subfolder='''diffusers_cd_imagenet64_l2'''					)
       __lowerCAmelCase					:						List[Any]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)
       __lowerCAmelCase					:						List[str]					=					ConsistencyModelPipeline(unet=A_      ,							scheduler=A_					)
       pipe.to(torch_device=A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						str					=					self.get_inputs()
       __lowerCAmelCase					:						Any					=					pipe(**A_					).images
       assert image.shape == (1, 64, 64, 3)

       __lowerCAmelCase					:						Dict					=					image[0, -3:, -3:, -1]

       __lowerCAmelCase					:						Optional[int]					=					np.array([0.0_888, 0.0_881, 0.0_666, 0.0_479, 0.0_292, 0.0_195, 0.0_201, 0.0_163, 0.0_254]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 2e-2
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					UNetaDModel.from_pretrained('''diffusers/consistency_models'''      ,							subfolder='''diffusers_cd_imagenet64_l2'''					)
       __lowerCAmelCase					:						Optional[Any]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)
       __lowerCAmelCase					:						List[str]					=					ConsistencyModelPipeline(unet=A_      ,							scheduler=A_					)
       pipe.to(torch_device=A_					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						List[Any]					=					self.get_inputs()
       __lowerCAmelCase					:						Tuple					=					1
       __lowerCAmelCase					:						Optional[Any]					=					None
       __lowerCAmelCase					:						str					=					pipe(**A_					).images
       assert image.shape == (1, 64, 64, 3)

       __lowerCAmelCase					:						Optional[int]					=					image[0, -3:, -3:, -1]

       __lowerCAmelCase					:						List[Any]					=					np.array([0.0_340, 0.0_152, 0.0_063, 0.0_267, 0.0_221, 0.0_107, 0.0_416, 0.0_186, 0.0_217]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 2e-2
 @require_torch_a
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					UNetaDModel.from_pretrained('''diffusers/consistency_models'''      ,							subfolder='''diffusers_cd_imagenet64_l2'''					)
       __lowerCAmelCase					:						List[str]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)
       __lowerCAmelCase					:						Union[str, Any]					=					ConsistencyModelPipeline(unet=A_      ,							scheduler=A_					)
       pipe.to(torch_device=A_      ,							torch_dtype=torch.floataa					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Any					=					self.get_inputs(get_fixed_latents=A_      ,							device=A_					)
       # Ensure usage of flash attention in torch 2.0
       with sdp_kernel(enable_flash=A_      ,							enable_math=A_      ,							enable_mem_efficient=A_					):
             __lowerCAmelCase					:						Dict					=					pipe(**A_					).images
       assert image.shape == (1, 64, 64, 3)

       __lowerCAmelCase					:						Dict					=					image[0, -3:, -3:, -1]

       __lowerCAmelCase					:						Optional[int]					=					np.array([0.1_875, 0.1_428, 0.1_289, 0.2_151, 0.2_092, 0.1_477, 0.1_877, 0.1_641, 0.1_353]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3





 @require_torch_a
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[Any]					=					UNetaDModel.from_pretrained('''diffusers/consistency_models'''      ,							subfolder='''diffusers_cd_imagenet64_l2'''					)
       __lowerCAmelCase					:						List[Any]					=					CMStochasticIterativeScheduler(
           num_train_timesteps=40      ,							sigma_min=0.002      ,							sigma_max=80.0      ,							)
       __lowerCAmelCase					:						Union[str, Any]					=					ConsistencyModelPipeline(unet=A_      ,							scheduler=A_					)
       pipe.to(torch_device=A_      ,							torch_dtype=torch.floataa					)
       pipe.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Union[str, Any]					=					self.get_inputs(get_fixed_latents=A_      ,							device=A_					)
       __lowerCAmelCase					:						Any					=					1
       __lowerCAmelCase					:						int					=					None
       # Ensure usage of flash attention in torch 2.0
       with sdp_kernel(enable_flash=A_      ,							enable_math=A_      ,							enable_mem_efficient=A_					):
             __lowerCAmelCase					:						int					=					pipe(**A_					).images
       assert image.shape == (1, 64, 64, 3)

       __lowerCAmelCase					:						str					=					image[0, -3:, -3:, -1]

       __lowerCAmelCase					:						Any					=					np.array([0.1_663, 0.1_948, 0.2_275, 0.1_680, 0.1_204, 0.1_245, 0.1_858, 0.1_338, 0.2_095]					)

       assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3


