
"""simple docstring"""


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


__A       						=       1000







class 		lowerCamelCase__					:





			'''simple docstring'''



			def __init__(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase=1.2     ,     __UpperCAmelCase="g"     ,     __UpperCAmelCase=0.5     ,     __UpperCAmelCase="-"     ,     __UpperCAmelCase="test_out.jpg"     ,     __UpperCAmelCase=True     ,     __UpperCAmelCase=False     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=0.7     ,     ) ->     Union[str, Any]:
				if isinstance(__UpperCAmelCase     ,     torch.Tensor  ):
					_lowerCAmelCase								=img.numpy().astype("""np.uint8"""  )
				if isinstance(__UpperCAmelCase     ,     __UpperCAmelCase  ):
					_lowerCAmelCase								=img_tensorize(__UpperCAmelCase  )
				assert isinstance(__UpperCAmelCase     ,     np.ndarray  )

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

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



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase=None  ) ->     List[str]:
				if color is None:
					_lowerCAmelCase								=self.edgecolor
				((_lowerCAmelCase)						,							(_lowerCAmelCase)						,							(_lowerCAmelCase)						,							(_lowerCAmelCase))								=box
				_lowerCAmelCase								=xa - xa
				_lowerCAmelCase								=ya - ya
				self.ax.add_patch(
				    mpl.patches.Rectangle(
				        (xa, ya)     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     fill=__UpperCAmelCase     ,     edgecolor=__UpperCAmelCase     ,     linewidth=self.font_size // 3     ,     alpha=self.alpha     ,     linestyle=self.linestyle     ,     )  )



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

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



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase  ) ->     List[str]:
				_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase								=box
				_lowerCAmelCase								=(xa, ya)
				_lowerCAmelCase								=(ya - ya) * (xa - xa)
				_lowerCAmelCase								=_SMALL_OBJ * self.scale
				if instance_area < small or ya - ya < 40 * self.scale:
					if ya >= self.height - 5:
						_lowerCAmelCase								=(xa, ya)
					else:
						_lowerCAmelCase								=(xa, ya)

				_lowerCAmelCase								=(ya - ya) / np.sqrt(self.height * self.width  )
				_lowerCAmelCase								=self._change_color_brightness(__UpperCAmelCase     ,     brightness_factor=0.7  )
				_lowerCAmelCase								=np.clip((height_ratio - 0.0_2) / 0.0_8 + 1     ,     1.2     ,     2  )
				font_size *= 0.7_5 * self.font_size

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



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



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



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase  ) ->     Optional[Any]:
				_lowerCAmelCase								=[self.idaobj[i] for i in classes]
				_lowerCAmelCase								=[self.idaattr[i] for i in attr_classes]
				_lowerCAmelCase								=[
				    f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
				    for label, score, attr, attr_score in zip(__UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase  )
				]
				return labels



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



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



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

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

				try:
					import numexpr as ne  # fuse them with numexpr

					_lowerCAmelCase								=ne.evaluate("""img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"""  )
				except ImportError:
					_lowerCAmelCase								=alpha.astype("""float32"""  ) / 2_5_5.0
					_lowerCAmelCase								=img * (1 - alpha) + rgb * alpha

				return visualized_image.astype("""uint8"""  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase  ) ->     Tuple:
				assert brightness_factor >= -1.0 and brightness_factor <= 1.0
				_lowerCAmelCase								=mplc.to_rgb(__UpperCAmelCase  )
				_lowerCAmelCase								=colorsys.rgb_to_hls(*mplc.to_rgb(__UpperCAmelCase  )  )
				_lowerCAmelCase								=polygon_color[1] + (brightness_factor * polygon_color[1])
				_lowerCAmelCase								=0.0 if modified_lightness < 0.0 else modified_lightness
				_lowerCAmelCase								=1.0 if modified_lightness > 1.0 else modified_lightness
				_lowerCAmelCase								=colorsys.hls_to_rgb(polygon_color[0]     ,     __UpperCAmelCase     ,     polygon_color[2]  )
				return modified_color







__A       						=       (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)





"""simple docstring"""


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
			raise Exception('requires fairseq >= 0.9.0')


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

__A       						=       'Hello world! cécé herlolip'



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Any:
	_lowerCAmelCase						,							_lowerCAmelCase								=os.path.split(__UpperCamelCase       )
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase    , checkpoint_file=__UpperCamelCase       )
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=DataaVecTextConfig(
	    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings    , hidden_size=dataavec_model.args.encoder_embed_dim    , num_hidden_layers=dataavec_model.args.encoder_layers    , num_attention_heads=dataavec_model.args.encoder_attention_heads    , intermediate_size=dataavec_model.args.encoder_ffn_embed_dim    , max_position_embeddings=514    , type_vocab_size=1    , layer_norm_eps=1E-5    , )
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""    , __UpperCamelCase       )

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase       ) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase       )
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	_lowerCAmelCase								=dataavec_sent_encoder.embed_tokens.weight
	_lowerCAmelCase								=dataavec_sent_encoder.embed_positions.weight
	_lowerCAmelCase								=torch.zeros_like(
	    model.dataavec_text.embeddings.token_type_embeddings.weight       )  # just zero them out b/c data2vec doesn't use them.
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.weight
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers       ):
		# Encoder: start of layer
		_lowerCAmelCase								=model.dataavec_text.encoder.layer[i]
		_lowerCAmelCase								=dataavec_sent_encoder.layers[i]

		# self attention
		_lowerCAmelCase								=layer.attention.self
		assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)
		assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)
		assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)

		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		_lowerCAmelCase								=layer.attention.output
		assert (
		    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
		), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=layer.intermediate
		assert (
		    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=layer.output
		assert (
		    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.bias
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.bias
	else:
		# LM Head
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.bias

	# Let's check that we get the same results.
	_lowerCAmelCase								=dataavec.encode(__UpperCamelCase       ).unsqueeze(0       )  # batch of size 1

	_lowerCAmelCase								=model(__UpperCamelCase       )[0]
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__UpperCamelCase       )       )
	else:
		_lowerCAmelCase								=dataavec_model(__UpperCamelCase       )[0]
	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(__UpperCamelCase    , __UpperCamelCase    , atol=1E-3       )
	print("""Do both models output the same tensors?"""    , """🔥""" if success else """💩"""       )
	if not success:
		raise Exception("""Something went wRoNg"""       )

	pathlib.Path(__UpperCamelCase       ).mkdir(parents=__UpperCamelCase    , exist_ok=__UpperCamelCase       )
	print(F'''Saving model to {pytorch_dump_folder_path}'''       )
	model.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
			__A       						=       argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
			)
			__A       						=       parser.parse_args()
			convert_dataavec_checkpoint_to_pytorch(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



