


"""simple docstring"""



import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


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

lowercase_							 =       {
    """post_extract_proj""": """feature_projection.projection""",
    """models.0.layer_norm""": """feature_projection.layer_norm""",
    """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""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
lowercase_							 =       [
    """lm_head""",
]
def     UpperCAmelCase    (					_lowercase   :      Any ,      _lowercase   :      Union[str, Any] ,      _lowercase   :      Optional[int] ,      _lowercase   :      List[str] ,      _lowercase   :      Optional[Any]  )	->   Optional[int]:


			"""simple docstring"""




			for attribute in key.split('''.'''  ):
						lowerCAmelCase_			       = getattr(lowerCAmelCase__ ,      lowerCAmelCase__  )

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

			logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""  )
def     UpperCAmelCase    (					_lowercase   :      int ,      _lowercase   :      Union[str, Any] ,      _lowercase   :      Optional[int]  )	->   Union[str, Any]:


			"""simple docstring"""




			lowerCAmelCase_			       = []
			lowerCAmelCase_			       = fairseq_model.state_dict()

			if not is_headless:
						lowerCAmelCase_			       = hf_model.dataavec_audio.feature_extractor
						lowerCAmelCase_			       = hf_model.dataavec_audio.encoder.pos_conv_embed

			else:
						lowerCAmelCase_			       = hf_model.feature_extractor
						lowerCAmelCase_			       = hf_model.encoder.pos_conv_embed

			for name, value in fairseq_dict.items():
						lowerCAmelCase_			       = False
						if "conv_layers" in name:
									load_conv_layer(
									    lowerCAmelCase__ ,      lowerCAmelCase__ ,      lowerCAmelCase__ ,      lowerCAmelCase__ ,      )
									lowerCAmelCase_			       = True
						elif "pos_conv" in name:
									load_pos_conv_layer(
									    lowerCAmelCase__ ,      lowerCAmelCase__ ,      lowerCAmelCase__ ,      lowerCAmelCase__ ,      )
									lowerCAmelCase_			       = True
						else:
									for key, mapped_key in MAPPING.items():
												if not is_headless:
															lowerCAmelCase_			       = 'data2vec_audio.' + 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]:
															lowerCAmelCase_			       = True
															if "*" in mapped_key:
																		lowerCAmelCase_			       = name.split(lowerCAmelCase__  )[0].split('''.'''  )[-2]
																		lowerCAmelCase_			       = mapped_key.replace('''*''' ,      lowerCAmelCase__  )
															if "weight_g" in name:
																		lowerCAmelCase_			       = 'weight_g'
															elif "weight_v" in name:
																		lowerCAmelCase_			       = 'weight_v'
															elif "bias" in name:
																		lowerCAmelCase_			       = 'bias'
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		lowerCAmelCase_			       = 'weight'
															else:
																		lowerCAmelCase_			       = 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    (					_lowercase   :      Optional[Any] ,      _lowercase   :      int  )	->   int:


			"""simple docstring"""




			lowerCAmelCase_			       = path.split('''.'''  )
			return reduce(lowerCAmelCase__ ,      lowerCAmelCase__ ,      lowerCAmelCase__  )
def     UpperCAmelCase    (					_lowercase   :      Tuple ,      _lowercase   :      str ,      _lowercase   :      int ,      _lowercase   :      Optional[int]  )	->   Optional[int]:


			"""simple docstring"""




			lowerCAmelCase_			       = access_by_string(lowerCAmelCase__ ,      lowerCAmelCase__  )
			lowerCAmelCase_			       = hf_weight.data

			if fsq_value.shape != hf_value.shape:
						raise ValueError(F"""{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."""  )
			lowerCAmelCase_			       = fsq_value
			logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""  )
def     UpperCAmelCase    (					_lowercase   :      Union[str, Any] ,      _lowercase   :      List[Any] ,      _lowercase   :      List[Any] ,      _lowercase   :      Union[str, Any]  )	->   Union[str, Any]:


			"""simple docstring"""




			lowerCAmelCase_			       = full_name.split('''conv_layers.'''  )[-1]
			lowerCAmelCase_			       = name.split('''.'''  )
			lowerCAmelCase_			       = int(items[0]  )
			lowerCAmelCase_			       = int(items[1]  )

			lowerCAmelCase_			       = name.split('''.'''  )[-1]
			if type_id == 0:
						lowerCAmelCase_			       = 'conv'
			elif type_id == 2:
						lowerCAmelCase_			       = 'layer_norm'
			else:
						unused_weights.append(lowerCAmelCase__  )
						return

			set_weights(lowerCAmelCase__ ,      lowerCAmelCase__ ,      lowerCAmelCase__ ,      F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""  )
def     UpperCAmelCase    (					_lowercase   :      List[str] ,      _lowercase   :      Dict ,      _lowercase   :      Tuple ,      _lowercase   :      Optional[int]  )	->   int:


			"""simple docstring"""




			lowerCAmelCase_			       = full_name.split('''pos_conv.'''  )[-1]
			lowerCAmelCase_			       = name.split('''.'''  )
			lowerCAmelCase_			       = int(items[0]  )
			lowerCAmelCase_			       = int(items[1]  )

			lowerCAmelCase_			       = name.split('''.'''  )[-1]
			if type_id != 0:
						unused_weights.append(lowerCAmelCase__  )
						return
			else:
						lowerCAmelCase_			       = 'conv'

			set_weights(lowerCAmelCase__ ,      lowerCAmelCase__ ,      lowerCAmelCase__ ,      F"""layers.{layer_id}.{layer_type}.{weight_type}"""  )
@torch.no_grad()
def     UpperCAmelCase    (					_lowercase   :      int ,      _lowercase   :      Union[str, Any] ,      _lowercase   :      Dict=None ,      _lowercase   :      Any=None ,      _lowercase   :      Union[str, Any]=True  )	->   List[Any]:


			"""simple docstring"""




			if config_path is not None:
						lowerCAmelCase_			       = DataaVecAudioConfig.from_pretrained(lowerCAmelCase__  )
			else:
						lowerCAmelCase_			       = DataaVecAudioConfig()

			if not is_finetuned:
						# Modify final_proj layer name
						lowerCAmelCase_			       = DataaVecAudioModel(lowerCAmelCase__  )
						lowerCAmelCase_			       = os.path.dirname(lowerCAmelCase__  )

						lowerCAmelCase_			       = torch.load(lowerCAmelCase__  )
						lowerCAmelCase_			       = state_dict['model'].pop('''final_proj.0.weight'''  )
						lowerCAmelCase_			       = state_dict['model'].pop('''final_proj.0.bias'''  )
						lowerCAmelCase_			       = os.path.join(lowerCAmelCase__ ,      '''converted.pt'''  )
						torch.save(lowerCAmelCase__ ,      lowerCAmelCase__  )
			else:
						lowerCAmelCase_			       = DataaVecAudioForCTC(lowerCAmelCase__  )
						lowerCAmelCase_			       = checkpoint_path

			def load_dataavec(_lowercase   :      str  ):
						lowerCAmelCase_			       = fairseq.checkpoint_utils.load_model_ensemble_and_task([path]  )
						return model[0].eval()

			lowerCAmelCase_			       = load_dataavec(lowerCAmelCase__  )

			recursively_load_weights(lowerCAmelCase__ ,      lowerCAmelCase__ ,      not is_finetuned  )

			lowerCAmelCase_			       = WavaVecaProcessor.from_pretrained('''facebook/wav2vec2-large-lv60'''  )

			lowerCAmelCase_			       = load_dataset('''patrickvonplaten/librispeech_asr_dummy''' ,      '''clean''' ,      split='''validation'''  )
			lowerCAmelCase_			       = [x['array'] for x in ds[:4]['audio']]

			lowerCAmelCase_			       = processor(lowerCAmelCase__ ,      return_tensors='''pt''' ,      padding=lowerCAmelCase__  )

			lowerCAmelCase_			       = inputs.input_values
			lowerCAmelCase_			       = inputs.attention_mask
			#    input_values = inputs.input_values[:, :-1]
			#    attention_mask = inputs.attention_mask[:, :-1]

			hf_wavavec.eval()
			model.eval()
			if is_finetuned:
						lowerCAmelCase_			       = model(source=lowerCAmelCase__ ,      padding_mask=(1 - attention_mask) ,      mask=lowerCAmelCase__ ,      features_only=lowerCAmelCase__  )[
						    'encoder_out'
						].transpose(0 ,      1  )
						lowerCAmelCase_			       = hf_wavavec(lowerCAmelCase__ ,      attention_mask=lowerCAmelCase__  )['logits']

						lowerCAmelCase_			       = torch.argmax(lowerCAmelCase__ ,      dim=-1  )
						lowerCAmelCase_			       = processor.batch_decode(lowerCAmelCase__  )

						print(F"""Expected Output: {ds[:4]["text"]}, Pred: {output_string}"""  )
			else:
						lowerCAmelCase_			       = model(source=lowerCAmelCase__ ,      padding_mask=(1 - attention_mask) ,      mask=lowerCAmelCase__ ,      features_only=lowerCAmelCase__  )[
						    'layer_results'
						][-1][0].transpose(0 ,      1  )
						lowerCAmelCase_			       = hf_wavavec(lowerCAmelCase__ ,      attention_mask=lowerCAmelCase__  )['last_hidden_state']

			print(our_output.shape ,      their_output.shape  )
			lowerCAmelCase_			       = torch.max(torch.abs(our_output - their_output  )  ).item()
			print(F"""max_absolute_diff = {max_absolute_diff}"""  )  # ~ 1e-7
			lowerCAmelCase_			       = torch.allclose(lowerCAmelCase__ ,      lowerCAmelCase__ ,      atol=1E-3  )
			print('''Do both models output the same tensors?''' ,      '''🔥''' if success else '''💩'''  )
			if not success:
						raise Exception('''Something went wRoNg'''  )

			hf_wavavec.save_pretrained(lowerCAmelCase__  )

			if is_finetuned:
						processor.save_pretrained(lowerCAmelCase__  )
			else:
						processor.feature_extractor.save_pretrained(lowerCAmelCase__  )


if __name__ == "__main__":
		lowercase_							 =       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'
		)
		lowercase_							 =       parser.parse_args()
		convert_wavaveca_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)



"""simple docstring"""



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available







@dataclass
class       __a	(   __snake_case       ):
				lowerCamelCase    :					int 					={
				    'sld_warmup_steps': 15,
				    'sld_guidance_scale': 20,
				    'sld_threshold': 0.0,
				    'sld_momentum_scale': 0.0,
				    'sld_mom_beta': 0.0,
				}
				lowerCamelCase    :					Optional[Any] 					={
				    'sld_warmup_steps': 10,
				    'sld_guidance_scale': 1000,
				    'sld_threshold': 0.01,
				    'sld_momentum_scale': 0.3,
				    'sld_mom_beta': 0.4,
				}
				lowerCamelCase    :					List[str] 					={
				    'sld_warmup_steps': 7,
				    'sld_guidance_scale': 2000,
				    'sld_threshold': 0.025,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}
				lowerCamelCase    :					Any 					={
				    'sld_warmup_steps': 0,
				    'sld_guidance_scale': 5000,
				    'sld_threshold': 1.0,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}







@dataclass
class       __a	(   __snake_case       ):
				lowerCamelCase    :					Union[List[PIL.Image.Image], np.ndarray]
				lowerCamelCase    :					Optional[List[bool]]
				lowerCamelCase    :					Optional[Union[List[PIL.Image.Image], np.ndarray]]
				lowerCamelCase    :					Optional[str]


if is_transformers_available() and is_torch_available():
		from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
		from .safety_checker import SafeStableDiffusionSafetyChecker