
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    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->List[Any]:
		A			,    A				=os.path.split(__UpperCamelCase		)
		A				=DataaVecTextModel.from_pretrained(
		    __UpperCamelCase  ,				checkpoint_file=__UpperCamelCase		)
		dataavec.eval()  # disable dropout
		A				=dataavec.models[0]
		A				=dataavec_model.encoder.sentence_encoder
		A				=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:
				A				=dataavec.model.classification_heads["mnli"].out_proj.weight.shape[0]
		print("Our BERT config:"  ,				__UpperCamelCase		)

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

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

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

				# self attention
				A				=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)		)}'''
				)

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

				# self-attention output
				A				=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}'''
				A				=dataavec_layer.self_attn.out_proj.weight
				A				=dataavec_layer.self_attn.out_proj.bias
				A				=dataavec_layer.self_attn_layer_norm.weight
				A				=dataavec_layer.self_attn_layer_norm.bias

				# intermediate
				A				=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}'''
				A				=dataavec_layer.fca.weight
				A				=dataavec_layer.fca.bias

				# output
				A				=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}'''
				A				=dataavec_layer.fca.weight
				A				=dataavec_layer.fca.bias
				A				=dataavec_layer.final_layer_norm.weight
				A				=dataavec_layer.final_layer_norm.bias
				# end of layer

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

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

		A				=model(__UpperCamelCase		)[0]
		if classification_head:
				A				=dataavec.model.classification_heads["mnli"](dataavec.extract_features(__UpperCamelCase		)		)
		else:
				A				=dataavec_model(__UpperCamelCase		)[0]
		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(__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
	)






"""simple docstring"""


import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 						lowerCamelCase__   (   unittest.TestCase							):





			'''simple docstring'''



			@property
			def    _lowerCAmelCase						(				self    )					->	List[str]:
				_lowerCAmelCase								=1
				_lowerCAmelCase								=3
				_lowerCAmelCase								=8
				_lowerCAmelCase								=8

				_lowerCAmelCase								=torch.rand((batch_size, num_channels, height, width)    )

				return sample



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase    )					->	Any:
				if isinstance(image[0]	,	PIL.Image.Image    ):
					return np.stack([np.array(__UpperCAmelCase    ) for i in image]	,	axis=0    )
				elif isinstance(__UpperCAmelCase	,	torch.Tensor    ):
					return image.cpu().numpy().transpose(0	,	2	,	3	,	1    )
				return image



			def    _lowerCAmelCase						(				self    )					->	Tuple:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )

				_lowerCAmelCase								=self.dummy_sample
				_lowerCAmelCase								=self.to_np(__UpperCAmelCase    )

				for output_type in ["pt", "np", "pil"]:
					_lowerCAmelCase								=image_processor.postprocess(
					    image_processor.preprocess(__UpperCAmelCase    )	,	output_type=__UpperCAmelCase	,	)
					_lowerCAmelCase								=self.to_np(__UpperCAmelCase    )
					_lowerCAmelCase								=(input_np * 2_55).round() if output_type == """pil""" else input_np
					assert (
					    np.abs(in_np - out_np    ).max() < 1e-6
					), f'''decoded output does not match input for output_type {output_type}'''



			def    _lowerCAmelCase						(				self    )					->	Union[str, Any]:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )
				_lowerCAmelCase								=self.dummy_sample.cpu().numpy().transpose(0	,	2	,	3	,	1    )

				for output_type in ["pt", "np", "pil"]:
					_lowerCAmelCase								=image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )	,	output_type=__UpperCAmelCase    )

					_lowerCAmelCase								=self.to_np(__UpperCAmelCase    )
					_lowerCAmelCase								=(input_np * 2_55).round() if output_type == """pil""" else input_np
					assert (
					    np.abs(in_np - out_np    ).max() < 1e-6
					), f'''decoded output does not match input for output_type {output_type}'''



			def    _lowerCAmelCase						(				self    )					->	List[str]:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )

				_lowerCAmelCase								=self.dummy_sample.cpu().numpy().transpose(0	,	2	,	3	,	1    )
				_lowerCAmelCase								=image_processor.numpy_to_pil(__UpperCAmelCase    )

				for output_type in ["pt", "np", "pil"]:
					_lowerCAmelCase								=image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )	,	output_type=__UpperCAmelCase    )
					for i, o in zip(__UpperCAmelCase	,	__UpperCAmelCase    ):
						_lowerCAmelCase								=np.array(__UpperCAmelCase    )
						_lowerCAmelCase								=self.to_np(__UpperCAmelCase    ) if output_type == """pil""" else (self.to_np(__UpperCAmelCase    ) * 2_55).round()
						assert (
						    np.abs(in_np - out_np    ).max() < 1e-6
						), f'''decoded output does not match input for output_type {output_type}'''



			def    _lowerCAmelCase						(				self    )					->	Tuple:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )

				_lowerCAmelCase								=self.dummy_sample
				_lowerCAmelCase								=input_pt_ad.squeeze(0    )

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)
				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				_lowerCAmelCase								=self.to_np(self.dummy_sample    )
				_lowerCAmelCase								=input_np_ad.squeeze(0    )

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)
				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				assert np.abs(out_pt_ad - out_pt_ad    ).max() < 1e-6
				assert np.abs(out_np_ad - out_np_ad    ).max() < 1e-6



			def    _lowerCAmelCase						(				self    )					->	Optional[Any]:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )

				_lowerCAmelCase								=self.dummy_sample
				_lowerCAmelCase								=list(__UpperCAmelCase    )

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				_lowerCAmelCase								=self.to_np(self.dummy_sample    )
				list(__UpperCAmelCase    )

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				assert np.abs(out_pt_ad - out_pt_list    ).max() < 1e-6
				assert np.abs(out_np_ad - out_np_list    ).max() < 1e-6



