

import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
		raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
		raise Exception('requires fairseq < v2')

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

A									=  'Hello, World!'
A									=  'en_XX'





def a(lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			Path('data_bin'						)
	snake_case_   		=			FairseqXmodModel.from_pretrained(
	    model_name_or_path=str(Path(lowercase__						).parent						)      ,			checkpoint_file=Path(lowercase__						).name      ,			_name='xmod_base'      ,			arch='xmod_base'      ,			task='multilingual_masked_lm'      ,			data_name_or_path=str(lowercase__						)      ,			bpe='sentencepiece'      ,			sentencepiece_model=str(Path(lowercase__						).parent / 'sentencepiece.bpe.model'						)      ,			src_dict=str(data_dir / 'dict.txt'						)      ,			)
	xmod.eval()  # disable dropout
	print(lowercase__						)

	snake_case_   		=			xmod.model.encoder.sentence_encoder
	snake_case_   		=			XmodConfig(
	    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings      ,			hidden_size=xmod.cfg.model.encoder_embed_dim      ,			num_hidden_layers=xmod.cfg.model.encoder_layers      ,			num_attention_heads=xmod.cfg.model.encoder_attention_heads      ,			intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim      ,			max_position_embeddings=514      ,			type_vocab_size=1      ,			layer_norm_eps=1e-5      ,			pre_norm=xmod.cfg.model.encoder_normalize_before      ,			adapter_reduction_factor=getattr(xmod.cfg.model      ,			'bottleneck'      ,			2						)      ,			adapter_layer_norm=xmod.cfg.model.adapter_layer_norm      ,			adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm      ,			ln_before_adapter=xmod.cfg.model.ln_before_adapter      ,			languages=xmod.cfg.model.languages      ,			)
	if classification_head:
		snake_case_   		=			xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

	print('Our X-MOD config:'      ,			lowercase__						)

	snake_case_   		=			XmodForSequenceClassification(lowercase__						) if classification_head else XmodForMaskedLM(lowercase__						)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	snake_case_   		=			xmod_sent_encoder.embed_tokens.weight
	snake_case_   		=			xmod_sent_encoder.embed_positions.weight
	snake_case_   		=			torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight						)  # just zero them out b/c xmod doesn't use them.

	snake_case_   		=			xmod_sent_encoder.layernorm_embedding.weight
	snake_case_   		=			xmod_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers						):
		# Encoder: start of layer
		snake_case_   		=			model.roberta.encoder.layer[i]
		snake_case_   		=			xmod_sent_encoder.layers[i]

		# self attention
		snake_case_   		=			layer.attention.self
		if not (
		    xmod_layer.self_attn.k_proj.weight.data.shape
		    == xmod_layer.self_attn.q_proj.weight.data.shape
		    == xmod_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)						)
		):
			raise AssertionError('Dimensions of self-attention weights do not match.'						)

		snake_case_   		=			xmod_layer.self_attn.q_proj.weight
		snake_case_   		=			xmod_layer.self_attn.q_proj.bias
		snake_case_   		=			xmod_layer.self_attn.k_proj.weight
		snake_case_   		=			xmod_layer.self_attn.k_proj.bias
		snake_case_   		=			xmod_layer.self_attn.v_proj.weight
		snake_case_   		=			xmod_layer.self_attn.v_proj.bias

		# self-attention output
		snake_case_   		=			layer.attention.output
		if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
			raise AssertionError('Dimensions of self-attention output weights do not match.'						)
		snake_case_   		=			xmod_layer.self_attn.out_proj.weight
		snake_case_   		=			xmod_layer.self_attn.out_proj.bias
		snake_case_   		=			xmod_layer.self_attn_layer_norm.weight
		snake_case_   		=			xmod_layer.self_attn_layer_norm.bias

		# intermediate
		snake_case_   		=			layer.intermediate
		if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('Dimensions of intermediate weights do not match.'						)
		snake_case_   		=			xmod_layer.fca.weight
		snake_case_   		=			xmod_layer.fca.bias

		# output
		snake_case_   		=			layer.output
		if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('Dimensions of feed-forward weights do not match.'						)
		snake_case_   		=			xmod_layer.fca.weight
		snake_case_   		=			xmod_layer.fca.bias
		snake_case_   		=			xmod_layer.final_layer_norm.weight
		snake_case_   		=			xmod_layer.final_layer_norm.bias
		if bert_output.adapter_layer_norm is not None:
			snake_case_   		=			xmod_layer.adapter_layer_norm.weight
			snake_case_   		=			xmod_layer.adapter_layer_norm.bias

		if sorted(bert_output.adapter_modules.keys()						) != sorted(xmod_layer.adapter_modules.keys()						):
			raise AssertionError('Lists of language adapters do not match.'						)
		for lang_code, adapter in xmod_layer.adapter_modules.items():
			snake_case_   		=			bert_output.adapter_modules[lang_code]
			snake_case_   		=			xmod_layer.adapter_modules[lang_code]
			snake_case_   		=			from_adapter.fca.weight
			snake_case_   		=			from_adapter.fca.bias
			snake_case_   		=			from_adapter.fca.weight
			snake_case_   		=			from_adapter.fca.bias

		# end of layer

	if xmod_sent_encoder.layer_norm is not None:
		snake_case_   		=			xmod_sent_encoder.layer_norm.weight
		snake_case_   		=			xmod_sent_encoder.layer_norm.bias

	if classification_head:
		snake_case_   		=			xmod.model.classification_heads['mnli'].dense.weight
		snake_case_   		=			xmod.model.classification_heads['mnli'].dense.bias
		snake_case_   		=			xmod.model.classification_heads['mnli'].out_proj.weight
		snake_case_   		=			xmod.model.classification_heads['mnli'].out_proj.bias
	else:
		# LM Head
		snake_case_   		=			xmod.model.encoder.lm_head.dense.weight
		snake_case_   		=			xmod.model.encoder.lm_head.dense.bias
		snake_case_   		=			xmod.model.encoder.lm_head.layer_norm.weight
		snake_case_   		=			xmod.model.encoder.lm_head.layer_norm.bias
		snake_case_   		=			xmod.model.encoder.lm_head.weight
		snake_case_   		=			xmod.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	snake_case_   		=			xmod.encode(lowercase__						).unsqueeze(0						)  # batch of size 1
	model.roberta.set_default_language(lowercase__						)

	snake_case_   		=			model(lowercase__						)[0]
	if classification_head:
		snake_case_   		=			xmod.model.classification_heads['mnli'](xmod.extract_features(lowercase__						)						)
	else:
		snake_case_   		=			xmod.model(lowercase__      ,			lang_id=[SAMPLE_LANGUAGE]						)[0]
	print(our_output.shape      ,			their_output.shape						)
	snake_case_   		=			torch.max(torch.abs(our_output - their_output						)						).item()
	print(f"""max_absolute_diff = {max_absolute_diff}"""						)  # ~ 1e-7
	snake_case_   		=			torch.allclose(lowercase__      ,			lowercase__      ,			atol=1e-3						)
	print('Do both models output the same tensors?'      ,			'🔥' if success else '💩'						)
	if not success:
		raise Exception('Something went wRoNg'						)

	Path(lowercase__						).mkdir(parents=lowercase__      ,			exist_ok=lowercase__						)
	print(f"""Saving model to {pytorch_dump_folder_path}"""						)
	model.save_pretrained(lowercase__						)


if __name__ == "__main__":
		A									=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xmod_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_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor


class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase=7			,					__UpperCamelCase=3			,					__UpperCamelCase=30			,					__UpperCamelCase=4_00			,					__UpperCamelCase=True			,					__UpperCamelCase=None			,					__UpperCamelCase=True			,					__UpperCamelCase=[0.5, 0.5, 0.5]			,					__UpperCamelCase=[0.5, 0.5, 0.5]			,					__UpperCamelCase=True			,					__UpperCamelCase=1 / 2_55			,					__UpperCamelCase=True			,					):

				"""simple docstring"""


				snake_case_   		=			size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
				snake_case_   		=			parent
				snake_case_   		=			batch_size
				snake_case_   		=			num_channels
				snake_case_   		=			min_resolution
				snake_case_   		=			max_resolution
				snake_case_   		=			do_resize
				snake_case_   		=			size
				snake_case_   		=			do_normalize
				snake_case_   		=			image_mean
				snake_case_   		=			image_std
				snake_case_   		=			do_rescale
				snake_case_   		=			rescale_factor
				snake_case_   		=			do_pad


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_rescale": self.do_rescale,
				    "rescale_factor": self.rescale_factor,
				    "do_pad": self.do_pad,
				}







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=False				):

				"""simple docstring"""


				if not batched:
					snake_case_   		=			image_inputs[0]
					if isinstance(__UpperCamelCase			,					Image.Image				):
						snake_case_		,							snake_case_   		=			image.size
					else:
						snake_case_		,							snake_case_   		=			image.shape[1], image.shape[2]
					if w < h:
						snake_case_   		=			int(self.size['shortest_edge'] * h / w				)
						snake_case_   		=			self.size['shortest_edge']
					elif w > h:
						snake_case_   		=			self.size['shortest_edge']
						snake_case_   		=			int(self.size['shortest_edge'] * w / h				)
					else:
						snake_case_   		=			self.size['shortest_edge']
						snake_case_   		=			self.size['shortest_edge']

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

				return expected_height, expected_width







@require_torch
@require_vision
class SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	DetaImageProcessor if is_vision_available() else None


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			DetaImageProcessingTester(self				)


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.image_processor_tester.prepare_image_processor_dict()


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class(**self.image_processor_dict				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'image_mean'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'image_std'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'do_normalize'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'do_resize'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'do_rescale'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'do_pad'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'size'				)				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class.from_dict(self.image_processor_dict				)
				self.assertEqual(image_processor.size			,					{'shortest_edge': 18, 'longest_edge': 13_33}				)
				self.assertEqual(image_processor.do_pad			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				pass


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class(**self.image_processor_dict				)
				# create random PIL images
				snake_case_   		=			prepare_image_inputs(self.image_processor_tester			,					equal_resolution=__UpperCamelCase				)
				for image in image_inputs:
					self.assertIsInstance(__UpperCamelCase			,					Image.Image				)

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

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase				)

				self.assertEqual(
				    encoded_images.shape			,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,					)

				# Test batched
				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase			,					batched=__UpperCamelCase				)

				snake_case_   		=			image_processing(__UpperCamelCase			,					return_tensors='pt'				).pixel_values
				self.assertEqual(
				    encoded_images.shape			,					(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )			,					)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class(**self.image_processor_dict				)
				# create random numpy tensors
				snake_case_   		=			prepare_image_inputs(self.image_processor_tester			,					equal_resolution=__UpperCamelCase			,					numpify=__UpperCamelCase				)
				for image in image_inputs:
					self.assertIsInstance(__UpperCamelCase			,					np.ndarray				)

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

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase				)

				self.assertEqual(
				    encoded_images.shape			,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,					)

				# Test batched
				snake_case_   		=			image_processing(__UpperCamelCase			,					return_tensors='pt'				).pixel_values

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase			,					batched=__UpperCamelCase				)

				self.assertEqual(
				    encoded_images.shape			,					(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )			,					)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class(**self.image_processor_dict				)
				# create random PyTorch tensors
				snake_case_   		=			prepare_image_inputs(self.image_processor_tester			,					equal_resolution=__UpperCamelCase			,					torchify=__UpperCamelCase				)
				for image in image_inputs:
					self.assertIsInstance(__UpperCamelCase			,					torch.Tensor				)

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

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase				)

				self.assertEqual(
				    encoded_images.shape			,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,					)

				# Test batched
				snake_case_   		=			image_processing(__UpperCamelCase			,					return_tensors='pt'				).pixel_values

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase			,					batched=__UpperCamelCase				)

				self.assertEqual(
				    encoded_images.shape			,					(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )			,					)


			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
				with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'			,					'r'				) as f:
					snake_case_   		=			json.loads(f.read()				)

				snake_case_   		=			{'image_id': 3_97_69, 'annotations': target}

				# encode them
				snake_case_   		=			DetaImageProcessor()
				snake_case_   		=			image_processing(images=__UpperCamelCase			,					annotations=__UpperCamelCase			,					return_tensors='pt'				)

				# verify pixel values
				snake_case_   		=			torch.Size([1, 3, 8_00, 10_66]				)
				self.assertEqual(encoding['pixel_values'].shape			,					__UpperCamelCase				)

				snake_case_   		=			torch.tensor([0.2796, 0.3138, 0.3481]				)
				self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			,					__UpperCamelCase			,					atol=1E-4				)				)

				# verify area
				snake_case_   		=			torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,					__UpperCamelCase				)				)
				# verify boxes
				snake_case_   		=			torch.Size([6, 4]				)
				self.assertEqual(encoding['labels'][0]['boxes'].shape			,					__UpperCamelCase				)
				snake_case_   		=			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,					__UpperCamelCase			,					atol=1E-3				)				)
				# verify image_id
				snake_case_   		=			torch.tensor([3_97_69]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,					__UpperCamelCase				)				)
				# verify is_crowd
				snake_case_   		=			torch.tensor([0, 0, 0, 0, 0, 0]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,					__UpperCamelCase				)				)
				# verify class_labels
				snake_case_   		=			torch.tensor([75, 75, 63, 65, 17, 17]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,					__UpperCamelCase				)				)
				# verify orig_size
				snake_case_   		=			torch.tensor([4_80, 6_40]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,					__UpperCamelCase				)				)
				# verify size
				snake_case_   		=			torch.tensor([8_00, 10_66]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,					__UpperCamelCase				)				)







			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
				with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'			,					'r'				) as f:
					snake_case_   		=			json.loads(f.read()				)

				snake_case_   		=			{'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

				snake_case_   		=			pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'				)

				# encode them
				snake_case_   		=			DetaImageProcessor(format='coco_panoptic'				)
				snake_case_   		=			image_processing(images=__UpperCamelCase			,					annotations=__UpperCamelCase			,					masks_path=__UpperCamelCase			,					return_tensors='pt'				)

				# verify pixel values
				snake_case_   		=			torch.Size([1, 3, 8_00, 10_66]				)
				self.assertEqual(encoding['pixel_values'].shape			,					__UpperCamelCase				)

				snake_case_   		=			torch.tensor([0.2796, 0.3138, 0.3481]				)
				self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]			,					__UpperCamelCase			,					atol=1E-4				)				)

				# verify area
				snake_case_   		=			torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']			,					__UpperCamelCase				)				)
				# verify boxes
				snake_case_   		=			torch.Size([6, 4]				)
				self.assertEqual(encoding['labels'][0]['boxes'].shape			,					__UpperCamelCase				)
				snake_case_   		=			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,					__UpperCamelCase			,					atol=1E-3				)				)
				# verify image_id
				snake_case_   		=			torch.tensor([3_97_69]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,					__UpperCamelCase				)				)
				# verify is_crowd
				snake_case_   		=			torch.tensor([0, 0, 0, 0, 0, 0]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,					__UpperCamelCase				)				)
				# verify class_labels
				snake_case_   		=			torch.tensor([17, 17, 63, 75, 75, 93]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,					__UpperCamelCase				)				)
				# verify masks
				snake_case_   		=			82_28_73
				self.assertEqual(encoding['labels'][0]['masks'].sum().item()			,					__UpperCamelCase				)
				# verify orig_size
				snake_case_   		=			torch.tensor([4_80, 6_40]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,					__UpperCamelCase				)				)
				# verify size
				snake_case_   		=			torch.tensor([8_00, 10_66]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,					__UpperCamelCase				)				)
