
import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

lowerCamelCase__        =		{
    'post_extract_proj': 'feature_projection.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.layer_norm': 'encoder.layer_norm',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
lowerCamelCase__        =		[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	for attribute in key.split('.'		):
		__lowerCAmelCase      :						Any						      =				getattr(UpperCamelCase__	,  UpperCamelCase__		)

	if weight_type is not None:
		__lowerCAmelCase      :						Dict						      =				getattr(UpperCamelCase__	,  UpperCamelCase__		).shape
	else:
		__lowerCAmelCase      :						Union[str, Any]						      =				hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    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[Any]						      =				value
	elif weight_type == "weight_g":
		__lowerCAmelCase      :						Optional[Any]						      =				value
	elif weight_type == "weight_v":
		__lowerCAmelCase      :						Tuple						      =				value
	elif weight_type == "bias":
		__lowerCAmelCase      :						List[Any]						      =				value
	else:
		__lowerCAmelCase      :						Optional[Any]						      =				value

	logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Optional[int]						      =				[]
	__lowerCAmelCase      :						List[str]						      =				fairseq_model.state_dict()

	__lowerCAmelCase      :						Optional[Any]						      =				hf_model.unispeech_sat.feature_extractor

	for name, value in fairseq_dict.items():
		__lowerCAmelCase      :						Optional[int]						      =				False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCamelCase__	,  UpperCamelCase__	,  UpperCamelCase__	,  UpperCamelCase__	,  hf_model.config.feat_extract_norm == 'group'	,  )
			__lowerCAmelCase      :						Dict						      =				True
		else:
			for key, mapped_key in MAPPING.items():
				__lowerCAmelCase      :						List[Any]						      =				'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
				if key in name or key.split('w2v_model.'		)[-1] == name.split('.'		)[0]:
					if "layer_norm_for_extract" in name and (".".join(name.split('.'		)[:-1]		) != key):
						# special case since naming is very similar
						continue
					__lowerCAmelCase      :						str						      =				True
					if "*" in mapped_key:
						__lowerCAmelCase      :						Dict						      =				name.split(UpperCamelCase__		)[0].split('.'		)[-2]
						__lowerCAmelCase      :						Optional[Any]						      =				mapped_key.replace('*'	,  UpperCamelCase__		)
					if "weight_g" in name:
						__lowerCAmelCase      :						str						      =				'weight_g'
					elif "weight_v" in name:
						__lowerCAmelCase      :						int						      =				'weight_v'
					elif "bias" in name:
						__lowerCAmelCase      :						str						      =				'bias'
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						__lowerCAmelCase      :						List[str]						      =				'weight'
					else:
						__lowerCAmelCase      :						Tuple						      =				None
					set_recursively(UpperCamelCase__	,  UpperCamelCase__	,  UpperCamelCase__	,  UpperCamelCase__	,  UpperCamelCase__		)
				continue
		if not is_used:
			unused_weights.append(UpperCamelCase__		)

	logger.warning(F"Unused weights: {unused_weights}"		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Union[str, Any]						      =				full_name.split('conv_layers.'		)[-1]
	__lowerCAmelCase      :						str						      =				name.split('.'		)
	__lowerCAmelCase      :						Optional[Any]						      =				int(items[0]		)
	__lowerCAmelCase      :						int						      =				int(items[1]		)

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    F"{full_name} has size {value.shape}, but"
				    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."		)
			__lowerCAmelCase      :						Tuple						      =				value
			logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    F"{full_name} has size {value.shape}, but"
				    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."		)
			__lowerCAmelCase      :						Tuple						      =				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:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    F"{full_name} has size {value.shape}, but"
				    F" {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:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    F"{full_name} has size {value.shape}, but"
				    F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."		)
			__lowerCAmelCase      :						Union[str, Any]						      =				value
			logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
	else:
		unused_weights.append(UpperCamelCase__		)
@torch.no_grad()
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase=None	,  _UpperCamelCase=None	,  _UpperCamelCase=True		):
	if config_path is not None:
		__lowerCAmelCase      :						Union[str, Any]						      =				UniSpeechSatConfig.from_pretrained(UpperCamelCase__		)
	else:
		__lowerCAmelCase      :						Any						      =				UniSpeechSatConfig()

	__lowerCAmelCase      :						Any						      =				''

	if is_finetuned:
		__lowerCAmelCase      :						List[str]						      =				UniSpeechSatForCTC(UpperCamelCase__		)
	else:
		__lowerCAmelCase      :						Any						      =				UniSpeechSatForPreTraining(UpperCamelCase__		)

	__lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      :						str						      =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path]	,  arg_overrides={'data': '/'.join(dict_path.split('/'		)[:-1]		)}		)
	__lowerCAmelCase      :						int						      =				model[0].eval()

	recursively_load_weights(UpperCamelCase__	,  UpperCamelCase__		)

	hf_wavavec.save_pretrained(UpperCamelCase__		)


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(
							    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
							)
							lowerCamelCase__        =		parser.parse_args()
							convert_unispeech_sat_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)

"""simple docstring"""







import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference




class 			A__	(     _lowerCamelCase	,					unittest.TestCase):
		A_						:	Optional[int]           =     ShapEPipeline
		A_						:	str           =     ['prompt']
		A_						:	Any           =     ['prompt']
		A_						:	List[Any]           =     [
		    'num_images_per_prompt',
		    'num_inference_steps',
		    'generator',
		    'latents',
		    'guidance_scale',
		    'frame_size',
		    'output_type',
		    'return_dict',
		]
		A_						:	Optional[int]           =     False

		@property
		def 							__lowerCamelCase							(				self			):
			return 32

		@property
		def 							__lowerCamelCase							(				self			):
			return 32

		@property
		def 							__lowerCamelCase							(				self			):
			return self.time_input_dim * 4

		@property
		def 							__lowerCamelCase							(				self			):
			return 8

		@property
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
			return tokenizer

		@property
		def 							__lowerCamelCase							(				self			):
			torch.manual_seed(0			)
			__lowerCAmelCase      :						str						      =				CLIPTextConfig(
			    bos_token_id=0	,		eos_token_id=2	,		hidden_size=self.text_embedder_hidden_size	,		projection_dim=self.text_embedder_hidden_size	,		intermediate_size=37	,		layer_norm_eps=1E-05	,		num_attention_heads=4	,		num_hidden_layers=5	,		pad_token_id=1	,		vocab_size=10_00	,		)
			return CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE			)

		@property
		def 							__lowerCamelCase							(				self			):
			torch.manual_seed(0			)

			__lowerCAmelCase      :						Optional[Any]						      =				{
			    'num_attention_heads': 2,
			    'attention_head_dim': 16,
			    'embedding_dim': self.time_input_dim,
			    'num_embeddings': 32,
			    'embedding_proj_dim': self.text_embedder_hidden_size,
			    'time_embed_dim': self.time_embed_dim,
			    'num_layers': 1,
			    'clip_embed_dim': self.time_input_dim * 2,
			    'additional_embeddings': 0,
			    'time_embed_act_fn': 'gelu',
			    'norm_in_type': 'layer',
			    'encoder_hid_proj_type': None,
			    'added_emb_type': None,
			}

			__lowerCAmelCase      :						int						      =				PriorTransformer(**_SCREAMING_SNAKE_CASE			)
			return model

		@property
		def 							__lowerCamelCase							(				self			):
			torch.manual_seed(0			)

			__lowerCAmelCase      :						Dict						      =				{
			    'param_shapes': (
			        (self.renderer_dim, 93),
			        (self.renderer_dim, 8),
			        (self.renderer_dim, 8),
			        (self.renderer_dim, 8),
			    ),
			    'd_latent': self.time_input_dim,
			    'd_hidden': self.renderer_dim,
			    'n_output': 12,
			    'background': (
			        0.1,
			        0.1,
			        0.1,
			    ),
			}
			__lowerCAmelCase      :						Union[str, Any]						      =				ShapERenderer(**_SCREAMING_SNAKE_CASE			)
			return model

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				self.dummy_prior
			__lowerCAmelCase      :						str						      =				self.dummy_text_encoder
			__lowerCAmelCase      :						List[Any]						      =				self.dummy_tokenizer
			__lowerCAmelCase      :						str						      =				self.dummy_renderer

			__lowerCAmelCase      :						List[Any]						      =				HeunDiscreteScheduler(
			    beta_schedule='exp'	,		num_train_timesteps=10_24	,		prediction_type='sample'	,		use_karras_sigmas=_SCREAMING_SNAKE_CASE	,		clip_sample=_SCREAMING_SNAKE_CASE	,		clip_sample_range=1.0	,		)
			__lowerCAmelCase      :						int						      =				{
			    'prior': prior,
			    'text_encoder': text_encoder,
			    'tokenizer': tokenizer,
			    'renderer': renderer,
			    'scheduler': scheduler,
			}

			return components

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=0			):
			if str(_SCREAMING_SNAKE_CASE			).startswith('mps'			):
				__lowerCAmelCase      :						int						      =				torch.manual_seed(_SCREAMING_SNAKE_CASE			)
			else:
				__lowerCAmelCase      :						Dict						      =				torch.Generator(device=_SCREAMING_SNAKE_CASE			).manual_seed(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[Any]						      =				{
			    'prompt': 'horse',
			    'generator': generator,
			    'num_inference_steps': 1,
			    'frame_size': 32,
			    'output_type': 'np',
			}
			return inputs

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				'cpu'

			__lowerCAmelCase      :						int						      =				self.get_dummy_components()

			__lowerCAmelCase      :						Union[str, Any]						      =				self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Union[str, Any]						      =				pipe.to(_SCREAMING_SNAKE_CASE			)

			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Optional[int]						      =				pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)			)
			__lowerCAmelCase      :						Optional[Any]						      =				output.images[0]
			__lowerCAmelCase      :						Optional[int]						      =				image[0, -3:, -3:, -1]

			assert image.shape == (20, 32, 32, 3)

			__lowerCAmelCase      :						str						      =				np.array(
			    [
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			    ]			)

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

		def 							__lowerCamelCase							(				self			):
			# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
			self._test_inference_batch_consistent(batch_sizes=[1, 2]			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				torch_device == 'cpu'
			__lowerCAmelCase      :						str						      =				True

			self._test_inference_batch_single_identical(
			    batch_size=2	,		test_max_difference=_SCREAMING_SNAKE_CASE	,		relax_max_difference=_SCREAMING_SNAKE_CASE	,		)







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				self.get_dummy_components()
			__lowerCAmelCase      :						List[Any]						      =				self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				pipe.to(_SCREAMING_SNAKE_CASE			)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						int						      =				1
			__lowerCAmelCase      :						List[Any]						      =				2

			__lowerCAmelCase      :						int						      =				self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)

			for key in inputs.keys():
				if key in self.batch_params:
					__lowerCAmelCase      :						Dict						      =				batch_size * [inputs[key]]

			__lowerCAmelCase      :						Any						      =				pipe(**_SCREAMING_SNAKE_CASE	,		num_images_per_prompt=_SCREAMING_SNAKE_CASE			)[0]

			assert images.shape[0] == batch_size * num_images_per_prompt




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

		def 							__lowerCamelCase							(				self			):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/shap_e/test_shap_e_np_out.npy'			)
			__lowerCAmelCase      :						Dict						      =				ShapEPipeline.from_pretrained('openai/shap-e'			)
			__lowerCAmelCase      :						int						      =				pipe.to(_SCREAMING_SNAKE_CASE			)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Any						      =				torch.Generator(device=_SCREAMING_SNAKE_CASE			).manual_seed(0			)

			__lowerCAmelCase      :						List[str]						      =				pipe(
			    'a shark'	,		generator=_SCREAMING_SNAKE_CASE	,		guidance_scale=15.0	,		num_inference_steps=64	,		frame_size=64	,		output_type='np'	,		).images[0]

			assert images.shape == (20, 64, 64, 3)

			assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)