
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


__lowerCamelCase							=       [
    # tf -> hf
    ("""/""", """."""),
    ("""layer_""", """layers."""),
    ("""kernel""", """weight"""),
    ("""beta""", """bias"""),
    ("""gamma""", """weight"""),
    ("""pegasus""", """model"""),
]
__lowerCamelCase							=       [
    (""".output.dense""", """.fc2"""),
    ("""intermediate.LayerNorm""", """final_layer_norm"""),
    ("""intermediate.dense""", """fc1"""),
]

__lowerCamelCase							=       (
    INIT_COMMON
    + [
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.out_proj"""),
        ("""attention.self""", """self_attn"""),
        ("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
        ("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
        ("""attention.encdec""", """encoder_attn"""),
        ("""key""", """k_proj"""),
        ("""value""", """v_proj"""),
        ("""query""", """q_proj"""),
        ("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

__lowerCamelCase							=       (
    INIT_COMMON
    + [
        ("""embeddings.word_embeddings""", """shared.weight"""),
        ("""embeddings.position_embeddings""", """embed_positions.weight"""),
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.output"""),
        ("""attention.self""", """self_attn.self"""),
        ("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

__lowerCamelCase							=       [
    """encdec/key/bias""",
    """encdec/query/bias""",
    """encdec/value/bias""",
    """self/key/bias""",
    """self/query/bias""",
    """self/value/bias""",
    """encdec_output/dense/bias""",
    """attention/output/dense/bias""",
]


def 			UpperCamelCase					(    __lowerCamelCase     : Any				,					__lowerCamelCase     : Tuple    ):
       for tf_name, hf_name in patterns:
              snake_case       :   Union[str, Any]							=      k.replace(__lowerCamelCase				,					__lowerCamelCase    )
       return k


def 			UpperCamelCase					(    __lowerCamelCase     : dict				,					__lowerCamelCase     : dict    ):
       snake_case       :   Dict							=      BigBirdPegasusConfig(**__lowerCamelCase    )
       snake_case       :   List[Any]							=      BigBirdPegasusForConditionalGeneration(__lowerCamelCase    )
       snake_case       :   List[Any]							=      torch_model.state_dict()
       snake_case       :   Any							=      {}

       # separating decoder weights
       snake_case       :   Tuple							=      {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"    )}
       snake_case       :   str							=      {k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder"    )}

       for k, v in tqdm(decoder_weights.items()				,					"tf -> hf conversion"    ):
              snake_case       :   Any							=      [k.endswith(__lowerCamelCase    ) for ending in KEYS_TO_IGNORE]
              if any(__lowerCamelCase    ):
                     continue
              snake_case       :   Union[str, Any]							=      DECODER_PATTERNS
              snake_case       :   str							=      rename_state_dict_key(__lowerCamelCase				,					__lowerCamelCase    )
              if new_k not in state_dict:
                     raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""    )
              if any(True if i in k else False for i in ["dense", "query", "key", "value"]    ):
                     snake_case       :   Optional[Any]							=      v.T
              snake_case       :   Optional[Any]							=      torch.from_numpy(__lowerCamelCase    )
              assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

       for k, v in tqdm(remaining_weights.items()				,					"tf -> hf conversion"    ):
              snake_case       :   Tuple							=      [k.endswith(__lowerCamelCase    ) for ending in KEYS_TO_IGNORE]
              if any(__lowerCamelCase    ):
                     continue
              snake_case       :   Dict							=      REMAINING_PATTERNS
              snake_case       :   List[str]							=      rename_state_dict_key(__lowerCamelCase				,					__lowerCamelCase    )
              if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
                     raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""    )
              if any(True if i in k else False for i in ["dense", "query", "key", "value"]    ):
                     snake_case       :   str							=      v.T
              snake_case       :   int							=      torch.from_numpy(__lowerCamelCase    )
              if k != "pegasus/embeddings/position_embeddings":
                     assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

       snake_case       :   Optional[Any]							=      mapping["model.embed_positions.weight"]
       snake_case       :   List[str]							=      mapping.pop("model.embed_positions.weight"    )
       snake_case				,					snake_case       :   List[Any]							=      torch_model.load_state_dict(__lowerCamelCase				,					strict=__lowerCamelCase    )
       snake_case       :   Optional[int]							=      [
           k
           for k in missing
           if k
           not in [
               "final_logits_bias",
               "model.encoder.embed_tokens.weight",
               "model.decoder.embed_tokens.weight",
               "lm_head.weight",
           ]
       ]
       assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
       assert extra == [], f"""no matches found for the following tf keys {extra}"""
       return torch_model


def 			UpperCamelCase					(    __lowerCamelCase     : str    ):
       snake_case       :   Optional[Any]							=      tf.train.list_variables(__lowerCamelCase    )
       snake_case       :   List[str]							=      {}
       snake_case       :   List[str]							=      ["global_step"]
       for name, shape in tqdm(__lowerCamelCase				,					desc="converting tf checkpoint to dict"    ):
              snake_case       :   str							=      any(pat in name for pat in ignore_name    )
              if skip_key:
                     continue
              snake_case       :   Union[str, Any]							=      tf.train.load_variable(__lowerCamelCase				,					__lowerCamelCase    )
              snake_case       :   Union[str, Any]							=      array
       return tf_weights


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : dict    ):
       snake_case       :   str							=      get_tf_weights_as_numpy(__lowerCamelCase    )
       snake_case       :   Optional[Any]							=      convert_bigbird_pegasus(__lowerCamelCase				,					__lowerCamelCase    )
       torch_model.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
   __lowerCamelCase							=       argparse.ArgumentParser()
   parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
   parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
   __lowerCamelCase							=       parser.parse_args()
   __lowerCamelCase							=       {}
   convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor






@require_tokenizers
@require_vision
class   UpperCAmelCase		(						unittest.TestCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	List[str]:


          '''simple docstring'''


          snake_case       :   int							=      tempfile.mkdtemp()

          # fmt: off
          snake_case       :   Optional[int]							=      ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
          # fmt: on
          snake_case       :   List[str]							=      os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["vocab_file"] )
          with open(self.vocab_file    ,						"w"    ,						encoding="utf-8" ) as vocab_writer:
                 vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

          snake_case       :   int							=      {
              "do_resize": True,
              "size": {"height": 18, "width": 18},
              "do_normalize": True,
              "image_mean": [0.5, 0.5, 0.5],
              "image_std": [0.5, 0.5, 0.5],
          }
          snake_case       :   Optional[Any]							=      os.path.join(self.tmpdirname    ,						snake_case__ )
          with open(self.image_processor_file    ,						"w"    ,						encoding="utf-8" ) as fp:
                 json.dump(snake_case__    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						**snake_case__		:					str )		->	Optional[int]:


          '''simple docstring'''


          return BertTokenizer.from_pretrained(self.tmpdirname    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						**snake_case__		:					List[str] )		->	int:


          '''simple docstring'''


          return ViTImageProcessor.from_pretrained(self.tmpdirname    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Dict:


          '''simple docstring'''


          shutil.rmtree(self.tmpdirname )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	str:


          '''simple docstring'''


          snake_case       :   List[Any]							=      [np.random.randint(2_55    ,						size=(3, 30, 4_00)    ,						dtype=np.uinta )]

          snake_case       :   Optional[int]							=      [Image.fromarray(np.moveaxis(snake_case__    ,						0    ,						-1 ) ) for x in image_inputs]

          return image_inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.get_tokenizer()
          snake_case       :   Optional[Any]							=      self.get_image_processor()

          snake_case       :   Optional[Any]							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          processor.save_pretrained(self.tmpdirname )
          snake_case       :   Any							=      VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

          self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer.get_vocab() )
          self.assertIsInstance(processor.tokenizer    ,						(BertTokenizer, BertTokenizerFast) )

          self.assertEqual(processor.image_processor.to_json_string()    ,						image_processor.to_json_string() )
          self.assertIsInstance(processor.image_processor    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   str							=      VisionTextDualEncoderProcessor(
              tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor() )
          processor.save_pretrained(self.tmpdirname )

          snake_case       :   Optional[int]							=      self.get_tokenizer(bos_token="(BOS)"    ,						eos_token="(EOS)" )
          snake_case       :   Tuple							=      self.get_image_processor(do_normalize=snake_case__    ,						padding_value=1.0 )

          snake_case       :   List[str]							=      VisionTextDualEncoderProcessor.from_pretrained(
              self.tmpdirname    ,						bos_token="(BOS)"    ,						eos_token="(EOS)"    ,						do_normalize=snake_case__    ,						padding_value=1.0 )

          self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer_add_kwargs.get_vocab() )
          self.assertIsInstance(processor.tokenizer    ,						(BertTokenizer, BertTokenizerFast) )

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	int:


          '''simple docstring'''


          snake_case       :   str							=      self.get_image_processor()
          snake_case       :   Optional[int]							=      self.get_tokenizer()

          snake_case       :   List[Any]							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   Optional[Any]							=      self.prepare_image_inputs()

          snake_case       :   str							=      image_processor(snake_case__    ,						return_tensors="np" )
          snake_case       :   Any							=      processor(images=snake_case__    ,						return_tensors="np" )

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.get_image_processor()
          snake_case       :   int							=      self.get_tokenizer()

          snake_case       :   Any							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   Tuple							=      "lower newer"

          snake_case       :   Tuple							=      processor(text=snake_case__ )

          snake_case       :   Union[str, Any]							=      tokenizer(snake_case__ )

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.get_image_processor()
          snake_case       :   Dict							=      self.get_tokenizer()

          snake_case       :   Dict							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   int							=      "lower newer"
          snake_case       :   Dict							=      self.prepare_image_inputs()

          snake_case       :   Union[str, Any]							=      processor(text=snake_case__    ,						images=snake_case__ )

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

          # test if it raises when no input is passed
          with self.assertRaises(snake_case__ ):
                 processor()



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Tuple:


          '''simple docstring'''


          snake_case       :   Tuple							=      self.get_image_processor()
          snake_case       :   Optional[Any]							=      self.get_tokenizer()

          snake_case       :   Tuple							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   List[str]							=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          snake_case       :   List[Any]							=      processor.batch_decode(snake_case__ )
          snake_case       :   Union[str, Any]							=      tokenizer.batch_decode(snake_case__ )

          self.assertListEqual(snake_case__    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	List[str]:


          '''simple docstring'''


          snake_case       :   str							=      self.get_image_processor()
          snake_case       :   Union[str, Any]							=      self.get_tokenizer()

          snake_case       :   Any							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   Optional[Any]							=      "lower newer"
          snake_case       :   List[Any]							=      self.prepare_image_inputs()

          snake_case       :   Tuple							=      processor(text=snake_case__    ,						images=snake_case__ )

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





