

import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class       __snake_case  (    lowerCAmelCase__     , unittest.TestCase       ):
  __lowerCAmelCase     :	Optional[Any]						 =  AudioLDMPipeline
  __lowerCAmelCase     :	Optional[int]						 =  TEXT_TO_AUDIO_PARAMS
  __lowerCAmelCase     :	Union[str, Any]						 =  TEXT_TO_AUDIO_BATCH_PARAMS
  __lowerCAmelCase     :	Union[str, Any]						 =  frozenset(
      [
          'num_inference_steps',
          'num_waveforms_per_prompt',
          'generator',
          'latents',
          'output_type',
          'return_dict',
          'callback',
          'callback_steps',
      ]       )

  def 						lowerCAmelCase__     (					self):
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE_									=						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, 64)						,		class_embed_type='simple_projection'						,		projection_class_embeddings_input_dim=32						,		class_embeddings_concat=_A						,		)
      SCREAMING_SNAKE_CASE_									=						DDIMScheduler(
          beta_start=0.0_0_0_8_5						,		beta_end=0.0_1_2						,		beta_schedule='scaled_linear'						,		clip_sample=_A						,		set_alpha_to_one=_A						,		)
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE_									=						AutoencoderKL(
          block_out_channels=[32, 64]						,		in_channels=1						,		out_channels=1						,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,		latent_channels=4						,		)
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE_									=						ClapTextConfig(
          bos_token_id=0						,		eos_token_id=2						,		hidden_size=32						,		intermediate_size=37						,		layer_norm_eps=1E-05						,		num_attention_heads=4						,		num_hidden_layers=5						,		pad_token_id=1						,		vocab_size=1000						,		projection_dim=32						,		)
      SCREAMING_SNAKE_CASE_									=						ClapTextModelWithProjection(_A)
      SCREAMING_SNAKE_CASE_									=						RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'						,		model_max_length=77)

      SCREAMING_SNAKE_CASE_									=						SpeechTaHifiGanConfig(
          model_in_dim=8						,		sampling_rate=16000						,		upsample_initial_channel=16						,		upsample_rates=[2, 2]						,		upsample_kernel_sizes=[4, 4]						,		resblock_kernel_sizes=[3, 7]						,		resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]						,		normalize_before=_A						,		)

      SCREAMING_SNAKE_CASE_									=						SpeechTaHifiGan(_A)

      SCREAMING_SNAKE_CASE_									=						{
          'unet': unet,
          'scheduler': scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'vocoder': vocoder,
      }
      return components

  def 						lowerCAmelCase__     (					self						,		_A						,		_A=0):
      if str(_A).startswith('mps'):
          SCREAMING_SNAKE_CASE_									=						torch.manual_seed(_A)
      else:
          SCREAMING_SNAKE_CASE_									=						torch.Generator(device=_A).manual_seed(_A)
      SCREAMING_SNAKE_CASE_									=						{
          'prompt': 'A hammer hitting a wooden surface',
          'generator': generator,
          'num_inference_steps': 2,
          'guidance_scale': 6.0,
      }
      return inputs

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						'cpu'  # ensure determinism for the device-dependent torch.Generator

      SCREAMING_SNAKE_CASE_									=						self.get_dummy_components()
      SCREAMING_SNAKE_CASE_									=						AudioLDMPipeline(**_A)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.to(_A)
      audioldm_pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_									=						self.get_dummy_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(**_A)
      SCREAMING_SNAKE_CASE_									=						output.audios[0]

      assert audio.ndim == 1
      assert len(_A) == 256

      SCREAMING_SNAKE_CASE_									=						audio[:10]
      SCREAMING_SNAKE_CASE_									=						np.array(
          [-0.0_0_5_0, 0.0_0_5_0, -0.0_0_6_0, 0.0_0_3_3, -0.0_0_2_6, 0.0_0_3_3, -0.0_0_2_7, 0.0_0_3_3, -0.0_0_2_8, 0.0_0_3_3])

      assert np.abs(audio_slice - expected_slice).max() < 1E-2

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_components()
      SCREAMING_SNAKE_CASE_									=						AudioLDMPipeline(**_A)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.to(_A)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.to(_A)
      audioldm_pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_									=						self.get_dummy_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						3 * [inputs['prompt']]

      # forward
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(**_A)
      SCREAMING_SNAKE_CASE_									=						output.audios[0]

      SCREAMING_SNAKE_CASE_									=						self.get_dummy_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						3 * [inputs.pop('prompt')]

      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.tokenizer(
          _A						,		padding='max_length'						,		max_length=audioldm_pipe.tokenizer.model_max_length						,		truncation=_A						,		return_tensors='pt'						,		)
      SCREAMING_SNAKE_CASE_									=						text_inputs['input_ids'].to(_A)

      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.text_encoder(
          _A						,		)
      SCREAMING_SNAKE_CASE_									=						prompt_embeds.text_embeds
      # additional L_2 normalization over each hidden-state
      SCREAMING_SNAKE_CASE_									=						F.normalize(_A						,		dim=-1)

      SCREAMING_SNAKE_CASE_									=						prompt_embeds

      # forward
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(**_A)
      SCREAMING_SNAKE_CASE_									=						output.audios[0]

      assert np.abs(audio_a - audio_a).max() < 1E-2

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_components()
      SCREAMING_SNAKE_CASE_									=						AudioLDMPipeline(**_A)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.to(_A)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.to(_A)
      audioldm_pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_									=						self.get_dummy_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						3 * ['this is a negative prompt']
      SCREAMING_SNAKE_CASE_									=						negative_prompt
      SCREAMING_SNAKE_CASE_									=						3 * [inputs['prompt']]

      # forward
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(**_A)
      SCREAMING_SNAKE_CASE_									=						output.audios[0]

      SCREAMING_SNAKE_CASE_									=						self.get_dummy_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						3 * [inputs.pop('prompt')]

      SCREAMING_SNAKE_CASE_									=						[]
      for p in [prompt, negative_prompt]:
          SCREAMING_SNAKE_CASE_									=						audioldm_pipe.tokenizer(
              _A						,		padding='max_length'						,		max_length=audioldm_pipe.tokenizer.model_max_length						,		truncation=_A						,		return_tensors='pt'						,		)
          SCREAMING_SNAKE_CASE_									=						text_inputs['input_ids'].to(_A)

          SCREAMING_SNAKE_CASE_									=						audioldm_pipe.text_encoder(
              _A						,		)
          SCREAMING_SNAKE_CASE_									=						text_embeds.text_embeds
          # additional L_2 normalization over each hidden-state
          SCREAMING_SNAKE_CASE_									=						F.normalize(_A						,		dim=-1)

          embeds.append(_A)

      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						embeds

      # forward
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(**_A)
      SCREAMING_SNAKE_CASE_									=						output.audios[0]

      assert np.abs(audio_a - audio_a).max() < 1E-2

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						'cpu'  # ensure determinism for the device-dependent torch.Generator
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_components()
      SCREAMING_SNAKE_CASE_									=						PNDMScheduler(skip_prk_steps=_A)
      SCREAMING_SNAKE_CASE_									=						AudioLDMPipeline(**_A)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.to(_A)
      audioldm_pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_									=						self.get_dummy_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						'egg cracking'
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(**_A						,		negative_prompt=_A)
      SCREAMING_SNAKE_CASE_									=						output.audios[0]

      assert audio.ndim == 1
      assert len(_A) == 256

      SCREAMING_SNAKE_CASE_									=						audio[:10]
      SCREAMING_SNAKE_CASE_									=						np.array(
          [-0.0_0_5_1, 0.0_0_5_0, -0.0_0_6_0, 0.0_0_3_4, -0.0_0_2_6, 0.0_0_3_3, -0.0_0_2_7, 0.0_0_3_3, -0.0_0_2_8, 0.0_0_3_2])

      assert np.abs(audio_slice - expected_slice).max() < 1E-2

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						'cpu'  # ensure determinism for the device-dependent torch.Generator
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_components()
      SCREAMING_SNAKE_CASE_									=						PNDMScheduler(skip_prk_steps=_A)
      SCREAMING_SNAKE_CASE_									=						AudioLDMPipeline(**_A)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.to(_A)
      audioldm_pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_									=						'A hammer hitting a wooden surface'

      # test num_waveforms_per_prompt=1 (default)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(_A						,		num_inference_steps=2).audios

      assert audios.shape == (1, 256)

      # test num_waveforms_per_prompt=1 (default) for batch of prompts
      SCREAMING_SNAKE_CASE_									=						2
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe([prompt] * batch_size						,		num_inference_steps=2).audios

      assert audios.shape == (batch_size, 256)

      # test num_waveforms_per_prompt for single prompt
      SCREAMING_SNAKE_CASE_									=						2
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(_A						,		num_inference_steps=2						,		num_waveforms_per_prompt=_A).audios

      assert audios.shape == (num_waveforms_per_prompt, 256)

      # test num_waveforms_per_prompt for batch of prompts
      SCREAMING_SNAKE_CASE_									=						2
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(
          [prompt] * batch_size						,		num_inference_steps=2						,		num_waveforms_per_prompt=_A).audios

      assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						'cpu'  # ensure determinism for the device-dependent torch.Generator
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_components()
      SCREAMING_SNAKE_CASE_									=						AudioLDMPipeline(**_A)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.to(_A)
      audioldm_pipe.set_progress_bar_config(disable=_A)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.vocoder.config.sampling_rate

      SCREAMING_SNAKE_CASE_									=						self.get_dummy_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(audio_length_in_s=0.0_1_6						,		**_A)
      SCREAMING_SNAKE_CASE_									=						output.audios[0]

      assert audio.ndim == 1
      assert len(_A) / vocoder_sampling_rate == 0.0_1_6

      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(audio_length_in_s=0.0_3_2						,		**_A)
      SCREAMING_SNAKE_CASE_									=						output.audios[0]

      assert audio.ndim == 1
      assert len(_A) / vocoder_sampling_rate == 0.0_3_2

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_dummy_components()
      SCREAMING_SNAKE_CASE_									=						AudioLDMPipeline(**_A)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.to(_A)
      audioldm_pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_									=						['hey']

      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(_A						,		num_inference_steps=1)
      SCREAMING_SNAKE_CASE_									=						output.audios.shape
      assert audio_shape == (1, 256)

      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.vocoder.config
      config.model_in_dim *= 2
      SCREAMING_SNAKE_CASE_									=						SpeechTaHifiGan(_A).to(_A)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(_A						,		num_inference_steps=1)
      SCREAMING_SNAKE_CASE_									=						output.audios.shape
      # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
      assert audio_shape == (1, 256)

  def 						lowerCAmelCase__     (					self):
      self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_A)

  def 						lowerCAmelCase__     (					self):
      self._test_inference_batch_single_identical(test_mean_pixel_difference=_A)


  @unittest.skipIf(
      torch_device != 'cuda' or not is_xformers_available()						,		reason='XFormers attention is only available with CUDA and `xformers` installed'						,		)
  def 						lowerCAmelCase__     (					self):
      self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_A)






@slow
class       __snake_case  (    unittest.TestCase       ):

  def 						lowerCAmelCase__     (					self):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

  def 						lowerCAmelCase__     (					self						,		_A						,		_A="cpu"						,		_A=torch.floataa						,		_A=0):
      SCREAMING_SNAKE_CASE_									=						torch.Generator(device=_A).manual_seed(_A)
      SCREAMING_SNAKE_CASE_									=						np.random.RandomState(_A).standard_normal((1, 8, 128, 16))
      SCREAMING_SNAKE_CASE_									=						torch.from_numpy(_A).to(device=_A						,		dtype=_A)
      SCREAMING_SNAKE_CASE_									=						{
          'prompt': 'A hammer hitting a wooden surface',
          'latents': latents,
          'generator': generator,
          'num_inference_steps': 3,
          'guidance_scale': 2.5,
      }
      return inputs

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						AudioLDMPipeline.from_pretrained('cvssp/audioldm')
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.to(_A)
      audioldm_pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_									=						self.get_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						25
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(**_A).audios[0]

      assert audio.ndim == 1
      assert len(_A) == 81920

      SCREAMING_SNAKE_CASE_									=						audio[77230:77240]
      SCREAMING_SNAKE_CASE_									=						np.array(
          [-0.4_8_8_4, -0.4_6_0_7, 0.0_0_2_3, 0.5_0_0_7, 0.5_8_9_6, 0.5_1_5_1, 0.3_8_1_3, -0.0_2_0_8, -0.3_6_8_7, -0.4_3_1_5])
      SCREAMING_SNAKE_CASE_									=						np.abs(expected_slice - audio_slice).max()
      assert max_diff < 1E-2


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						AudioLDMPipeline.from_pretrained('cvssp/audioldm')
      SCREAMING_SNAKE_CASE_									=						LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe.to(_A)
      audioldm_pipe.set_progress_bar_config(disable=_A)

      SCREAMING_SNAKE_CASE_									=						self.get_inputs(_A)
      SCREAMING_SNAKE_CASE_									=						audioldm_pipe(**_A).audios[0]

      assert audio.ndim == 1
      assert len(_A) == 81920

      SCREAMING_SNAKE_CASE_									=						audio[27780:27790]
      SCREAMING_SNAKE_CASE_									=						np.array([-0.2_1_3_1, -0.0_8_7_3, -0.0_1_2_4, -0.0_1_8_9, 0.0_5_6_9, 0.1_3_7_3, 0.1_8_8_3, 0.2_8_8_6, 0.3_2_9_7, 0.2_2_1_2])
      SCREAMING_SNAKE_CASE_									=						np.abs(expected_slice - audio_slice).max()
      assert max_diff < 3E-2





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


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

UpperCamelCase__  :      List[Any]			=    torch.device("cpu")


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						'http://images.cocodataset.org/val2017/000000039769.jpg'
    SCREAMING_SNAKE_CASE_									=						Image.open(requests.get(_SCREAMING_SNAKE_CASE ,   stream=_SCREAMING_SNAKE_CASE       ).raw       )
    return im


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]       )

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]       )

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]       )

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						dct.pop(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						val


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    for k in state_dict.keys():
        SCREAMING_SNAKE_CASE_									=						k
        if ".pwconv" in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.pwconv' ,   '.point_wise_conv'       )
        if ".dwconv" in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.dwconv' ,   '.depth_wise_conv'       )
        if ".Proj." in k:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('.Proj.' ,   '.proj.'       )
        if "patch_embed" in k_new:
            SCREAMING_SNAKE_CASE_									=						k_new.replace('patch_embed' ,   'swiftformer.patch_embed.patch_embedding'       )
        if "network" in k_new:
            SCREAMING_SNAKE_CASE_									=						k_new.split('.'       )
            if ls[2].isdigit():
                SCREAMING_SNAKE_CASE_									=						'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]       )
            else:
                SCREAMING_SNAKE_CASE_									=						k_new.replace('network' ,   'swiftformer.encoder.network'       )
        rename_keys.append((k, k_new)       )
    return rename_keys


@torch.no_grad()
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    SCREAMING_SNAKE_CASE_									=						1_000
    SCREAMING_SNAKE_CASE_									=						'huggingface/label-files'
    SCREAMING_SNAKE_CASE_									=						'imagenet-1k-id2label.json'
    SCREAMING_SNAKE_CASE_									=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   repo_type='dataset'       ) ,   'r'       )       )
    SCREAMING_SNAKE_CASE_									=						{int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE_									=						idalabel
    SCREAMING_SNAKE_CASE_									=						{v: k for k, v in idalabel.items()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        SCREAMING_SNAKE_CASE_									=						[3, 3, 6, 4]
        SCREAMING_SNAKE_CASE_									=						[48, 56, 112, 220]

    elif swiftformer_name == "swiftformer_s":
        SCREAMING_SNAKE_CASE_									=						[3, 3, 9, 6]
        SCREAMING_SNAKE_CASE_									=						[48, 64, 168, 224]

    elif swiftformer_name == "swiftformer_l1":
        SCREAMING_SNAKE_CASE_									=						[4, 3, 10, 5]
        SCREAMING_SNAKE_CASE_									=						[48, 96, 192, 384]

    elif swiftformer_name == "swiftformer_l3":
        SCREAMING_SNAKE_CASE_									=						[4, 4, 12, 6]
        SCREAMING_SNAKE_CASE_									=						[64, 128, 320, 512]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith('https'       ):
            SCREAMING_SNAKE_CASE_									=						torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE ,   map_location='cpu' ,   check_hash=_SCREAMING_SNAKE_CASE       )
        else:
            SCREAMING_SNAKE_CASE_									=						torch.load(_SCREAMING_SNAKE_CASE ,   map_location='cpu'       )
    SCREAMING_SNAKE_CASE_									=						checkpoint

    SCREAMING_SNAKE_CASE_									=						create_rename_keys(_SCREAMING_SNAKE_CASE       )
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

    # load HuggingFace model
    SCREAMING_SNAKE_CASE_									=						SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE       ).eval()
    hf_model.load_state_dict(_SCREAMING_SNAKE_CASE       )

    # prepare test inputs
    SCREAMING_SNAKE_CASE_									=						prepare_img()
    SCREAMING_SNAKE_CASE_									=						ViTImageProcessor.from_pretrained('preprocessor_config'       )
    SCREAMING_SNAKE_CASE_									=						processor(images=_SCREAMING_SNAKE_CASE ,   return_tensors='pt'       )

    # compare outputs from both models
    SCREAMING_SNAKE_CASE_									=						get_expected_output(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						hf_model(inputs['pixel_values']       ).logits

    assert hf_logits.shape == torch.Size([1, 1_000]       )
    assert torch.allclose(hf_logits[0, 0:5] ,   _SCREAMING_SNAKE_CASE ,   atol=1E-3       )

    Path(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
    print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""       )
    hf_model.save_pretrained(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       UpperCamelCase__  :      str			=    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--swiftformer_name",
           default="swiftformer_xs",
           choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
           type=str,
           help="Name of the SwiftFormer model you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default="./converted_outputs/",
           type=str,
           help="Path to the output PyTorch model directory.",
       )
       parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

       UpperCamelCase__  :      Union[str, Any]			=    parser.parse_args()
       convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


