






'''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()
__magic_name__		:     Optional[int]					=    logging.get_logger(__name__)

__magic_name__		:     str					=    """Hello, World!"""
__magic_name__		:     Optional[int]					=    """en_XX"""



def     A__						(	A_    ,      A_    ,      A_		)     ->				int:
					_lowercase			= Path("data_bin"		)
					_lowercase			= FairseqXmodModel.from_pretrained(
					    model_name_or_path=str(Path(__snake_case		).parent		)    ,      checkpoint_file=Path(__snake_case		).name    ,      _name="xmod_base"    ,      arch="xmod_base"    ,      task="multilingual_masked_lm"    ,      data_name_or_path=str(__snake_case		)    ,      bpe="sentencepiece"    ,      sentencepiece_model=str(Path(__snake_case		).parent / "sentencepiece.bpe.model"		)    ,      src_dict=str(data_dir / "dict.txt"		)    ,      )
					xmod.eval()  # disable dropout
					print(__snake_case		)

					_lowercase			= xmod.model.encoder.sentence_encoder
					_lowercase			= 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:
										_lowercase			= xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

					print("Our X-MOD config:"    ,      __snake_case		)

					_lowercase			= XmodForSequenceClassification(__snake_case		) if classification_head else XmodForMaskedLM(__snake_case		)
					model.eval()

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

					_lowercase			= xmod_sent_encoder.layernorm_embedding.weight
					_lowercase			= xmod_sent_encoder.layernorm_embedding.bias

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

										# self attention
										_lowercase			= 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."		)

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

										# self-attention output
										_lowercase			= 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."		)
										_lowercase			= xmod_layer.self_attn.out_proj.weight
										_lowercase			= xmod_layer.self_attn.out_proj.bias
										_lowercase			= xmod_layer.self_attn_layer_norm.weight
										_lowercase			= xmod_layer.self_attn_layer_norm.bias

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

										# output
										_lowercase			= layer.output
										if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
															raise AssertionError("Dimensions of feed-forward weights do not match."		)
										_lowercase			= xmod_layer.fca.weight
										_lowercase			= xmod_layer.fca.bias
										_lowercase			= xmod_layer.final_layer_norm.weight
										_lowercase			= xmod_layer.final_layer_norm.bias
										if bert_output.adapter_layer_norm is not None:
															_lowercase			= xmod_layer.adapter_layer_norm.weight
															_lowercase			= 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():
															_lowercase			= bert_output.adapter_modules[lang_code]
															_lowercase			= xmod_layer.adapter_modules[lang_code]
															_lowercase			= from_adapter.fca.weight
															_lowercase			= from_adapter.fca.bias
															_lowercase			= from_adapter.fca.weight
															_lowercase			= from_adapter.fca.bias

										# end of layer

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

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

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

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

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


if __name__ == "__main__":
	__magic_name__		:     Dict					=    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.'''
	)
	__magic_name__		:     Optional[Any]					=    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 unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						a__		( a_,     unittest.TestCase		):
   __lowerCAmelCase				=							GPTaTokenizer
   __lowerCAmelCase				=							GPTaTokenizerFast
   __lowerCAmelCase				=							True
   __lowerCAmelCase				=							{"""add_prefix_space""": True}
   __lowerCAmelCase				=							False
   def      __magic_name__     (     self							):
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          lowercase       :							Any									=				[
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "\u0120",
              "\u0120l",
              "\u0120n",
              "\u0120lo",
              "\u0120low",
              "er",
              "\u0120lowest",
              "\u0120newer",
              "\u0120wider",
              "<unk>",
              "<|endoftext|>",
          ]
          lowercase       :							Any									=				dict(zip(_a      ,       range(len(_a							)							)							)							)
          lowercase       :							Dict									=				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
          lowercase       :							int									=				{"unk_token": "<unk>"}

          lowercase       :							Tuple									=				os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES["vocab_file"]							)
          lowercase       :							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(_a							) + "\n"							)
          with open(self.merges_file      ,       "w"      ,       encoding="utf-8"							) as fp:
                 fp.write("\n".join(_a							)							)
   def      __magic_name__     (     self      ,       **_a							):
          kwargs.update(self.special_tokens_map							)
          return GPTaTokenizer.from_pretrained(self.tmpdirname      ,       **_a							)
   def      __magic_name__     (     self      ,       **_a							):
          kwargs.update(self.special_tokens_map							)
          return GPTaTokenizerFast.from_pretrained(self.tmpdirname      ,       **_a							)
   def      __magic_name__     (     self      ,       _a							):
          lowercase       :							Tuple									=				"lower newer"
          lowercase       :							Dict									=				"lower newer"
          return input_text, output_text
   def      __magic_name__     (     self							):
          lowercase       :							str									=				GPTaTokenizer(self.vocab_file      ,       self.merges_file      ,       **self.special_tokens_map							)
          lowercase       :							Union[str, Any]									=				"lower newer"
          lowercase       :							Any									=				["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
          lowercase       :							int									=				tokenizer.tokenize(_a      ,       add_prefix_space=_a							)
          self.assertListEqual(_a      ,       _a							)

          lowercase       :							int									=				tokens + [tokenizer.unk_token]
          lowercase       :							List[Any]									=				[14, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_a							)      ,       _a							)
   def      __magic_name__     (     self							):
          if not self.test_rust_tokenizer:
                 return

          lowercase       :							Union[str, Any]									=				self.get_tokenizer()
          lowercase       :							int									=				self.get_rust_tokenizer(add_prefix_space=_a							)

          lowercase       :							Union[str, Any]									=				"lower newer"

          # Testing tokenization
          lowercase       :							Union[str, Any]									=				tokenizer.tokenize(_a      ,       add_prefix_space=_a							)
          lowercase       :							List[Any]									=				rust_tokenizer.tokenize(_a							)
          self.assertListEqual(_a      ,       _a							)

          # Testing conversion to ids without special tokens
          lowercase       :							Dict									=				tokenizer.encode(_a      ,       add_special_tokens=_a      ,       add_prefix_space=_a							)
          lowercase       :							Dict									=				rust_tokenizer.encode(_a      ,       add_special_tokens=_a							)
          self.assertListEqual(_a      ,       _a							)

          # Testing conversion to ids with special tokens
          lowercase       :							Any									=				self.get_rust_tokenizer(add_prefix_space=_a							)
          lowercase       :							Any									=				tokenizer.encode(_a      ,       add_prefix_space=_a							)
          lowercase       :							Any									=				rust_tokenizer.encode(_a							)
          self.assertListEqual(_a      ,       _a							)

          # Testing the unknown token
          lowercase       :							Union[str, Any]									=				tokens + [rust_tokenizer.unk_token]
          lowercase       :							str									=				[14, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_a							)      ,       _a							)
   def      __magic_name__     (     self      ,       *_a      ,       **_a							):
          # It's very difficult to mix/test pretokenization with byte-level
          # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
          pass
   def      __magic_name__     (     self      ,       _a=15							):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
                        lowercase       :							int									=				self.rust_tokenizer_class.from_pretrained(_a      ,       **_a							)

                        # Simple input
                        lowercase       :							int									=				"This is a simple input"
                        lowercase       :							Optional[int]									=				["This is a simple input 1", "This is a simple input 2"]
                        lowercase       :							Union[str, Any]									=				("This is a simple input", "This is a pair")
                        lowercase       :							Any									=				[
                            ("This is a simple input 1", "This is a simple input 2"),
                            ("This is a simple pair 1", "This is a simple pair 2"),
                        ]

                        # Simple input tests
                        self.assertRaises(_a      ,       tokenizer_r.encode      ,       _a      ,       max_length=_a      ,       padding="max_length"							)

                        # Simple input
                        self.assertRaises(_a      ,       tokenizer_r.encode_plus      ,       _a      ,       max_length=_a      ,       padding="max_length"							)

                        # Simple input
                        self.assertRaises(
                            _a      ,       tokenizer_r.batch_encode_plus      ,       _a      ,       max_length=_a      ,       padding="max_length"      ,       )

                        # Pair input
                        self.assertRaises(_a      ,       tokenizer_r.encode      ,       _a      ,       max_length=_a      ,       padding="max_length"							)

                        # Pair input
                        self.assertRaises(_a      ,       tokenizer_r.encode_plus      ,       _a      ,       max_length=_a      ,       padding="max_length"							)

                        # Pair input
                        self.assertRaises(
                            _a      ,       tokenizer_r.batch_encode_plus      ,       _a      ,       max_length=_a      ,       padding="max_length"      ,       )
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				GPTaTokenizer.from_pretrained(self.tmpdirname      ,       pad_token="<pad>"							)

          # Simple input
          lowercase       :							int									=				"This is a simple input"
          lowercase       :							Tuple									=				["This is a simple input looooooooong", "This is a simple input"]
          lowercase       :							Any									=				("This is a simple input", "This is a pair")
          lowercase       :							str									=				[
              ("This is a simple input loooooong", "This is a simple input"),
              ("This is a simple pair loooooong", "This is a simple pair"),
          ]

          lowercase       :							List[Any]									=				tokenizer.pad_token_id

          lowercase       :							List[Any]									=				tokenizer(_a      ,       padding="max_length"      ,       max_length=30      ,       return_tensors="np"							)
          lowercase       :							List[str]									=				tokenizer(_a      ,       padding=_a      ,       truncate=_a      ,       return_tensors="np"							)
          lowercase       :							List[Any]									=				tokenizer(*_a      ,       padding="max_length"      ,       max_length=60      ,       return_tensors="np"							)
          lowercase       :							Any									=				tokenizer(_a      ,       padding=_a      ,       truncate=_a      ,       return_tensors="np"							)

          # s
          # test single string max_length padding
          self.assertEqual(out_s["input_ids"].shape[-1]      ,       30							)
          self.assertTrue(pad_token_id in out_s["input_ids"]							)
          self.assertTrue(0 in out_s["attention_mask"]							)

          # s2
          # test automatic padding
          self.assertEqual(out_sa["input_ids"].shape[-1]      ,       33							)
          # long slice doesn't have padding
          self.assertFalse(pad_token_id in out_sa["input_ids"][0]							)
          self.assertFalse(0 in out_sa["attention_mask"][0]							)
          # short slice does have padding
          self.assertTrue(pad_token_id in out_sa["input_ids"][1]							)
          self.assertTrue(0 in out_sa["attention_mask"][1]							)

          # p
          # test single pair max_length padding
          self.assertEqual(out_p["input_ids"].shape[-1]      ,       60							)
          self.assertTrue(pad_token_id in out_p["input_ids"]							)
          self.assertTrue(0 in out_p["attention_mask"]							)

          # p2
          # test automatic padding pair
          self.assertEqual(out_pa["input_ids"].shape[-1]      ,       52							)
          # long slice pair doesn't have padding
          self.assertFalse(pad_token_id in out_pa["input_ids"][0]							)
          self.assertFalse(0 in out_pa["attention_mask"][0]							)
          # short slice pair does have padding
          self.assertTrue(pad_token_id in out_pa["input_ids"][1]							)
          self.assertTrue(0 in out_pa["attention_mask"][1]							)
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				"$$$"
          lowercase       :							List[str]									=				GPTaTokenizer.from_pretrained(self.tmpdirname      ,       bos_token=_a      ,       add_bos_token=_a							)

          lowercase       :							List[Any]									=				"This is a simple input"
          lowercase       :							str									=				["This is a simple input 1", "This is a simple input 2"]

          lowercase       :							Union[str, Any]									=				tokenizer.bos_token_id

          lowercase       :							List[str]									=				tokenizer(_a							)
          lowercase       :							Any									=				tokenizer(_a							)

          self.assertEqual(out_s.input_ids[0]      ,       _a							)
          self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids							)							)

          lowercase       :							List[Any]									=				tokenizer.decode(out_s.input_ids							)
          lowercase       :							Tuple									=				tokenizer.batch_decode(out_sa.input_ids							)

          self.assertEqual(decode_s.split()[0]      ,       _a							)
          self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa							)							)
   def      __magic_name__     (     self							):
          pass

   def      __magic_name__     (     self							):
          # TODO: change to self.get_tokenizers() when the fast version is implemented
          lowercase       :							Dict									=				[self.get_tokenizer(do_lower_case=_a      ,       add_bos_token=_a							)]
          for tokenizer in tokenizers:
                 with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
                        lowercase       :							Optional[Any]									=				"Encode this."
                        lowercase       :							Union[str, Any]									=				"This one too please."
                        lowercase       :							Dict									=				tokenizer.encode(_a      ,       add_special_tokens=_a							)
                        encoded_sequence += tokenizer.encode(_a      ,       add_special_tokens=_a							)
                        lowercase       :							Optional[Any]									=				tokenizer.encode_plus(
                            _a      ,       _a      ,       add_special_tokens=_a      ,       return_special_tokens_mask=_a      ,       )
                        lowercase       :							Tuple									=				encoded_sequence_dict["input_ids"]
                        lowercase       :							int									=				encoded_sequence_dict["special_tokens_mask"]
                        self.assertEqual(len(_a							)      ,       len(_a							)							)

                        lowercase       :							List[str]									=				[
                            (x if not special_tokens_mask[i] else None) for i, x in enumerate(_a							)
                        ]
                        lowercase       :							List[str]									=				[x for x in filtered_sequence if x is not None]
                        self.assertEqual(_a      ,       _a							)


@require_tokenizers
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          # More context:
          # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
          # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
          # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

          lowercase       :							Tuple									=				AutoTokenizer.from_pretrained("facebook/opt-350m"      ,       from_slow=_a							)
          lowercase       :							Any									=				"A photo of a cat"

          lowercase       :							List[str]									=				tokenizer.encode(
              _a      ,       )
          self.assertEqual(_a      ,       [2, 250, 1_345, 9, 10, 4_758]							)
          tokenizer.save_pretrained("test_opt"							)

          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("./test_opt"							)
          lowercase       :							Tuple									=				tokenizer.encode(
              _a      ,       )
          self.assertEqual(_a      ,       [2, 250, 1_345, 9, 10, 4_758]							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("facebook/opt-350m"      ,       use_slow=_a							)
          lowercase       :							Union[str, Any]									=				"A photo of a cat"

          lowercase       :							Union[str, Any]									=				tokenizer.encode(
              _a      ,       )
          # Same as above
          self.assertEqual(_a      ,       [2, 250, 1_345, 9, 10, 4_758]							)

   @unittest.skip("This test is failing because of a bug in the fast tokenizer"							)
   def      __magic_name__     (     self							):
          lowercase       :							int									=				AutoTokenizer.from_pretrained("facebook/opt-350m"      ,       from_slow=_a							)

          lowercase       :							List[Any]									=				"bos"
          lowercase       :							Optional[int]									=				tokenizer.get_vocab()["bos"]

          lowercase       :							List[str]									=				"A photo of a cat"
          lowercase       :							Dict									=				tokenizer.encode(
              _a      ,       )
          # We changed the bos token
          self.assertEqual(_a      ,       [31_957, 250, 1_345, 9, 10, 4_758]							)
          tokenizer.save_pretrained("./tok"							)
          lowercase       :							Tuple									=				AutoTokenizer.from_pretrained("./tok"							)
          self.assertTrue(tokenizer.is_fast							)
          lowercase       :							str									=				tokenizer.encode(
              _a      ,       )
          self.assertEqual(_a      ,       [31_957, 250, 1_345, 9, 10, 4_758]							)


