
"""simple docstring"""





import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class A_	(lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		LEDTokenizer
  SCREAMING_SNAKE_CASE__			:						Dict       =		LEDTokenizerFast
  SCREAMING_SNAKE_CASE__			:						List[str]       =		True







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     super().setUp()
     UpperCAmelCase_					:					List[Any]			     =    [
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "\u0120",
         "\u0120l",
         "\u0120n",
         "\u0120lo",
         "\u0120low",
         "er",
         "\u0120lowest",
         "\u0120newer",
         "\u0120wider",
         "<unk>",
     ]
     UpperCAmelCase_					:					Union[str, Any]			     =    dict(zip(lowercase_				,   range(len(lowercase_  )  )  )  )
     UpperCAmelCase_					:					Optional[Any]			     =    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
     UpperCAmelCase_					:					Union[str, Any]			     =    {"unk_token": "<unk>"}

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







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     kwargs.update(self.special_tokens_map  )
     return self.tokenizer_class.from_pretrained(self.tmpdirname				,   **lowercase_  )







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     kwargs.update(self.special_tokens_map  )
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,   **lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     return "lower newer", "lower newer"







  @cached_property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return LEDTokenizer.from_pretrained("allenai/led-base-16384"  )







  @cached_property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"  )







  @require_torch
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
     UpperCAmelCase_					:					Dict			     =    [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_					:					int			     =    tokenizer(lowercase_				,   max_length=len(lowercase_  )				,   padding=lowercase_				,   return_tensors="pt"  )
        self.assertIsInstance(lowercase_				,   lowercase_  )

        self.assertEqual((2, 9)				,   batch.input_ids.shape  )
        self.assertEqual((2, 9)				,   batch.attention_mask.shape  )
        UpperCAmelCase_					:					str			     =    batch.input_ids.tolist()[0]
        self.assertListEqual(lowercase_				,   lowercase_  )







  @require_torch
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_					:					str			     =    tokenizer(lowercase_				,   padding=lowercase_				,   return_tensors="pt"  )
        self.assertIn("input_ids"				,   lowercase_  )
        self.assertIn("attention_mask"				,   lowercase_  )
        self.assertNotIn("labels"				,   lowercase_  )
        self.assertNotIn("decoder_attention_mask"				,   lowercase_  )







  @require_torch
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    [
         "Summary of the text.",
         "Another summary.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_					:					List[Any]			     =    tokenizer(text_target=lowercase_				,   max_length=32				,   padding="max_length"				,   return_tensors="pt"  )
        self.assertEqual(32				,   targets["input_ids"].shape[1]  )







  @require_torch
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_					:					Tuple			     =    tokenizer(
            ["I am a small frog" * 1024, "I am a small frog"]				,   padding=lowercase_				,   truncation=lowercase_				,   return_tensors="pt"  )
        self.assertIsInstance(lowercase_				,   lowercase_  )
        self.assertEqual(batch.input_ids.shape				,   (2, 5122)  )







  @require_torch
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    ["A long paragraph for summarization."]
     UpperCAmelCase_					:					Optional[int]			     =    [
         "Summary of the text.",
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_					:					Optional[Any]			     =    tokenizer(lowercase_				,   return_tensors="pt"  )
        UpperCAmelCase_					:					List[str]			     =    tokenizer(text_target=lowercase_				,   return_tensors="pt"  )
        UpperCAmelCase_					:					Optional[Any]			     =    inputs["input_ids"]
        UpperCAmelCase_					:					int			     =    targets["input_ids"]
        self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
        self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
        self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
        self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )







  @require_torch
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCAmelCase_					:					Dict			     =    ["Summary of the text.", "Another summary."]
        UpperCAmelCase_					:					Optional[Any]			     =    [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

        UpperCAmelCase_					:					Optional[Any]			     =    tokenizer(lowercase_				,   padding=lowercase_  )
        UpperCAmelCase_					:					Tuple			     =    [[0] * len(lowercase_  ) for x in encoded_output["input_ids"]]
        UpperCAmelCase_					:					Optional[Any]			     =    tokenizer.pad(lowercase_  )
        self.assertSequenceEqual(outputs["global_attention_mask"]				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     pass







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
           UpperCAmelCase_					:					Any			     =    self.rust_tokenizer_class.from_pretrained(lowercase_				,   **lowercase_  )
           UpperCAmelCase_					:					str			     =    self.tokenizer_class.from_pretrained(lowercase_				,   **lowercase_  )
           UpperCAmelCase_					:					Union[str, Any]			     =    "A, <mask> AllenNLP sentence."
           UpperCAmelCase_					:					List[Any]			     =    tokenizer_r.encode_plus(lowercase_				,   add_special_tokens=lowercase_				,   return_token_type_ids=lowercase_  )
           UpperCAmelCase_					:					Dict			     =    tokenizer_p.encode_plus(lowercase_				,   add_special_tokens=lowercase_				,   return_token_type_ids=lowercase_  )
           self.assertEqual(sum(tokens_r["token_type_ids"]  )				,   sum(tokens_p["token_type_ids"]  )  )
           self.assertEqual(
               sum(tokens_r["attention_mask"]  ) / len(tokens_r["attention_mask"]  )				,   sum(tokens_p["attention_mask"]  ) / len(tokens_p["attention_mask"]  )				,   )

           UpperCAmelCase_					:					Optional[Any]			     =    tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
           UpperCAmelCase_					:					Optional[Any]			     =    tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]  )
           self.assertSequenceEqual(tokens_p["input_ids"]				,   [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
           self.assertSequenceEqual(tokens_r["input_ids"]				,   [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

           self.assertSequenceEqual(
               lowercase_				,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )
           self.assertSequenceEqual(
               lowercase_				,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )








import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(_A ,  _A  )





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_			,					lowerCAmelCase_      			=  emb.weight.shape
						lowerCAmelCase_      			=  nn.Linear(_A ,  _A ,  bias=_A  )
						lowerCAmelCase_      			=  emb.weight.data
						return lin_layer





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  torch.load(_A ,  map_location='''cpu'''  )
						lowerCAmelCase_      			=  Namespace(**checkpoint['''cfg''']['''model''']  )
						lowerCAmelCase_      			=  checkpoint['''model''']
						remove_ignore_keys_(_A  )
						lowerCAmelCase_      			=  state_dict['''decoder.embed_tokens.weight'''].shape[0]

						lowerCAmelCase_      			=  {key.replace('''decoder''' ,  '''model'''  ): val for key, val in state_dict.items()}

						lowerCAmelCase_      			=  XGLMConfig(
						    vocab_size=_A ,  max_position_embeddings=args.max_target_positions ,  num_layers=args.decoder_layers ,  attention_heads=args.decoder_attention_heads ,  ffn_dim=args.decoder_ffn_embed_dim ,  d_model=args.decoder_embed_dim ,  layerdrop=args.decoder_layerdrop ,  dropout=args.dropout ,  attention_dropout=args.attention_dropout ,  activation_dropout=args.activation_dropout ,  activation_function='''gelu''' ,  scale_embedding=not args.no_scale_embedding ,  tie_word_embeddings=args.share_decoder_input_output_embed ,  )

						lowerCAmelCase_      			=  XGLMForCausalLM(_A  )
						lowerCAmelCase_      			=  model.load_state_dict(_A ,  strict=_A  )
						print(_A  )
						lowerCAmelCase_      			=  make_linear_from_emb(model.model.embed_tokens  )

						return model


if __name__ == "__main__":
						_A     =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						_A     =			parser.parse_args()
						_A     =			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)




