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 							lowercase						(						lowercase__ ,				unittest.TestCase ):
   lowerCamelCase_    					=LEDTokenizer
   lowerCamelCase_    					=LEDTokenizerFast
   lowerCamelCase_    					=True


   def __UpperCAmelCase       (   self				:	int)      ->					Optional[int]:
         super().setUp()
         lowercase_     =      [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''\u0120''',
             '''\u0120l''',
             '''\u0120n''',
             '''\u0120lo''',
             '''\u0120low''',
             '''er''',
             '''\u0120lowest''',
             '''\u0120newer''',
             '''\u0120wider''',
             '''<unk>''',
         ]
         lowercase_     =      dict(zip(UpperCAmelCase__   ,		range(len(UpperCAmelCase__))))
         lowercase_     =      ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
         lowercase_     =      {'''unk_token''': '''<unk>'''}

         lowercase_     =      os.path.join(self.tmpdirname   ,		VOCAB_FILES_NAMES["vocab_file"])
         lowercase_     =      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__))


   def __UpperCAmelCase       (   self				:	List[Any]   ,		**__lowerCAmelCase				:	Dict)      ->					str:
         kwargs.update(self.special_tokens_map)
         return self.tokenizer_class.from_pretrained(self.tmpdirname   ,		**UpperCAmelCase__)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		**__lowerCAmelCase				:	List[str])      ->					List[str]:
         kwargs.update(self.special_tokens_map)
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,		**UpperCAmelCase__)


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	int)      ->					str:
         return "lower newer", "lower newer"


   @cached_property
   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Optional[int]:
         return LEDTokenizer.from_pretrained("allenai/led-base-16384")


   @cached_property
   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Optional[int]:
         return LEDTokenizerFast.from_pretrained("allenai/led-base-16384")


   @require_torch
   def __UpperCAmelCase       (   self				:	Any)      ->					str:
         lowercase_     =      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         lowercase_     =      [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

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


   @require_torch
   def __UpperCAmelCase       (   self				:	List[Any])      ->					str:
         lowercase_     =      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     =      tokenizer(UpperCAmelCase__   ,		padding=UpperCAmelCase__   ,		return_tensors="pt")
               self.assertIn("input_ids"   ,		UpperCAmelCase__)
               self.assertIn("attention_mask"   ,		UpperCAmelCase__)
               self.assertNotIn("labels"   ,		UpperCAmelCase__)
               self.assertNotIn("decoder_attention_mask"   ,		UpperCAmelCase__)


   @require_torch
   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Tuple:
         lowercase_     =      [
             '''Summary of the text.''',
             '''Another summary.''',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     =      tokenizer(text_target=UpperCAmelCase__   ,		max_length=32   ,		padding="max_length"   ,		return_tensors="pt")
               self.assertEqual(32   ,		targets["input_ids"].shape[1])


   @require_torch
   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Dict:
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     =      tokenizer(
                   ["I am a small frog" * 1024, "I am a small frog"]   ,		padding=UpperCAmelCase__   ,		truncation=UpperCAmelCase__   ,		return_tensors="pt")
               self.assertIsInstance(UpperCAmelCase__   ,		UpperCAmelCase__)
               self.assertEqual(batch.input_ids.shape   ,		(2, 5122))


   @require_torch
   def __UpperCAmelCase       (   self				:	int)      ->					Optional[int]:
         lowercase_     =      ['''A long paragraph for summarization.''']
         lowercase_     =      [
             '''Summary of the text.''',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     =      tokenizer(UpperCAmelCase__   ,		return_tensors="pt")
               lowercase_     =      tokenizer(text_target=UpperCAmelCase__   ,		return_tensors="pt")
               lowercase_     =      inputs['''input_ids''']
               lowercase_     =      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				:	Union[str, Any])      ->					str:
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     =      ['''Summary of the text.''', '''Another summary.''']
               lowercase_     =      [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

               lowercase_     =      tokenizer(UpperCAmelCase__   ,		padding=UpperCAmelCase__)
               lowercase_     =      [[0] * len(UpperCAmelCase__) for x in encoded_output['''input_ids''']]
               lowercase_     =      tokenizer.pad(UpperCAmelCase__)
               self.assertSequenceEqual(outputs["global_attention_mask"]   ,		UpperCAmelCase__)


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Optional[int]:
         pass


   def __UpperCAmelCase       (   self				:	int)      ->					str:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                     lowercase_     =      self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__   ,		**UpperCAmelCase__)
                     lowercase_     =      self.tokenizer_class.from_pretrained(UpperCAmelCase__   ,		**UpperCAmelCase__)
                     lowercase_     =      '''A, <mask> AllenNLP sentence.'''
                     lowercase_     =      tokenizer_r.encode_plus(UpperCAmelCase__   ,		add_special_tokens=UpperCAmelCase__   ,		return_token_type_ids=UpperCAmelCase__)
                     lowercase_     =      tokenizer_p.encode_plus(UpperCAmelCase__   ,		add_special_tokens=UpperCAmelCase__   ,		return_token_type_ids=UpperCAmelCase__)
                     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"])   ,		)

                     lowercase_     =      tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
                     lowercase_     =      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(
                         UpperCAmelCase__   ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
                     self.assertSequenceEqual(
                         UpperCAmelCase__   ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])







'''simple docstring'''

import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def __a (	__lowerCamelCase			:						List[Any]				)	->							Tuple:
      '''simple docstring'''


      return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()				)


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						str				)	->							List[str]:
      '''simple docstring'''


      lowercase_     =      {}

      for key, value in state_dict.items():
            if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
                  continue

            lowercase_     =      key.replace("heads.cmd.mim_head.cls.predictions" ,		"mmm_image_head"				)
            lowercase_     =      key.replace("heads.cmd.mlm_head.cls.predictions" ,		"mmm_text_head"				)
            lowercase_     =      key.replace("heads.cmd.itm_head.cls" ,		"itm_head"				)
            lowercase_     =      key.replace("heads.cmd.itm_head.pooler" ,		"itm_head.pooler"				)
            lowercase_     =      key.replace("heads.cmd.clip_head.logit_scale" ,		"flava.logit_scale"				)
            lowercase_     =      key.replace("heads.fairseq_mlm.cls.predictions" ,		"mlm_head"				)
            lowercase_     =      key.replace("heads.imagenet.mim_head.cls.predictions" ,		"mim_head"				)
            lowercase_     =      key.replace("mm_text_projection" ,		"flava.text_to_mm_projection"				)
            lowercase_     =      key.replace("mm_image_projection" ,		"flava.image_to_mm_projection"				)
            lowercase_     =      key.replace("image_encoder.module" ,		"flava.image_model"				)
            lowercase_     =      key.replace("text_encoder.module" ,		"flava.text_model"				)
            lowercase_     =      key.replace("mm_encoder.module.encoder.cls_token" ,		"flava.multimodal_model.cls_token"				)
            lowercase_     =      key.replace("mm_encoder.module" ,		"flava.multimodal_model"				)
            lowercase_     =      key.replace("text_projection" ,		"flava.text_projection"				)
            lowercase_     =      key.replace("image_projection" ,		"flava.image_projection"				)

            lowercase_     =      value.float()

      for key, value in codebook_state_dict.items():
            lowercase_     =      value

      return upgrade


@torch.no_grad()
def __a (	__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Tuple=None				)	->							Optional[int]:
      '''simple docstring'''


      if config_path is not None:
            lowercase_     =      FlavaConfig.from_pretrained(__lowerCamelCase				)
      else:
            lowercase_     =      FlavaConfig()

      lowercase_     =      FlavaForPreTraining(__lowerCamelCase				).eval()

      lowercase_     =      convert_dalle_checkpoint(__lowerCamelCase ,		__lowerCamelCase ,		save_checkpoint=__lowerCamelCase				)

      if os.path.exists(__lowerCamelCase				):
            lowercase_     =      torch.load(__lowerCamelCase ,		map_location="cpu"				)
      else:
            lowercase_     =      torch.hub.load_state_dict_from_url(__lowerCamelCase ,		map_location="cpu"				)

      lowercase_     =      upgrade_state_dict(__lowerCamelCase ,		__lowerCamelCase				)
      hf_model.load_state_dict(__lowerCamelCase				)
      lowercase_     =      hf_model.state_dict()
      lowercase_     =      count_parameters(__lowerCamelCase				)
      lowercase_     =      count_parameters(__lowerCamelCase				) + count_parameters(__lowerCamelCase				)

      assert torch.allclose(__lowerCamelCase ,		__lowerCamelCase ,		atol=1e-3				)

      hf_model.save_pretrained(__lowerCamelCase				)


if __name__ == "__main__":
       lowerCAmelCase_ : List[str]						  =      argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
       parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       lowerCAmelCase_ : List[Any]						  =      parser.parse_args()

       convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






