




"""simple docstring"""






import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class lowerCAmelCase__	(     lowercase,	lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			CycleDiffusionPipeline
			lowerCamelCase__      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
			    """negative_prompt""",
			    """height""",
			    """width""",
			    """negative_prompt_embeds""",
			}
			lowerCamelCase__      =			PipelineTesterMixin.required_optional_params - {"""latents"""}
			lowerCamelCase__      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}						)
			lowerCamelCase__      =			IMAGE_TO_IMAGE_IMAGE_PARAMS
			lowerCamelCase__      =			IMAGE_TO_IMAGE_IMAGE_PARAMS
			def 					A_    (  self  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			Dict    =    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     :			Optional[Any]    =    DDIMScheduler(
							    beta_start=0.0_00_85  ,      beta_end=0.0_12  ,      beta_schedule='scaled_linear'  ,      num_train_timesteps=1000  ,      clip_sample=lowercase  ,      set_alpha_to_one=lowercase  ,      )
							torch.manual_seed(0  )
							_lowerCamelCase     :			Optional[Any]    =    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     :			Optional[Any]    =    CLIPTextConfig(
							    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  ,      )
							_lowerCamelCase     :			Any    =    CLIPTextModel(lowercase  )
							_lowerCamelCase     :			Optional[int]    =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

							_lowerCamelCase     :			int    =    {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}
							return components
			def 					A_    (  self  ,      lowercase  ,      lowercase=0  ):
							_lowerCamelCase     :			Union[str, Any]    =    floats_tensor((1, 3, 32, 32)  ,      rng=random.Random(lowercase  )  ).to(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    image / 2 + 0.5
							if str(lowercase  ).startswith('mps'  ):
											_lowerCamelCase     :			Optional[int]    =    torch.manual_seed(lowercase  )
							else:
											_lowerCamelCase     :			Union[str, Any]    =    torch.Generator(device=lowercase  ).manual_seed(lowercase  )
							_lowerCamelCase     :			List[Any]    =    {
							    'prompt': 'An astronaut riding an elephant',
							    'source_prompt': 'An astronaut riding a horse',
							    'image': image,
							    'generator': generator,
							    'num_inference_steps': 2,
							    'eta': 0.1,
							    'strength': 0.8,
							    'guidance_scale': 3,
							    'source_guidance_scale': 1,
							    'output_type': 'numpy',
							}
							return inputs
			def 					A_    (  self  ):
							_lowerCamelCase     :			Any    =    'cpu'  # ensure determinism for the device-dependent torch.Generator

							_lowerCamelCase     :			str    =    self.get_dummy_components()
							_lowerCamelCase     :			Tuple    =    CycleDiffusionPipeline(**lowercase  )
							_lowerCamelCase     :			int    =    pipe.to(lowercase  )
							pipe.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			List[Any]    =    self.get_dummy_inputs(lowercase  )
							_lowerCamelCase     :			List[Any]    =    pipe(**lowercase  )
							_lowerCamelCase     :			Optional[int]    =    output.images

							_lowerCamelCase     :			Optional[Any]    =    images[0, -3:, -3:, -1]

							assert images.shape == (1, 32, 32, 3)
							_lowerCamelCase     :			List[str]    =    np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79]  )

							assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
			@unittest.skipIf(torch_device != 'cuda'  ,      'This test requires a GPU'  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.get_dummy_components()
							for name, module in components.items():
											if hasattr(lowercase  ,      'half'  ):
															_lowerCamelCase     :			str    =    module.half()
							_lowerCamelCase     :			List[str]    =    CycleDiffusionPipeline(**lowercase  )
							_lowerCamelCase     :			Union[str, Any]    =    pipe.to(lowercase  )
							pipe.set_progress_bar_config(disable=lowercase  )

							_lowerCamelCase     :			List[Any]    =    self.get_dummy_inputs(lowercase  )
							_lowerCamelCase     :			Union[str, Any]    =    pipe(**lowercase  )
							_lowerCamelCase     :			Tuple    =    output.images

							_lowerCamelCase     :			int    =    images[0, -3:, -3:, -1]

							assert images.shape == (1, 32, 32, 3)
							_lowerCamelCase     :			str    =    np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16]  )

							assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
			@skip_mps
			def 					A_    (  self  ):
							return super().test_save_load_local()
			@unittest.skip('non-deterministic pipeline'  )
			def 					A_    (  self  ):
							return super().test_inference_batch_single_identical()
			@skip_mps
			def 					A_    (  self  ):
							return super().test_dict_tuple_outputs_equivalent()
			@skip_mps
			def 					A_    (  self  ):
							return super().test_save_load_optional_components()






			@skip_mps
			def 					A_    (  self  ):
							return super().test_attention_slicing_forward_pass()






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






			'''simple docstring'''



			def 					A_    (  self  ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/cycle-diffusion/black_colored_car.png'  )
							_lowerCamelCase     :			Any    =    load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'  )
							_lowerCamelCase     :			Optional[Any]    =    init_image.resize((512, 512)  )

							_lowerCamelCase     :			Dict    =    'CompVis/stable-diffusion-v1-4'
							_lowerCamelCase     :			Any    =    DDIMScheduler.from_pretrained(lowercase  ,      subfolder='scheduler'  )
							_lowerCamelCase     :			Optional[int]    =    CycleDiffusionPipeline.from_pretrained(
							    lowercase  ,      scheduler=lowercase  ,      safety_checker=lowercase  ,      torch_dtype=torch.floataa  ,      revision='fp16'  )

							pipe.to(lowercase  )
							pipe.set_progress_bar_config(disable=lowercase  )
							pipe.enable_attention_slicing()

							_lowerCamelCase     :			Tuple    =    'A black colored car'
							_lowerCamelCase     :			Optional[Any]    =    'A blue colored car'

							_lowerCamelCase     :			List[Any]    =    torch.manual_seed(0  )
							_lowerCamelCase     :			str    =    pipe(
							    prompt=lowercase  ,      source_prompt=lowercase  ,      image=lowercase  ,      num_inference_steps=100  ,      eta=0.1  ,      strength=0.85  ,      guidance_scale=3  ,      source_guidance_scale=1  ,      generator=lowercase  ,      output_type='np'  ,      )
							_lowerCamelCase     :			Optional[Any]    =    output.images

							# the values aren't exactly equal, but the images look the same visually
							assert np.abs(image - expected_image  ).max() < 5E-1






			def 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/cycle-diffusion/black_colored_car.png'  )
							_lowerCamelCase     :			str    =    load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'  )
							_lowerCamelCase     :			Tuple    =    init_image.resize((512, 512)  )

							_lowerCamelCase     :			List[Any]    =    'CompVis/stable-diffusion-v1-4'
							_lowerCamelCase     :			str    =    DDIMScheduler.from_pretrained(lowercase  ,      subfolder='scheduler'  )
							_lowerCamelCase     :			str    =    CycleDiffusionPipeline.from_pretrained(lowercase  ,      scheduler=lowercase  ,      safety_checker=lowercase  )

							pipe.to(lowercase  )
							pipe.set_progress_bar_config(disable=lowercase  )
							pipe.enable_attention_slicing()

							_lowerCamelCase     :			List[str]    =    'A black colored car'
							_lowerCamelCase     :			Tuple    =    'A blue colored car'

							_lowerCamelCase     :			Dict    =    torch.manual_seed(0  )
							_lowerCamelCase     :			Dict    =    pipe(
							    prompt=lowercase  ,      source_prompt=lowercase  ,      image=lowercase  ,      num_inference_steps=100  ,      eta=0.1  ,      strength=0.85  ,      guidance_scale=3  ,      source_guidance_scale=1  ,      generator=lowercase  ,      output_type='np'  ,      )
							_lowerCamelCase     :			str    =    output.images

							assert np.abs(image - expected_image  ).max() < 2E-2

'''simple docstring'''







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()
lowercase    :							str      =					logging.get_logger(__name__)

lowercase    :							Union[str, Any]      =					{
    "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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A			,				__A	)       ->	Dict:
	for attribute in key.split('.'	):
		_snake_case   						=		getattr(__A			,				__A	)

	if weight_type is not None:
		_snake_case   						=		getattr(__A			,				__A	).shape
	else:
		_snake_case   						=		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":
		_snake_case   						=		value
	elif weight_type == "weight_g":
		_snake_case   						=		value
	elif weight_type == "weight_v":
		_snake_case   						=		value
	elif weight_type == "bias":
		_snake_case   						=		value
	else:
		_snake_case   						=		value

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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Any:
	_snake_case   						=		[]
	_snake_case   						=		fairseq_model.state_dict()

	_snake_case   						=		hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_snake_case   						=		False
		if "conv_layers" in name:
			load_conv_layer(
			    __A			,				__A			,				__A			,				__A			,				hf_model.config.feat_extract_norm == 'group'			,				)
			_snake_case   						=		True
		else:
			for key, mapped_key in MAPPING.items():
				_snake_case   						=		'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]:
					_snake_case   						=		True
					if "*" in mapped_key:
						_snake_case   						=		name.split(__A	)[0].split('.'	)[-2]
						_snake_case   						=		mapped_key.replace('*'			,				__A	)
					if "weight_g" in name:
						_snake_case   						=		'weight_g'
					elif "weight_v" in name:
						_snake_case   						=		'weight_v'
					elif "weight" in name:
						_snake_case   						=		'weight'
					elif "bias" in name:
						_snake_case   						=		'bias'
					else:
						_snake_case   						=		None
					set_recursively(__A			,				__A			,				__A			,				__A			,				__A	)
				continue
		if not is_used:
			unused_weights.append(__A	)

	logger.warning(F'Unused weights: {unused_weights}'	)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A			,				__A	)       ->	int:
	_snake_case   						=		full_name.split('conv_layers.'	)[-1]
	_snake_case   						=		name.split('.'	)
	_snake_case   						=		int(items[0]	)
	_snake_case   						=		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.'
			)
			_snake_case   						=		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.'
			)
			_snake_case   						=		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."
			)
			_snake_case   						=		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.'
			)
			_snake_case   						=		value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'	)
	else:
		unused_weights.append(__A	)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str:
	_snake_case   						=		SEWConfig()
	if is_finetuned:
		_snake_case   						=		model.wav_encoder.wav_model.cfg
	else:
		_snake_case   						=		model.cfg

	_snake_case   						=		fs_config.conv_bias
	_snake_case   						=		eval(fs_config.conv_feature_layers	)
	_snake_case   						=		[x[0] for x in conv_layers]
	_snake_case   						=		[x[1] for x in conv_layers]
	_snake_case   						=		[x[2] for x in conv_layers]
	_snake_case   						=		'gelu'
	_snake_case   						=		'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	_snake_case   						=		0.0
	_snake_case   						=		fs_config.activation_fn.name
	_snake_case   						=		fs_config.encoder_embed_dim
	_snake_case   						=		0.0_2
	_snake_case   						=		fs_config.encoder_ffn_embed_dim
	_snake_case   						=		1e-5
	_snake_case   						=		fs_config.encoder_layerdrop
	_snake_case   						=		fs_config.encoder_attention_heads
	_snake_case   						=		fs_config.conv_pos_groups
	_snake_case   						=		fs_config.conv_pos
	_snake_case   						=		len(__A	)
	_snake_case   						=		fs_config.encoder_layers
	_snake_case   						=		fs_config.squeeze_factor

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		_snake_case   						=		model.cfg
		_snake_case   						=		fs_config.final_dropout
		_snake_case   						=		fs_config.layerdrop
	_snake_case   						=		fs_config.activation_dropout
	_snake_case   						=		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	_snake_case   						=		fs_config.attention_dropout
	_snake_case   						=		fs_config.dropout_input
	_snake_case   						=		fs_config.dropout
	_snake_case   						=		fs_config.mask_channel_length
	_snake_case   						=		fs_config.mask_channel_prob
	_snake_case   						=		fs_config.mask_length
	_snake_case   						=		fs_config.mask_prob

	_snake_case   						=		'Wav2Vec2FeatureExtractor'
	_snake_case   						=		'Wav2Vec2CTCTokenizer'

	return config







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A=None			,				__A=None			,				__A=True	)       ->	List[str]:

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

	if config_path is not None:
		_snake_case   						=		SEWConfig.from_pretrained(__A	)
	else:
		_snake_case   						=		convert_config(model[0]			,				__A	)
	_snake_case   						=		model[0].eval()

	_snake_case   						=		True if config.feat_extract_norm == 'layer' else False
	_snake_case   						=		WavaVecaFeatureExtractor(
	    feature_size=1			,				sampling_rate=16_000			,				padding_value=0			,				do_normalize=__A			,				return_attention_mask=__A			,				)

	if is_finetuned:
		if dict_path:
			_snake_case   						=		Dictionary.load(__A	)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			_snake_case   						=		target_dict.pad_index
			_snake_case   						=		target_dict.bos_index
			_snake_case   						=		target_dict.pad_index
			_snake_case   						=		target_dict.bos_index
			_snake_case   						=		target_dict.eos_index
			_snake_case   						=		len(target_dict.symbols	)
			_snake_case   						=		os.path.join(__A			,				'vocab.json'	)
			if not os.path.isdir(__A	):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__A	)	)
				return
			os.makedirs(__A			,				exist_ok=__A	)
			with open(__A			,				'w'			,				encoding='utf-8'	) as vocab_handle:
				json.dump(target_dict.indices			,				__A	)
			_snake_case   						=		WavaVecaCTCTokenizer(
			    __A			,				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=__A			,				)
			_snake_case   						=		WavaVecaProcessor(feature_extractor=__A			,				tokenizer=__A	)
			processor.save_pretrained(__A	)

		_snake_case   						=		SEWForCTC(__A	)
	else:
		_snake_case   						=		SEWModel(__A	)
		feature_extractor.save_pretrained(__A	)

	recursively_load_weights(__A			,				__A			,				__A	)

	hf_model.save_pretrained(__A	)


if __name__ == "__main__":
					lowercase    :							int      =					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"
					)
					lowercase    :							Union[str, Any]      =					parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)

