






"""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()
_lowerCAmelCase            =	logging.get_logger(__name__)

_lowerCAmelCase            =	{
    '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      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A )     ->       Dict:
				for attribute in key.split(""".""" ):
								lowercase    :      Dict       = getattr(_lowerCAmelCase  ,				_lowerCAmelCase )

				if weight_type is not None:
								lowercase    :      int       = getattr(_lowerCAmelCase  ,				_lowerCAmelCase ).shape
				else:
								lowercase    :      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":
								lowercase    :      str       = value
				elif weight_type == "weight_g":
								lowercase    :      Tuple       = value
				elif weight_type == "weight_v":
								lowercase    :      Optional[int]       = value
				elif weight_type == "bias":
								lowercase    :      Dict       = value
				else:
								lowercase    :      List[Any]       = value

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


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       str:
				lowercase    :      int       = []
				lowercase    :      int       = fairseq_model.state_dict()

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

				for name, value in fairseq_dict.items():
								lowercase    :      int       = False
								if "conv_layers" in name:
												load_conv_layer(
												    _lowerCAmelCase  ,				_lowerCAmelCase  ,				_lowerCAmelCase  ,				_lowerCAmelCase  ,				hf_model.config.feat_extract_norm == """group"""  ,				)
												lowercase    :      Optional[Any]       = True
								else:
												for key, mapped_key in MAPPING.items():
																lowercase    :      int       = """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]:
																				lowercase    :      Dict       = True
																				if "*" in mapped_key:
																								lowercase    :      int       = name.split(_lowerCAmelCase )[0].split(""".""" )[-2]
																								lowercase    :      Tuple       = mapped_key.replace("""*"""  ,				_lowerCAmelCase )
																				if "weight_g" in name:
																								lowercase    :      Optional[int]       = """weight_g"""
																				elif "weight_v" in name:
																								lowercase    :      List[str]       = """weight_v"""
																				elif "weight" in name:
																								lowercase    :      str       = """weight"""
																				elif "bias" in name:
																								lowercase    :      List[Any]       = """bias"""
																				else:
																								lowercase    :      List[str]       = None
																				set_recursively(_lowerCAmelCase  ,				_lowerCAmelCase  ,				_lowerCAmelCase  ,				_lowerCAmelCase  ,				_lowerCAmelCase )
																continue
								if not is_used:
												unused_weights.append(_lowerCAmelCase )

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


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A )     ->       str:
				lowercase    :      Optional[Any]       = full_name.split("""conv_layers.""" )[-1]
				lowercase    :      List[Any]       = name.split(""".""" )
				lowercase    :      List[Any]       = int(items[0] )
				lowercase    :      Optional[int]       = 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."""
												)
												lowercase    :      List[Any]       = 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."""
												)
												lowercase    :      Dict       = 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."
												)
												lowercase    :      Tuple       = 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."""
												)
												lowercase    :      List[str]       = value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
				else:
								unused_weights.append(_lowerCAmelCase )


def      UpperCamelCase       ( _A  ,				_A )     ->       int:
				lowercase    :      Optional[int]       = SEWConfig()
				if is_finetuned:
								lowercase    :      str       = model.wav_encoder.wav_model.cfg
				else:
								lowercase    :      Optional[Any]       = model.cfg

				lowercase    :      Union[str, Any]       = fs_config.conv_bias
				lowercase    :      Tuple       = eval(fs_config.conv_feature_layers )
				lowercase    :      str       = [x[0] for x in conv_layers]
				lowercase    :      Tuple       = [x[1] for x in conv_layers]
				lowercase    :      Optional[int]       = [x[2] for x in conv_layers]
				lowercase    :      List[Any]       = """gelu"""
				lowercase    :      Optional[int]       = """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
				lowercase    :      Optional[Any]       = 0.0
				lowercase    :      List[str]       = fs_config.activation_fn.name
				lowercase    :      Dict       = fs_config.encoder_embed_dim
				lowercase    :      int       = 0.02
				lowercase    :      Optional[int]       = fs_config.encoder_ffn_embed_dim
				lowercase    :      Union[str, Any]       = 1e-5
				lowercase    :      Optional[Any]       = fs_config.encoder_layerdrop
				lowercase    :      List[Any]       = fs_config.encoder_attention_heads
				lowercase    :      int       = fs_config.conv_pos_groups
				lowercase    :      Optional[int]       = fs_config.conv_pos
				lowercase    :      Optional[Any]       = len(_lowerCAmelCase )
				lowercase    :      Tuple       = fs_config.encoder_layers
				lowercase    :      Tuple       = fs_config.squeeze_factor

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

				lowercase    :      str       = """Wav2Vec2FeatureExtractor"""
				lowercase    :      Optional[Any]       = """Wav2Vec2CTCTokenizer"""

				return config


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

				if is_finetuned:
								lowercase      ,      lowercase      ,      lowercase    :      Union[str, Any]       = fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]  ,				arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
				else:
								lowercase      ,      lowercase      ,      lowercase    :      Any       = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )

				if config_path is not None:
								lowercase    :      Tuple       = SEWConfig.from_pretrained(_lowerCAmelCase )
				else:
								lowercase    :      List[Any]       = convert_config(model[0]  ,				_lowerCAmelCase )
				lowercase    :      Any       = model[0].eval()

				lowercase    :      Optional[int]       = True if config.feat_extract_norm == """layer""" else False
				lowercase    :      Optional[Any]       = WavaVecaFeatureExtractor(
				    feature_size=1  ,				sampling_rate=16_000  ,				padding_value=0  ,				do_normalize=_lowerCAmelCase  ,				return_attention_mask=_lowerCAmelCase  ,				)

				if is_finetuned:
								if dict_path:
												lowercase    :      int       = Dictionary.load(_lowerCAmelCase )

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

								lowercase    :      List[str]       = SEWForCTC(_lowerCAmelCase )
				else:
								lowercase    :      Dict       = SEWModel(_lowerCAmelCase )
								feature_extractor.save_pretrained(_lowerCAmelCase )

				recursively_load_weights(_lowerCAmelCase  ,				_lowerCAmelCase  ,				_lowerCAmelCase )

				hf_model.save_pretrained(_lowerCAmelCase )


if __name__ == "__main__":
				_lowerCAmelCase            =	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'
				)
				_lowerCAmelCase            =	parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)


"""simple docstring"""
import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

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,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class       lowerCAmelCase_		(				A__				,				A__				,				A__				,				unittest.TestCase ):



							'''simple docstring'''





							_snake_case						      =		StableUnCLIPPipeline
							_snake_case						      =		TEXT_TO_IMAGE_PARAMS
							_snake_case						      =		TEXT_TO_IMAGE_BATCH_PARAMS
							_snake_case						      =		TEXT_TO_IMAGE_IMAGE_PARAMS
							_snake_case						      =		TEXT_TO_IMAGE_IMAGE_PARAMS

							# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
							_snake_case						      =		False




							def    A__						( self				)       ->  Optional[Any]:
													__lowerCAmelCase     =  32
													__lowerCAmelCase     =  embedder_hidden_size

													# prior components

													torch.manual_seed(0				)
													__lowerCAmelCase     =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  CLIPTextModelWithProjection(
													    CLIPTextConfig(
													        bos_token_id=0    ,				eos_token_id=2    ,				hidden_size=snake_case_    ,				projection_dim=snake_case_    ,				intermediate_size=37    ,				layer_norm_eps=1e-0_5    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				pad_token_id=1    ,				vocab_size=1_000    ,				)				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  PriorTransformer(
													    num_attention_heads=2    ,				attention_head_dim=12    ,				embedding_dim=snake_case_    ,				num_layers=1    ,				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  DDPMScheduler(
													    variance_type="""fixed_small_log"""    ,				prediction_type="""sample"""    ,				num_train_timesteps=1_000    ,				clip_sample=snake_case_    ,				clip_sample_range=5.0    ,				beta_schedule="""squaredcos_cap_v2"""    ,				)

													# regular denoising components

													torch.manual_seed(0				)
													__lowerCAmelCase     =  StableUnCLIPImageNormalizer(embedding_dim=snake_case_				)
													__lowerCAmelCase     =  DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  CLIPTextModel(
													    CLIPTextConfig(
													        bos_token_id=0    ,				eos_token_id=2    ,				hidden_size=snake_case_    ,				projection_dim=32    ,				intermediate_size=37    ,				layer_norm_eps=1e-0_5    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				pad_token_id=1    ,				vocab_size=1_000    ,				)				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  UNetaDConditionModel(
													    sample_size=32    ,				in_channels=4    ,				out_channels=4    ,				down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")    ,				up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")    ,				block_out_channels=(32, 64)    ,				attention_head_dim=(2, 4)    ,				class_embed_type="""projection"""    ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2    ,				cross_attention_dim=snake_case_    ,				layers_per_block=1    ,				upcast_attention=snake_case_    ,				use_linear_projection=snake_case_    ,				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  DDIMScheduler(
													    beta_schedule="""scaled_linear"""    ,				beta_start=0.00_085    ,				beta_end=0.012    ,				prediction_type="""v_prediction"""    ,				set_alpha_to_one=snake_case_    ,				steps_offset=1    ,				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  AutoencoderKL()

													__lowerCAmelCase     =  {
													    # prior components
													    """prior_tokenizer""": prior_tokenizer,
													    """prior_text_encoder""": prior_text_encoder,
													    """prior""": prior,
													    """prior_scheduler""": prior_scheduler,
													    # image noising components
													    """image_normalizer""": image_normalizer,
													    """image_noising_scheduler""": image_noising_scheduler,
													    # regular denoising components
													    """tokenizer""": tokenizer,
													    """text_encoder""": text_encoder,
													    """unet""": unet,
													    """scheduler""": scheduler,
													    """vae""": vae,
													}

													return components




							def    A__						( self    ,				snake_case_    ,				snake_case_=0				)       ->  Optional[Any]:
													if str(snake_case_				).startswith("""mps"""				):
																			__lowerCAmelCase     =  torch.manual_seed(snake_case_				)
													else:
																			__lowerCAmelCase     =  torch.Generator(device=snake_case_				).manual_seed(snake_case_				)
													__lowerCAmelCase     =  {
													    """prompt""": """A painting of a squirrel eating a burger""",
													    """generator""": generator,
													    """num_inference_steps""": 2,
													    """prior_num_inference_steps""": 2,
													    """output_type""": """numpy""",
													}
													return inputs




							def    A__						( self				)       ->  Tuple:
													__lowerCAmelCase     =  torch_device == """cpu"""

													self._test_attention_slicing_forward_pass(test_max_difference=snake_case_				)




							def    A__						( self				)       ->  Tuple:
													__lowerCAmelCase     =  torch_device in ["""cpu""", """mps"""]

													self._test_inference_batch_single_identical(test_max_difference=snake_case_				)
@slow
@require_torch_gpu
class       lowerCAmelCase_		(				unittest.TestCase ):



							'''simple docstring'''





							def    A__						( self				)       ->  Optional[int]:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()




							def    A__						( self				)       ->  Tuple:
													__lowerCAmelCase     =  load_numpy(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""				)

													__lowerCAmelCase     =  StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""    ,				torch_dtype=torch.floataa				)
													pipe.to(snake_case_				)
													pipe.set_progress_bar_config(disable=snake_case_				)
													# stable unclip will oom when integration tests are run on a V100,
													# so turn on memory savings
													pipe.enable_attention_slicing()
													pipe.enable_sequential_cpu_offload()

													__lowerCAmelCase     =  torch.Generator(device="""cpu"""				).manual_seed(0				)
													__lowerCAmelCase     =  pipe("""anime turle"""    ,				generator=snake_case_    ,				output_type="""np"""				)

													__lowerCAmelCase     =  output.images[0]

													assert image.shape == (768, 768, 3)

													assert_mean_pixel_difference(snake_case_    ,				snake_case_				)




							def    A__						( self				)       ->  Tuple:
													torch.cuda.empty_cache()
													torch.cuda.reset_max_memory_allocated()
													torch.cuda.reset_peak_memory_stats()

													__lowerCAmelCase     =  StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""    ,				torch_dtype=torch.floataa				)
													__lowerCAmelCase     =  pipe.to(snake_case_				)
													pipe.set_progress_bar_config(disable=snake_case_				)
													pipe.enable_attention_slicing()
													pipe.enable_sequential_cpu_offload()

													__lowerCAmelCase     =  pipe(
													    """anime turtle"""    ,				prior_num_inference_steps=2    ,				num_inference_steps=2    ,				output_type="""np"""    ,				)

													__lowerCAmelCase     =  torch.cuda.max_memory_allocated()
													# make sure that less than 7 GB is allocated
													assert mem_bytes < 7 * 10**9
