






'''simple docstring'''




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()
lowerCAmelCase__          =						logging.get_logger(__name__)

lowerCAmelCase__          =						"""Hello, World!"""
lowerCAmelCase__          =						"""en_XX"""







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Dict:
		UpperCamelCase__				:					Any					=					Path('data_bin')
		UpperCamelCase__				:					int					=					FairseqXmodModel.from_pretrained(
		    model_name_or_path=str(Path(_lowerCamelCase).parent)     ,      checkpoint_file=Path(_lowerCamelCase).name     ,      _name='xmod_base'     ,      arch='xmod_base'     ,      task='multilingual_masked_lm'     ,      data_name_or_path=str(_lowerCamelCase)     ,      bpe='sentencepiece'     ,      sentencepiece_model=str(Path(_lowerCamelCase).parent / 'sentencepiece.bpe.model')     ,      src_dict=str(data_dir / 'dict.txt')     ,      )
		xmod.eval()  # disable dropout
		print(_lowerCamelCase)

		UpperCamelCase__				:					Dict					=					xmod.model.encoder.sentence_encoder
		UpperCamelCase__				:					str					=					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:
				UpperCamelCase__				:					Tuple					=					xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

		UpperCamelCase__				:					List[str]					=					XmodForSequenceClassification(_lowerCamelCase) if classification_head else XmodForMaskedLM(_lowerCamelCase)
		model.eval()

		# Now let's copy all the weights.
		# Embeddings
		UpperCamelCase__				:					str					=					xmod_sent_encoder.embed_tokens.weight
		UpperCamelCase__				:					int					=					xmod_sent_encoder.embed_positions.weight
		UpperCamelCase__				:					List[Any]					=					torch.zeros_like(
		    model.roberta.embeddings.token_type_embeddings.weight)  # just zero them out b/c xmod doesn't use them.

		UpperCamelCase__				:					Any					=					xmod_sent_encoder.layernorm_embedding.weight
		UpperCamelCase__				:					Tuple					=					xmod_sent_encoder.layernorm_embedding.bias

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

				# self attention
				UpperCamelCase__				:					Any					=					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.')

				UpperCamelCase__				:					List[Any]					=					xmod_layer.self_attn.q_proj.weight
				UpperCamelCase__				:					Tuple					=					xmod_layer.self_attn.q_proj.bias
				UpperCamelCase__				:					str					=					xmod_layer.self_attn.k_proj.weight
				UpperCamelCase__				:					Optional[Any]					=					xmod_layer.self_attn.k_proj.bias
				UpperCamelCase__				:					Tuple					=					xmod_layer.self_attn.v_proj.weight
				UpperCamelCase__				:					List[str]					=					xmod_layer.self_attn.v_proj.bias

				# self-attention output
				UpperCamelCase__				:					List[Any]					=					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.')
				UpperCamelCase__				:					Optional[Any]					=					xmod_layer.self_attn.out_proj.weight
				UpperCamelCase__				:					int					=					xmod_layer.self_attn.out_proj.bias
				UpperCamelCase__				:					Any					=					xmod_layer.self_attn_layer_norm.weight
				UpperCamelCase__				:					List[Any]					=					xmod_layer.self_attn_layer_norm.bias

				# intermediate
				UpperCamelCase__				:					int					=					layer.intermediate
				if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
						raise AssertionError('Dimensions of intermediate weights do not match.')
				UpperCamelCase__				:					int					=					xmod_layer.fca.weight
				UpperCamelCase__				:					List[str]					=					xmod_layer.fca.bias

				# output
				UpperCamelCase__				:					int					=					layer.output
				if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
						raise AssertionError('Dimensions of feed-forward weights do not match.')
				UpperCamelCase__				:					Optional[Any]					=					xmod_layer.fca.weight
				UpperCamelCase__				:					int					=					xmod_layer.fca.bias
				UpperCamelCase__				:					Union[str, Any]					=					xmod_layer.final_layer_norm.weight
				UpperCamelCase__				:					int					=					xmod_layer.final_layer_norm.bias
				if bert_output.adapter_layer_norm is not None:
						UpperCamelCase__				:					str					=					xmod_layer.adapter_layer_norm.weight
						UpperCamelCase__				:					int					=					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():
						UpperCamelCase__				:					Optional[int]					=					bert_output.adapter_modules[lang_code]
						UpperCamelCase__				:					Optional[int]					=					xmod_layer.adapter_modules[lang_code]
						UpperCamelCase__				:					Union[str, Any]					=					from_adapter.fca.weight
						UpperCamelCase__				:					Tuple					=					from_adapter.fca.bias
						UpperCamelCase__				:					Optional[int]					=					from_adapter.fca.weight
						UpperCamelCase__				:					Dict					=					from_adapter.fca.bias

				# end of layer

		if xmod_sent_encoder.layer_norm is not None:
				UpperCamelCase__				:					Any					=					xmod_sent_encoder.layer_norm.weight
				UpperCamelCase__				:					Optional[Any]					=					xmod_sent_encoder.layer_norm.bias

		if classification_head:
				UpperCamelCase__				:					Optional[Any]					=					xmod.model.classification_heads['mnli'].dense.weight
				UpperCamelCase__				:					List[str]					=					xmod.model.classification_heads['mnli'].dense.bias
				UpperCamelCase__				:					Any					=					xmod.model.classification_heads['mnli'].out_proj.weight
				UpperCamelCase__				:					Any					=					xmod.model.classification_heads['mnli'].out_proj.bias
		else:
				# LM Head
				UpperCamelCase__				:					List[str]					=					xmod.model.encoder.lm_head.dense.weight
				UpperCamelCase__				:					Tuple					=					xmod.model.encoder.lm_head.dense.bias
				UpperCamelCase__				:					Optional[Any]					=					xmod.model.encoder.lm_head.layer_norm.weight
				UpperCamelCase__				:					Dict					=					xmod.model.encoder.lm_head.layer_norm.bias
				UpperCamelCase__				:					List[str]					=					xmod.model.encoder.lm_head.weight
				UpperCamelCase__				:					Optional[int]					=					xmod.model.encoder.lm_head.bias

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

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

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


if __name__ == "__main__":
		lowerCAmelCase__          =						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.'
		)
		lowerCAmelCase__          =						parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)












'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPImageProcessor, CLIPProcessor







@require_vision
class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					int					=					tempfile.mkdtemp()

			# fmt: off
			UpperCamelCase__				:					Union[str, Any]					=					['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
			# fmt: on
			UpperCamelCase__				:					Dict					=					dict(zip(UpperCAmelCase_  ,					range(len(UpperCAmelCase_))))
			UpperCamelCase__				:					Optional[Any]					=					['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
			UpperCamelCase__				:					Union[str, Any]					=					{'unk_token': '<unk>'}

			UpperCamelCase__				:					Union[str, Any]					=					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['vocab_file'])
			UpperCamelCase__				:					Optional[Any]					=					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['merges_file'])
			with open(self.vocab_file  ,					'w'  ,					encoding='utf-8') as fp:
					fp.write(json.dumps(UpperCAmelCase_) + '\n')
			with open(self.merges_file  ,					'w'  ,					encoding='utf-8') as fp:
					fp.write('\n'.join(UpperCAmelCase_))

			UpperCamelCase__				:					Dict					=					{
			    'do_resize': True,
			    'size': 20,
			    'do_center_crop': True,
			    'crop_size': 18,
			    'do_normalize': True,
			    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
			    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
			}
			UpperCamelCase__				:					Any					=					os.path.join(self.tmpdirname  ,					UpperCAmelCase_)
			with open(self.image_processor_file  ,					'w'  ,					encoding='utf-8') as fp:
					json.dump(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict  ,					**UpperCAmelCase_   :						Union[str, Any]):
			return CLIPTokenizer.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					**UpperCAmelCase_   :						str):
			return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[Any]  ,					**UpperCAmelCase_   :						Union[str, Any]):
			return CLIPImageProcessor.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str):
			shutil.rmtree(self.tmpdirname)

	def      __UpperCamelCase							(      self   :						Tuple):
			UpperCamelCase__				:					List[str]					=					[np.random.randint(255  ,					size=(3, 30, 400)  ,					dtype=np.uinta)]

			UpperCamelCase__				:					List[str]					=					[Image.fromarray(np.moveaxis(UpperCAmelCase_  ,					0  ,					-1)) for x in image_inputs]

			return image_inputs

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Union[str, Any]					=					self.get_tokenizer()
			UpperCamelCase__				:					Optional[Any]					=					self.get_rust_tokenizer()
			UpperCamelCase__				:					Any					=					self.get_image_processor()

			UpperCamelCase__				:					str					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)
			processor_slow.save_pretrained(self.tmpdirname)
			UpperCamelCase__				:					Any					=					CLIPProcessor.from_pretrained(self.tmpdirname  ,					use_fast=UpperCAmelCase_)

			UpperCamelCase__				:					str					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)
			processor_fast.save_pretrained(self.tmpdirname)
			UpperCamelCase__				:					Optional[int]					=					CLIPProcessor.from_pretrained(self.tmpdirname)

			self.assertEqual(processor_slow.tokenizer.get_vocab()  ,					tokenizer_slow.get_vocab())
			self.assertEqual(processor_fast.tokenizer.get_vocab()  ,					tokenizer_fast.get_vocab())
			self.assertEqual(tokenizer_slow.get_vocab()  ,					tokenizer_fast.get_vocab())
			self.assertIsInstance(processor_slow.tokenizer  ,					UpperCAmelCase_)
			self.assertIsInstance(processor_fast.tokenizer  ,					UpperCAmelCase_)

			self.assertEqual(processor_slow.image_processor.to_json_string()  ,					image_processor.to_json_string())
			self.assertEqual(processor_fast.image_processor.to_json_string()  ,					image_processor.to_json_string())
			self.assertIsInstance(processor_slow.image_processor  ,					UpperCAmelCase_)
			self.assertIsInstance(processor_fast.image_processor  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__				:					Union[str, Any]					=					CLIPProcessor(tokenizer=self.get_tokenizer()  ,					image_processor=self.get_image_processor())
			processor.save_pretrained(self.tmpdirname)

			UpperCamelCase__				:					List[str]					=					self.get_tokenizer(bos_token='(BOS)'  ,					eos_token='(EOS)')
			UpperCamelCase__				:					Tuple					=					self.get_image_processor(do_normalize=UpperCAmelCase_  ,					padding_value=1.0)

			UpperCamelCase__				:					Dict					=					CLIPProcessor.from_pretrained(
			    self.tmpdirname  ,					bos_token='(BOS)'  ,					eos_token='(EOS)'  ,					do_normalize=UpperCAmelCase_  ,					padding_value=1.0)

			self.assertEqual(processor.tokenizer.get_vocab()  ,					tokenizer_add_kwargs.get_vocab())
			self.assertIsInstance(processor.tokenizer  ,					UpperCAmelCase_)

			self.assertEqual(processor.image_processor.to_json_string()  ,					image_processor_add_kwargs.to_json_string())
			self.assertIsInstance(processor.image_processor  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Optional[Any]					=					self.get_image_processor()
			UpperCamelCase__				:					int					=					self.get_tokenizer()

			UpperCamelCase__				:					List[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					int					=					self.prepare_image_inputs()

			UpperCamelCase__				:					int					=					image_processor(UpperCAmelCase_  ,					return_tensors='np')
			UpperCamelCase__				:					Optional[int]					=					processor(images=UpperCAmelCase_  ,					return_tensors='np')

			for key in input_image_proc.keys():
					self.assertAlmostEqual(input_image_proc[key].sum()  ,					input_processor[key].sum()  ,					delta=1e-2)

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Optional[Any]					=					self.get_image_processor()
			UpperCamelCase__				:					Dict					=					self.get_tokenizer()

			UpperCamelCase__				:					List[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					'lower newer'

			UpperCamelCase__				:					Union[str, Any]					=					processor(text=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					tokenizer(UpperCAmelCase_)

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]  ,					encoded_processor[key])

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Optional[int]					=					self.get_image_processor()
			UpperCamelCase__				:					List[str]					=					self.get_tokenizer()

			UpperCamelCase__				:					List[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					'lower newer'
			UpperCamelCase__				:					List[Any]					=					self.prepare_image_inputs()

			UpperCamelCase__				:					str					=					processor(text=UpperCAmelCase_  ,					images=UpperCAmelCase_)

			self.assertListEqual(list(inputs.keys())  ,					['input_ids', 'attention_mask', 'pixel_values'])

			# test if it raises when no input is passed
			with pytest.raises(UpperCAmelCase_):
					processor()

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Any					=					self.get_image_processor()
			UpperCamelCase__				:					Dict					=					self.get_tokenizer()

			UpperCamelCase__				:					Optional[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			UpperCamelCase__				:					List[Any]					=					processor.batch_decode(UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					tokenizer.batch_decode(UpperCAmelCase_)

			self.assertListEqual(UpperCAmelCase_  ,					UpperCAmelCase_)





	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					Union[str, Any]					=					self.get_image_processor()
			UpperCamelCase__				:					List[str]					=					self.get_tokenizer()

			UpperCamelCase__				:					Optional[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					List[Any]					=					'lower newer'
			UpperCamelCase__				:					Optional[int]					=					self.prepare_image_inputs()

			UpperCamelCase__				:					List[str]					=					processor(text=UpperCAmelCase_  ,					images=UpperCAmelCase_)

			self.assertListEqual(list(inputs.keys())  ,					processor.model_input_names)




