




import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


__lowerCAmelCase		:    List[Any]	      =		1000




class    snake_case__							:
				"""simple docstring"""



				def __init__(				self    :					Optional[Any] ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					Optional[int]=1.2 ,				__lowerCamelCase    :					List[str]="g" ,				__lowerCamelCase    :					List[Any]=0.5 ,				__lowerCamelCase    :					List[Any]="-" ,				__lowerCamelCase    :					Tuple="test_out.jpg" ,				__lowerCamelCase    :					Any=True ,				__lowerCamelCase    :					List[str]=False ,				__lowerCamelCase    :					Tuple=None ,				__lowerCamelCase    :					str=None ,				__lowerCamelCase    :					str=0.7 ,				) ->					Dict:
							if isinstance(__lowerCamelCase ,				torch.Tensor							):
										a   =      img.numpy().astype("np.uint8"							)
							if isinstance(__lowerCamelCase ,				__lowerCamelCase							):
										a   =      img_tensorize(__lowerCamelCase							)
							assert isinstance(__lowerCamelCase ,				np.ndarray							)

							a   ,		a   =      img.shape[1], img.shape[0]
							a   =      mplfigure.Figure(frameon=__lowerCamelCase							)
							a   =      fig.get_dpi()
							a   =      (width * scale + 1e-2) / dpi
							a   =      (height * scale + 1e-2) / dpi
							fig.set_size_inches(__lowerCamelCase ,				__lowerCamelCase							)
							a   =      fig.add_axes([0.0, 0.0, 1.0, 1.0]							)
							ax.axis("off"							)
							ax.set_xlim(0.0 ,				__lowerCamelCase							)
							ax.set_ylim(__lowerCamelCase							)

							a   =      saveas
							a   =      rgb
							a   =      pynb
							a   =      img
							a   =      edgecolor
							a   =      0.5
							a   =      linestyle
							a   =      int(np.sqrt(min(__lowerCamelCase ,				__lowerCamelCase							)							) * scale // 3							)
							a   =      width
							a   =      height
							a   =      scale
							a   =      fig
							a   =      ax
							a   =      pad
							a   =      idaobj
							a   =      idaattr
							a   =      FigureCanvasAgg(__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					int=None							) ->					Optional[int]:
							if color is None:
										a   =      self.edgecolor
							((a)   ,		(a)   ,		(a)   ,		(a))   =      box
							a   =      xa - xa
							a   =      ya - ya
							self.ax.add_patch(
							    mpl.patches.Rectangle(
							        (xa, ya) ,				__lowerCamelCase ,				__lowerCamelCase ,				fill=__lowerCamelCase ,				edgecolor=__lowerCamelCase ,				linewidth=self.font_size // 3 ,				alpha=self.alpha ,				linestyle=self.linestyle ,				)							)





				def 							__UpperCAmelCase							(				self    :					List[str] ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Tuple=None ,				__lowerCamelCase    :					Union[str, Any]=None ,				__lowerCamelCase    :					Any=None ,				__lowerCamelCase    :					int=None							) ->					int:
							if len(boxes.shape							) > 2:
										a   =      boxes[0]
							if len(obj_ids.shape							) > 1:
										a   =      obj_ids[0]
							if len(obj_scores.shape							) > 1:
										a   =      obj_scores[0]
							if len(attr_ids.shape							) > 1:
										a   =      attr_ids[0]
							if len(attr_scores.shape							) > 1:
										a   =      attr_scores[0]
							if isinstance(__lowerCamelCase ,				torch.Tensor							):
										a   =      boxes.numpy()
							if isinstance(__lowerCamelCase ,				__lowerCamelCase							):
										a   =      np.array(__lowerCamelCase							)
							assert isinstance(__lowerCamelCase ,				np.ndarray							)
							a   =      np.prod(boxes[:, 2:] - boxes[:, :2] ,				axis=1							)
							a   =      np.argsort(-areas							).tolist()
							a   =      boxes[sorted_idxs] if boxes is not None else None
							a   =      obj_ids[sorted_idxs] if obj_ids is not None else None
							a   =      obj_scores[sorted_idxs] if obj_scores is not None else None
							a   =      attr_ids[sorted_idxs] if attr_ids is not None else None
							a   =      attr_scores[sorted_idxs] if attr_scores is not None else None

							a   =      [self._random_color(maximum=1							) for _ in range(len(__lowerCamelCase							)							)]
							a   =      [assigned_colors[idx] for idx in sorted_idxs]
							if obj_ids is not None:
										a   =      self._create_text_labels_attr(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							)
										for i in range(len(__lowerCamelCase							)							):
													a   =      assigned_colors[i]
													self.add_box(boxes[i] ,				__lowerCamelCase							)
													self.draw_labels(labels[i] ,				boxes[i] ,				__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					Tuple							) ->					Union[str, Any]:
							a   ,		a   ,		a   ,		a   =      box
							a   =      (xa, ya)
							a   =      (ya - ya) * (xa - xa)
							a   =      _SMALL_OBJ * self.scale
							if instance_area < small or ya - ya < 40 * self.scale:
										if ya >= self.height - 5:
													a   =      (xa, ya)
										else:
													a   =      (xa, ya)

							a   =      (ya - ya) / np.sqrt(self.height * self.width							)
							a   =      self._change_color_brightness(__lowerCamelCase ,				brightness_factor=0.7							)
							a   =      np.clip((height_ratio - 0.02) / 0.08 + 1 ,				1.2 ,				2							)
							font_size *= 0.75 * self.font_size

							self.draw_text(
							    text=__lowerCamelCase ,				position=__lowerCamelCase ,				color=__lowerCamelCase ,				)





				def 							__UpperCAmelCase							(				self    :					List[str] ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					str="g" ,				__lowerCamelCase    :					int="left" ,				) ->					Dict:
							a   =      0
							a   =      self.font_size
							a   =      np.maximum(list(mplc.to_rgb(__lowerCamelCase							)							) ,				0.2							)
							a   =      max(0.8 ,				np.max(__lowerCamelCase							)							)
							a   =      {
							    "facecolor": "black",
							    "alpha": self.alpha,
							    "pad": self.pad,
							    "edgecolor": "none",
							}
							a   ,		a   =      position
							self.ax.text(
							    __lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				size=font_size * self.scale ,				family="sans-serif" ,				bbox=__lowerCamelCase ,				verticalalignment="top" ,				horizontalalignment=__lowerCamelCase ,				color=__lowerCamelCase ,				zorder=10 ,				rotation=__lowerCamelCase ,				)





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					str=None							) ->					Optional[Any]:
							if saveas is None:
										a   =      self.saveas
							if saveas.lower().endswith(".jpg"							) or saveas.lower().endswith(".png"							):
										cva.imwrite(
										    __lowerCamelCase ,				self._get_buffer()[:, :, ::-1] ,				)
							else:
										self.fig.savefig(__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					Tuple							) ->					str:
							a   =      [self.idaobj[i] for i in classes]
							a   =      [self.idaattr[i] for i in attr_classes]
							a   =      [
							    f"""{label} {score:.2f} {attr} {attr_score:.2f}"""
							    for label, score, attr, attr_score in zip(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase							)
							]
							return labels





				def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					Optional[Any]							) ->					Union[str, Any]:
							a   =      [self.idaobj[i] for i in classes]
							if scores is not None:
										if labels is None:
													a   =      ["{:.0f}%".format(s * 1_00							) for s in scores]
										else:
													a   =      ["{} {:.0f}%".format(__lowerCamelCase ,				s * 1_00							) for li, s in zip(__lowerCamelCase ,				__lowerCamelCase							)]
							return labels





				def 							__UpperCAmelCase							(				self    :					Dict ,				__lowerCamelCase    :					Any=2_55							) ->					List[Any]:
							a   =      np.random.randint(0 ,				len(_COLORS							)							)
							a   =      _COLORS[idx] * maximum
							if not self.rgb:
										a   =      ret[::-1]
							return ret





				def 							__UpperCAmelCase							(				self    :					Any							) ->					Union[str, Any]:
							if not self.pynb:
										a   ,		a   =      self.canvas.print_to_buffer()
										if (width, height) != (self.width, self.height):
													a   =      cva.resize(self.img ,				(width, height)							)
										else:
													a   =      self.img
							else:
										a   =      io.BytesIO()  # works for cairo backend
										self.canvas.print_rgba(__lowerCamelCase							)
										a   ,		a   =      self.width, self.height
										a   =      buf.getvalue()
										a   =      self.img

							a   =      np.frombuffer(__lowerCamelCase ,				dtype="uint8"							)
							a   =      buffer.reshape(__lowerCamelCase ,				__lowerCamelCase ,				4							)
							a   ,		a   =      np.split(__lowerCamelCase ,				[3] ,				axis=2							)

							try:
										import numexpr as ne  # fuse them with numexpr

										a   =      ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"							)
							except ImportError:
										a   =      alpha.astype("float32"							) / 255.0
										a   =      img * (1 - alpha) + rgb * alpha

							return visualized_image.astype("uint8"							)





				def 							__UpperCAmelCase							(				self    :					List[Any] ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					Optional[Any]							) ->					Dict:
							assert brightness_factor >= -1.0 and brightness_factor <= 1.0
							a   =      mplc.to_rgb(__lowerCamelCase							)
							a   =      colorsys.rgb_to_hls(*mplc.to_rgb(__lowerCamelCase							)							)
							a   =      polygon_color[1] + (brightness_factor * polygon_color[1])
							a   =      0.0 if modified_lightness < 0.0 else modified_lightness
							a   =      1.0 if modified_lightness > 1.0 else modified_lightness
							a   =      colorsys.hls_to_rgb(polygon_color[0] ,				__lowerCamelCase ,				polygon_color[2]							)
							return modified_color

__lowerCAmelCase		:    Optional[int]	      =		(
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)












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		:    Dict	      =		logging.get_logger(__name__)

__lowerCAmelCase		:    Tuple	      =		{
    '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		:    List[Any]	      =		[
    'lm_head',
]




def       __magic_name__    (			A		: int,  A		: List[Any],  A		: Tuple,  A		: Dict,  A		: List[str]       ):






			'''simple docstring'''







			for attribute in key.split("."       ):
						a   =      getattr(A,  A       )

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

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




def       __magic_name__    (			A		: Optional[Any],  A		: int,  A		: Dict       ):






			'''simple docstring'''







			a   =      []
			a   =      fairseq_model.state_dict()

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

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

			for name, value in fairseq_dict.items():
						a   =      False
						if "conv_layers" in name:
									load_conv_layer(
									    A,  A,  A,  A,  )
									a   =      True
						elif "pos_conv" in name:
									load_pos_conv_layer(
									    A,  A,  A,  A,  )
									a   =      True
						else:
									for key, mapped_key in MAPPING.items():
												if not is_headless:
															a   =      "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]:
															a   =      True
															if "*" in mapped_key:
																		a   =      name.split(A       )[0].split("."       )[-2]
																		a   =      mapped_key.replace("*",  A       )
															if "weight_g" in name:
																		a   =      "weight_g"
															elif "weight_v" in name:
																		a   =      "weight_v"
															elif "bias" in name:
																		a   =      "bias"
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		a   =      "weight"
															else:
																		a   =      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       __magic_name__    (			A		: str,  A		: Dict       ):






			'''simple docstring'''







			a   =      path.split("."       )
			return reduce(A,  A,  A       )




def       __magic_name__    (			A		: Optional[int],  A		: Tuple,  A		: List[str],  A		: Optional[int]       ):






			'''simple docstring'''







			a   =      access_by_string(A,  A       )
			a   =      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."""       )
			a   =      fsq_value
			logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""       )




def       __magic_name__    (			A		: Optional[Any],  A		: List[str],  A		: Optional[Any],  A		: Optional[int]       ):






			'''simple docstring'''







			a   =      full_name.split("conv_layers."       )[-1]
			a   =      name.split("."       )
			a   =      int(items[0]       )
			a   =      int(items[1]       )

			a   =      name.split("."       )[-1]
			if type_id == 0:
						a   =      "conv"
			elif type_id == 2:
						a   =      "layer_norm"
			else:
						unused_weights.append(A       )
						return

			set_weights(A,  A,  A,  F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""       )




def       __magic_name__    (			A		: int,  A		: Dict,  A		: List[Any],  A		: Tuple       ):






			'''simple docstring'''







			a   =      full_name.split("pos_conv."       )[-1]
			a   =      name.split("."       )
			a   =      int(items[0]       )
			a   =      int(items[1]       )

			a   =      name.split("."       )[-1]
			if type_id != 0:
						unused_weights.append(A       )
						return
			else:
						a   =      "conv"

			set_weights(A,  A,  A,  F"""layers.{layer_id}.{layer_type}.{weight_type}"""       )




@torch.no_grad()
def       __magic_name__    (			A		: str,  A		: Dict,  A		: int=None,  A		: Tuple=None,  A		: List[str]=True       ):






			'''simple docstring'''







			if config_path is not None:
						a   =      DataaVecAudioConfig.from_pretrained(A       )
			else:
						a   =      DataaVecAudioConfig()

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

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

			def load_dataavec(A		: Optional[Any]       ):
						a   ,		a   ,		a   =      fairseq.checkpoint_utils.load_model_ensemble_and_task([path]       )
						return model[0].eval()

			a   =      load_dataavec(A       )

			recursively_load_weights(A,  A,  not is_finetuned       )

			a   =      WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"       )

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

			a   =      processor(A,  return_tensors="pt",  padding=A       )

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

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

						a   =      torch.argmax(A,  dim=-1       )
						a   =      processor.batch_decode(A       )

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

			print(our_output.shape,  their_output.shape       )
			a   =      torch.max(torch.abs(our_output - their_output       )       ).item()
			print(F"""max_absolute_diff = {max_absolute_diff}"""       )  # ~ 1e-7
			a   =      torch.allclose(A,  A,  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(A       )

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


if __name__ == "__main__":
		__lowerCAmelCase		:    List[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		:    int	      =		parser.parse_args()
		convert_wavaveca_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)






