



'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ViltImageProcessor






class        UpperCAmelCase__						(		unittest.TestCase			):



					def __init__(     self      ,					UpperCamelCase      ,					UpperCamelCase=7      ,					UpperCamelCase=3      ,					UpperCamelCase=18      ,					UpperCamelCase=30      ,					UpperCamelCase=400      ,					UpperCamelCase=True      ,					UpperCamelCase=None      ,					UpperCamelCase=2      ,					UpperCamelCase=True      ,					UpperCamelCase=[0.5, 0.5, 0.5]      ,					UpperCamelCase=[0.5, 0.5, 0.5]      ,					)  ->      Dict:
						__lowerCAmelCase       				=	size if size is not None else {"shortest_edge": 30}
						__lowerCAmelCase       				=	parent
						__lowerCAmelCase       				=	batch_size
						__lowerCAmelCase       				=	num_channels
						__lowerCAmelCase       				=	image_size
						__lowerCAmelCase       				=	min_resolution
						__lowerCAmelCase       				=	max_resolution
						__lowerCAmelCase       				=	do_resize
						__lowerCAmelCase       				=	size
						__lowerCAmelCase       				=	size_divisor
						__lowerCAmelCase       				=	do_normalize
						__lowerCAmelCase       				=	image_mean
						__lowerCAmelCase       				=	image_std



					def 							UpperCAmelCase_			(     self  )  ->      Union[str, Any]:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "size_divisor": self.size_divisor,
						}



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase=False  )  ->      Optional[int]:
						if not batched:
							__lowerCAmelCase       				=	self.size["shortest_edge"]
							__lowerCAmelCase       				=	image_inputs[0]
							if isinstance(a_      ,					Image.Image  ):
								__lowerCAmelCase				,							__lowerCAmelCase       				=	image.size
							else:
								__lowerCAmelCase				,							__lowerCAmelCase       				=	image.shape[1], image.shape[2]
							__lowerCAmelCase       				=	size / min(a_      ,					a_  )
							if h < w:
								__lowerCAmelCase				,							__lowerCAmelCase       				=	size, scale * w
							else:
								__lowerCAmelCase				,							__lowerCAmelCase       				=	scale * h, size

							__lowerCAmelCase       				=	int((1333 / 800) * size  )
							if max(a_      ,					a_  ) > max_size:
								__lowerCAmelCase       				=	max_size / max(a_      ,					a_  )
								__lowerCAmelCase       				=	newh * scale
								__lowerCAmelCase       				=	neww * scale

							__lowerCAmelCase				,							__lowerCAmelCase       				=	int(newh + 0.5  ), int(neww + 0.5  )
							__lowerCAmelCase				,							__lowerCAmelCase       				=	(
							    newh // self.size_divisor * self.size_divisor,
							    neww // self.size_divisor * self.size_divisor,
							)

						else:
							__lowerCAmelCase       				=	[]
							for image in image_inputs:
								__lowerCAmelCase				,							__lowerCAmelCase       				=	self.get_expected_values([image]  )
								expected_values.append((expected_height, expected_width)  )
							__lowerCAmelCase       				=	max(a_      ,					key=lambda UpperCamelCase  : item[0]  )[0]
							__lowerCAmelCase       				=	max(a_      ,					key=lambda UpperCamelCase  : item[1]  )[1]

						return expected_height, expected_width






@require_torch
@require_vision
class        UpperCAmelCase__						(		UpperCamelCase__				,							unittest.TestCase			):
					a   :  List[str]   =				ViltImageProcessor if is_vision_available() else None



					def 							UpperCAmelCase_			(     self  )  ->      int:
						__lowerCAmelCase       				=	ViltImageProcessingTester(self  )



					@property
					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()



					def 							UpperCAmelCase_			(     self  )  ->      Tuple:
						__lowerCAmelCase       				=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(a_      ,					"image_mean"  )  )
						self.assertTrue(hasattr(a_      ,					"image_std"  )  )
						self.assertTrue(hasattr(a_      ,					"do_normalize"  )  )
						self.assertTrue(hasattr(a_      ,					"do_resize"  )  )
						self.assertTrue(hasattr(a_      ,					"size"  )  )
						self.assertTrue(hasattr(a_      ,					"size_divisor"  )  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	self.image_processing_class.from_dict(self.image_processor_dict  )
						self.assertEqual(image_processor.size      ,					{"shortest_edge": 30}  )

						__lowerCAmelCase       				=	self.image_processing_class.from_dict(self.image_processor_dict      ,					size=42  )
						self.assertEqual(image_processor.size      ,					{"shortest_edge": 42}  )



					def 							UpperCAmelCase_			(     self  )  ->      int:
						pass



					def 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						# Initialize image_processing
						__lowerCAmelCase       				=	self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						__lowerCAmelCase       				=	prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=a_  )
						for image in image_inputs:
							self.assertIsInstance(a_      ,					Image.Image  )

						# Test not batched input
						__lowerCAmelCase       				=	image_processing(image_inputs[0]      ,					return_tensors="pt"  ).pixel_values

						__lowerCAmelCase				,							__lowerCAmelCase       				=	self.image_processor_tester.get_expected_values(a_  )
						self.assertEqual(
						    encoded_images.shape      ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,					)

						# Test batched
						__lowerCAmelCase       				=	image_processing(a_      ,					return_tensors="pt"  ).pixel_values

						__lowerCAmelCase				,							__lowerCAmelCase       				=	self.image_processor_tester.get_expected_values(a_      ,					batched=a_  )
						self.assertEqual(
						    encoded_images.shape      ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )      ,					)



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						# Initialize image_processing
						__lowerCAmelCase       				=	self.image_processing_class(**self.image_processor_dict  )
						# create random numpy tensors
						__lowerCAmelCase       				=	prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=a_      ,					numpify=a_  )
						for image in image_inputs:
							self.assertIsInstance(a_      ,					np.ndarray  )

						# Test not batched input
						__lowerCAmelCase       				=	image_processing(image_inputs[0]      ,					return_tensors="pt"  ).pixel_values

						__lowerCAmelCase				,							__lowerCAmelCase       				=	self.image_processor_tester.get_expected_values(a_  )
						self.assertEqual(
						    encoded_images.shape      ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,					)

						# Test batched
						__lowerCAmelCase       				=	image_processing(a_      ,					return_tensors="pt"  ).pixel_values

						__lowerCAmelCase				,							__lowerCAmelCase       				=	self.image_processor_tester.get_expected_values(a_      ,					batched=a_  )
						self.assertEqual(
						    encoded_images.shape      ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )      ,					)



					def 							UpperCAmelCase_			(     self  )  ->      int:
						# Initialize image_processing
						__lowerCAmelCase       				=	self.image_processing_class(**self.image_processor_dict  )
						# create random PyTorch tensors
						__lowerCAmelCase       				=	prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=a_      ,					torchify=a_  )
						for image in image_inputs:
							self.assertIsInstance(a_      ,					torch.Tensor  )

						# Test not batched input
						__lowerCAmelCase       				=	image_processing(image_inputs[0]      ,					return_tensors="pt"  ).pixel_values

						__lowerCAmelCase				,							__lowerCAmelCase       				=	self.image_processor_tester.get_expected_values(a_  )
						self.assertEqual(
						    encoded_images.shape      ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,					)

						# Test batched
						__lowerCAmelCase       				=	image_processing(a_      ,					return_tensors="pt"  ).pixel_values

						__lowerCAmelCase				,							__lowerCAmelCase       				=	self.image_processor_tester.get_expected_values(a_      ,					batched=a_  )
						self.assertEqual(
						    encoded_images.shape      ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )      ,					)




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()
UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       """Hello world! cécé herlolip"""

def       A_   (						__a  :					str			,		__a  :					str			,		__a  :					bool		):

				"""simple docstring"""
				a__   ,      a__					   =		os.path.split(__a		)
				a__					   =		DataaVecTextModel.from_pretrained(
				    __a			,		checkpoint_file=__a		)
				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:"""			,		__a		)

				a__					   =		DataaVecTextForSequenceClassification(__a		) if classification_head else DataaVecTextForMaskedLM(__a		)
				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(__a		).unsqueeze(0		)  # batch of size 1

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

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


if __name__ == "__main__":
	UpperCAmelCase							=       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."""
	)
	UpperCAmelCase							=       parser.parse_args()
	convert_dataavec_checkpoint_to_pytorch(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)

