
import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


lowerCamelCase_  :       int					      =     logging.get_logger(__name__)  # pylint: disable=invalid-name







def 				A__    (  self   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		str:
				UpperCamelCase_:							Tuple		=  image.to(device=lowerCamelCase  )
				UpperCamelCase_:							Optional[Any]		=  image  # take image as image_embeddings
				UpperCamelCase_:							int		=  image_embeddings.unsqueeze(1  )

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_:							List[Any]		=  image_embeddings.shape
				UpperCamelCase_:							Any		=  image_embeddings.repeat(1   ,			lowerCamelCase   ,			1  )
				UpperCamelCase_:							Dict		=  image_embeddings.view(bs_embed * num_images_per_prompt   ,			lowerCamelCase   ,			-1  )

				if do_classifier_free_guidance:
								UpperCamelCase_:							List[str]		=  torch.zeros_like(lowerCamelCase  )

								# For classifier free guidance, we need to do two forward passes.
								# Here we concatenate the unconditional and text embeddings into a single batch
								# to avoid doing two forward passes
								UpperCamelCase_:							List[Any]		=  torch.cat([uncond_embeddings, image_embeddings]  )

				return image_embeddings





class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			def __init__(				self       :				Optional[int]  ,		snake_case_       :				PriorTransformer  ,		snake_case_       :				CLIPTokenizer  ,		snake_case_       :				CLIPTextModelWithProjection  ,		snake_case_       :				UnCLIPScheduler  ,		snake_case_       :				Optional[dict] = None  ,		):
							super().__init__()

							UpperCamelCase_:							Optional[Any]		=  {"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

							UpperCamelCase_:							List[Any]		=  decoder_pipe_kwargs.get("""torch_dtype"""  ,		snake_case_					) or prior.dtype

							UpperCamelCase_:							List[Any]		=  StableDiffusionImageVariationPipeline.from_pretrained(
							    """lambdalabs/sd-image-variations-diffusers"""  ,		**snake_case_					)

							# replace `_encode_image` method
							UpperCamelCase_:							str		=  types.MethodType(_encode_image  ,		self.decoder_pipe					)

							self.register_modules(
							    prior=snake_case_  ,		tokenizer=snake_case_  ,		text_encoder=snake_case_  ,		prior_scheduler=snake_case_  ,		)

			def 					lowerCAmelCase__		(				self       :				Any  ,		snake_case_       :				Optional[int]  ,		snake_case_       :				Union[str, Any]  ,		snake_case_       :				Any  ,		snake_case_       :				Optional[Any]  ,		snake_case_       :				Optional[Union[CLIPTextModelOutput, Tuple]] = None  ,		snake_case_       :				Optional[torch.Tensor] = None  ,		):
							if text_model_output is None:
											UpperCamelCase_:							int		=  len(snake_case_					) if isinstance(snake_case_  ,		snake_case_					) else 1
											# get prompt text embeddings
											UpperCamelCase_:							Optional[Any]		=  self.tokenizer(
											    snake_case_  ,		padding="""max_length"""  ,		max_length=self.tokenizer.model_max_length  ,		return_tensors="""pt"""  ,		)
											UpperCamelCase_:							Tuple		=  text_inputs.input_ids
											UpperCamelCase_:							Union[str, Any]		=  text_inputs.attention_mask.bool().to(snake_case_					)

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
															UpperCamelCase_:							Dict		=  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
															logger.warning(
															    """The following part of your input was truncated because CLIP can only handle sequences up to"""
															    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''					)
															UpperCamelCase_:							Dict		=  text_input_ids[:, : self.tokenizer.model_max_length]

											UpperCamelCase_:							int		=  self.text_encoder(text_input_ids.to(snake_case_					)					)

											UpperCamelCase_:							Optional[Any]		=  text_encoder_output.text_embeds
											UpperCamelCase_:							List[Any]		=  text_encoder_output.last_hidden_state

							else:
											UpperCamelCase_:							List[Any]		=  text_model_output[0].shape[0]
											UpperCamelCase_,      UpperCamelCase_:							List[str]		=  text_model_output[0], text_model_output[1]
											UpperCamelCase_:							Optional[int]		=  text_attention_mask

							UpperCamelCase_:							List[str]		=  text_embeddings.repeat_interleave(snake_case_  ,		dim=0					)
							UpperCamelCase_:							Optional[Any]		=  text_encoder_hidden_states.repeat_interleave(snake_case_  ,		dim=0					)
							UpperCamelCase_:							Union[str, Any]		=  text_mask.repeat_interleave(snake_case_  ,		dim=0					)

							if do_classifier_free_guidance:
											UpperCamelCase_:							Optional[Any]		=  [""""""] * batch_size

											UpperCamelCase_:							Any		=  self.tokenizer(
											    snake_case_  ,		padding="""max_length"""  ,		max_length=self.tokenizer.model_max_length  ,		truncation=snake_case_  ,		return_tensors="""pt"""  ,		)
											UpperCamelCase_:							str		=  uncond_input.attention_mask.bool().to(snake_case_					)
											UpperCamelCase_:							Any		=  self.text_encoder(uncond_input.input_ids.to(snake_case_					)					)

											UpperCamelCase_:							Union[str, Any]		=  uncond_embeddings_text_encoder_output.text_embeds
											UpperCamelCase_:							int		=  uncond_embeddings_text_encoder_output.last_hidden_state

											# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

											UpperCamelCase_:							List[str]		=  uncond_embeddings.shape[1]
											UpperCamelCase_:							str		=  uncond_embeddings.repeat(1  ,		snake_case_					)
											UpperCamelCase_:							List[str]		=  uncond_embeddings.view(batch_size * num_images_per_prompt  ,		snake_case_					)

											UpperCamelCase_:							Optional[int]		=  uncond_text_encoder_hidden_states.shape[1]
											UpperCamelCase_:							List[Any]		=  uncond_text_encoder_hidden_states.repeat(1  ,		snake_case_  ,		1					)
											UpperCamelCase_:							List[str]		=  uncond_text_encoder_hidden_states.view(
											    batch_size * num_images_per_prompt  ,		snake_case_  ,		-1					)
											UpperCamelCase_:							Union[str, Any]		=  uncond_text_mask.repeat_interleave(snake_case_  ,		dim=0					)

											# done duplicates

											# For classifier free guidance, we need to do two forward passes.
											# Here we concatenate the unconditional and text embeddings into a single batch
											# to avoid doing two forward passes
											UpperCamelCase_:							Optional[int]		=  torch.cat([uncond_embeddings, text_embeddings]					)
											UpperCamelCase_:							int		=  torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)

											UpperCamelCase_:							int		=  torch.cat([uncond_text_mask, text_mask]					)

							return text_embeddings, text_encoder_hidden_states, text_mask

			@property
			def 					lowerCAmelCase__		(				self       :				Dict					):
							if self.device != torch.device("""meta"""					) or not hasattr(self.prior  ,		"""_hf_hook"""					):
											return self.device
							for module in self.prior.modules():
											if (
											    hasattr(snake_case_  ,		"""_hf_hook"""					)
											    and hasattr(module._hf_hook  ,		"""execution_device"""					)
											    and module._hf_hook.execution_device is not None
											):
															return torch.device(module._hf_hook.execution_device					)
							return self.device

			def 					lowerCAmelCase__		(				self       :				List[str]  ,		snake_case_       :				Optional[Any]  ,		snake_case_       :				List[str]  ,		snake_case_       :				Any  ,		snake_case_       :				str  ,		snake_case_       :				int  ,		snake_case_       :				str					):
							if latents is None:
											UpperCamelCase_:							str		=  randn_tensor(snake_case_  ,		generator=snake_case_  ,		device=snake_case_  ,		dtype=snake_case_					)
							else:
											if latents.shape != shape:
															raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''					)
											UpperCamelCase_:							Tuple		=  latents.to(snake_case_					)

							UpperCamelCase_:							Optional[int]		=  latents * scheduler.init_noise_sigma
							return latents

			def 					lowerCAmelCase__		(				self       :				Tuple  ,		snake_case_       :				Optional[Union[str, torch.device]] = None					):
							self.decoder_pipe.to(snake_case_					)
							super().to(snake_case_					)




			@torch.no_grad()
			def __call__(				self       :				Union[str, Any]  ,		snake_case_       :				Optional[Union[str, List[str]]] = None  ,		snake_case_       :				Optional[int] = None  ,		snake_case_       :				Optional[int] = None  ,		snake_case_       :				int = 1  ,		snake_case_       :				int = 25  ,		snake_case_       :				Optional[torch.Generator] = None  ,		snake_case_       :				Optional[torch.FloatTensor] = None  ,		snake_case_       :				Optional[Union[CLIPTextModelOutput, Tuple]] = None  ,		snake_case_       :				Optional[torch.Tensor] = None  ,		snake_case_       :				float = 4.0  ,		snake_case_       :				float = 8.0  ,		snake_case_       :				int = 50  ,		snake_case_       :				Optional[int] = 1  ,		snake_case_       :				float = 0.0  ,		snake_case_       :				Optional[str] = "pil"  ,		snake_case_       :				bool = True  ,		):
							if prompt is not None:
											if isinstance(snake_case_  ,		snake_case_					):
															UpperCamelCase_:							Any		=  1
											elif isinstance(snake_case_  ,		snake_case_					):
															UpperCamelCase_:							Dict		=  len(snake_case_					)
											else:
															raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(snake_case_					)}'''					)
							else:
											UpperCamelCase_:							Union[str, Any]		=  text_model_output[0].shape[0]

							UpperCamelCase_:							int		=  self._execution_device

							UpperCamelCase_:							Optional[int]		=  batch_size * num_images_per_prompt

							UpperCamelCase_:							Optional[Any]		=  prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

							UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_:							Dict		=  self._encode_prompt(
							    snake_case_  ,		snake_case_  ,		snake_case_  ,		snake_case_  ,		snake_case_  ,		snake_case_					)

							# prior

							self.prior_scheduler.set_timesteps(snake_case_  ,		device=snake_case_					)
							UpperCamelCase_:							List[Any]		=  self.prior_scheduler.timesteps

							UpperCamelCase_:							Union[str, Any]		=  self.prior.config.embedding_dim

							UpperCamelCase_:							Any		=  self.prepare_latents(
							    (batch_size, embedding_dim)  ,		text_embeddings.dtype  ,		snake_case_  ,		snake_case_  ,		snake_case_  ,		self.prior_scheduler  ,		)

							for i, t in enumerate(self.progress_bar(snake_case_					)					):
											# expand the latents if we are doing classifier free guidance
											UpperCamelCase_:							Optional[Any]		=  torch.cat([prior_latents] * 2					) if do_classifier_free_guidance else prior_latents

											UpperCamelCase_:							Any		=  self.prior(
											    snake_case_  ,		timestep=snake_case_  ,		proj_embedding=snake_case_  ,		encoder_hidden_states=snake_case_  ,		attention_mask=snake_case_  ,		).predicted_image_embedding

											if do_classifier_free_guidance:
															UpperCamelCase_,      UpperCamelCase_:							Dict		=  predicted_image_embedding.chunk(2					)
															UpperCamelCase_:							Optional[Any]		=  predicted_image_embedding_uncond + prior_guidance_scale * (
															    predicted_image_embedding_text - predicted_image_embedding_uncond
															)

											if i + 1 == prior_timesteps_tensor.shape[0]:
															UpperCamelCase_:							List[Any]		=  None
											else:
															UpperCamelCase_:							Union[str, Any]		=  prior_timesteps_tensor[i + 1]

											UpperCamelCase_:							List[Any]		=  self.prior_scheduler.step(
											    snake_case_  ,		timestep=snake_case_  ,		sample=snake_case_  ,		generator=snake_case_  ,		prev_timestep=snake_case_  ,		).prev_sample

							UpperCamelCase_:							Optional[Any]		=  self.prior.post_process_latents(snake_case_					)

							UpperCamelCase_:							Optional[int]		=  prior_latents

							UpperCamelCase_:							List[str]		=  self.decoder_pipe(
							    image=snake_case_  ,		height=snake_case_  ,		width=snake_case_  ,		num_inference_steps=snake_case_  ,		guidance_scale=snake_case_  ,		generator=snake_case_  ,		output_type=snake_case_  ,		return_dict=snake_case_  ,		num_images_per_prompt=snake_case_  ,		eta=snake_case_  ,		)
							return output








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()
lowerCamelCase_  :       int					      =     logging.get_logger(__name__)

lowerCamelCase_  :       Optional[int]					      =     {
    """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""",
}
lowerCamelCase_  :       Any					      =     [
    """lm_head""",
]







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		List[Any]:
				for attribute in key.split("""."""  ):
								UpperCamelCase_:							List[str]		=  getattr(lowerCamelCase   ,			lowerCamelCase  )

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

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







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Union[str, Any]:
				UpperCamelCase_:							Optional[int]		=  []
				UpperCamelCase_:							Tuple		=  fairseq_model.state_dict()

				if not is_headless:
								UpperCamelCase_:							Dict		=  hf_model.dataavec_audio.feature_extractor
								UpperCamelCase_:							Tuple		=  hf_model.dataavec_audio.encoder.pos_conv_embed

				else:
								UpperCamelCase_:							Tuple		=  hf_model.feature_extractor
								UpperCamelCase_:							int		=  hf_model.encoder.pos_conv_embed

				for name, value in fairseq_dict.items():
								UpperCamelCase_:							Union[str, Any]		=  False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			)
												UpperCamelCase_:							Optional[Any]		=  True
								elif "pos_conv" in name:
												load_pos_conv_layer(
												    lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			)
												UpperCamelCase_:							Optional[Any]		=  True
								else:
												for key, mapped_key in MAPPING.items():
																if not is_headless:
																				UpperCamelCase_:							List[str]		=  """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]:
																				UpperCamelCase_:							str		=  True
																				if "*" in mapped_key:
																								UpperCamelCase_:							Any		=  name.split(lowerCamelCase  )[0].split("""."""  )[-2]
																								UpperCamelCase_:							int		=  mapped_key.replace("""*"""   ,			lowerCamelCase  )
																				if "weight_g" in name:
																								UpperCamelCase_:							Tuple		=  """weight_g"""
																				elif "weight_v" in name:
																								UpperCamelCase_:							List[Any]		=  """weight_v"""
																				elif "bias" in name:
																								UpperCamelCase_:							Union[str, Any]		=  """bias"""
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								UpperCamelCase_:							str		=  """weight"""
																				else:
																								UpperCamelCase_:							Tuple		=  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 				A__    (  lowerCamelCase   ,			lowerCamelCase  )     ->		Dict:
				UpperCamelCase_:							Union[str, Any]		=  path.split("""."""  )
				return reduce(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		str:
				UpperCamelCase_:							Union[str, Any]		=  access_by_string(lowerCamelCase   ,			lowerCamelCase  )
				UpperCamelCase_:							str		=  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.'''  )
				UpperCamelCase_:							Optional[int]		=  fsq_value
				logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Optional[int]:
				UpperCamelCase_:							str		=  full_name.split("""conv_layers."""  )[-1]
				UpperCamelCase_:							Union[str, Any]		=  name.split("""."""  )
				UpperCamelCase_:							Union[str, Any]		=  int(items[0]  )
				UpperCamelCase_:							Union[str, Any]		=  int(items[1]  )

				UpperCamelCase_:							Tuple		=  name.split("""."""  )[-1]
				if type_id == 0:
								UpperCamelCase_:							List[Any]		=  """conv"""
				elif type_id == 2:
								UpperCamelCase_:							Tuple		=  """layer_norm"""
				else:
								unused_weights.append(lowerCamelCase  )
								return

				set_weights(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			F'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Optional[Any]:
				UpperCamelCase_:							Optional[int]		=  full_name.split("""pos_conv."""  )[-1]
				UpperCamelCase_:							Any		=  name.split("""."""  )
				UpperCamelCase_:							List[str]		=  int(items[0]  )
				UpperCamelCase_:							str		=  int(items[1]  )

				UpperCamelCase_:							List[str]		=  name.split("""."""  )[-1]
				if type_id != 0:
								unused_weights.append(lowerCamelCase  )
								return
				else:
								UpperCamelCase_:							Tuple		=  """conv"""

				set_weights(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			F'''layers.{layer_id}.{layer_type}.{weight_type}'''  )







@torch.no_grad()
def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase=None   ,			lowerCamelCase=None   ,			lowerCamelCase=True  )     ->		Dict:
				if config_path is not None:
								UpperCamelCase_:							str		=  DataaVecAudioConfig.from_pretrained(lowerCamelCase  )
				else:
								UpperCamelCase_:							Tuple		=  DataaVecAudioConfig()

				if not is_finetuned:
								# Modify final_proj layer name
								UpperCamelCase_:							Union[str, Any]		=  DataaVecAudioModel(lowerCamelCase  )
								UpperCamelCase_:							Optional[int]		=  os.path.dirname(lowerCamelCase  )

								UpperCamelCase_:							Optional[Any]		=  torch.load(lowerCamelCase  )
								UpperCamelCase_:							List[str]		=  state_dict["""model"""].pop("""final_proj.0.weight"""  )
								UpperCamelCase_:							int		=  state_dict["""model"""].pop("""final_proj.0.bias"""  )
								UpperCamelCase_:							Optional[int]		=  os.path.join(lowerCamelCase   ,			"""converted.pt"""  )
								torch.save(lowerCamelCase   ,			lowerCamelCase  )
				else:
								UpperCamelCase_:							int		=  DataaVecAudioForCTC(lowerCamelCase  )
								UpperCamelCase_:							Optional[int]		=  checkpoint_path

				def load_dataavec(lowerCamelCase  ):
								UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_:							Optional[int]		=  fairseq.checkpoint_utils.load_model_ensemble_and_task([path]  )
								return model[0].eval()

				UpperCamelCase_:							List[Any]		=  load_dataavec(lowerCamelCase  )

				recursively_load_weights(lowerCamelCase   ,			lowerCamelCase   ,			not is_finetuned  )

				UpperCamelCase_:							Tuple		=  WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""  )

				UpperCamelCase_:							Dict		=  load_dataset("""patrickvonplaten/librispeech_asr_dummy"""   ,			"""clean"""   ,			split="""validation"""  )
				UpperCamelCase_:							Optional[Any]		=  [x["""array"""] for x in ds[:4]["""audio"""]]

				UpperCamelCase_:							List[Any]		=  processor(lowerCamelCase   ,			return_tensors="""pt"""   ,			padding=lowerCamelCase  )

				UpperCamelCase_:							str		=  inputs.input_values
				UpperCamelCase_:							Dict		=  inputs.attention_mask
				#    input_values = inputs.input_values[:, :-1]
				#    attention_mask = inputs.attention_mask[:, :-1]

				hf_wavavec.eval()
				model.eval()
				if is_finetuned:
								UpperCamelCase_:							Optional[Any]		=  model(source=lowerCamelCase   ,			padding_mask=(1 - attention_mask)   ,			mask=lowerCamelCase   ,			features_only=lowerCamelCase  )[
								    """encoder_out"""
								].transpose(0   ,			1  )
								UpperCamelCase_:							Tuple		=  hf_wavavec(lowerCamelCase   ,			attention_mask=lowerCamelCase  )["""logits"""]

								UpperCamelCase_:							Optional[Any]		=  torch.argmax(lowerCamelCase   ,			dim=-1  )
								UpperCamelCase_:							Any		=  processor.batch_decode(lowerCamelCase  )

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

				print(our_output.shape   ,			their_output.shape  )
				UpperCamelCase_:							Optional[Any]		=  torch.max(torch.abs(our_output - their_output  )  ).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
				UpperCamelCase_:							List[str]		=  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__":
							lowerCamelCase_  :       str					      =     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_  :       Dict					      =     parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)






