import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCAmelCase			:							Any     =(
    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			:							Tuple     =(
    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			:							List[Any]     =[
    "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 						A__  (					__A   , __A	):







    '''simple docstring'''
    for tf_name, hf_name in patterns:
        _lowerCamelCase     :		List[Any] 						=						k.replace(__A   , __A	)
    return k







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		int 						=						BigBirdPegasusConfig(**__A	)
    _lowerCamelCase     :		str 						=						BigBirdPegasusForConditionalGeneration(__A	)
    _lowerCamelCase     :		Optional[Any] 						=						torch_model.state_dict()
    _lowerCamelCase     :		Any 						=						{}

    # separating decoder weights
    _lowerCamelCase     :		Union[str, Any] 						=						{k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""	)}
    _lowerCamelCase     :		int 						=						{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"""	):
        _lowerCamelCase     :		List[str] 						=						[k.endswith(__A	) for ending in KEYS_TO_IGNORE]
        if any(__A	):
            continue
        _lowerCamelCase     :		Any 						=						DECODER_PATTERNS
        _lowerCamelCase     :		str 						=						rename_state_dict_key(__A   , __A	)
        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"""]	):
            _lowerCamelCase     :		Optional[Any] 						=						v.T
        _lowerCamelCase     :		Optional[int] 						=						torch.from_numpy(__A	)
        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"""	):
        _lowerCamelCase     :		int 						=						[k.endswith(__A	) for ending in KEYS_TO_IGNORE]
        if any(__A	):
            continue
        _lowerCamelCase     :		int 						=						REMAINING_PATTERNS
        _lowerCamelCase     :		Dict 						=						rename_state_dict_key(__A   , __A	)
        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"""]	):
            _lowerCamelCase     :		str 						=						v.T
        _lowerCamelCase     :		int 						=						torch.from_numpy(__A	)
        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}"""

    _lowerCamelCase     :		List[str] 						=						mapping["""model.embed_positions.weight"""]
    _lowerCamelCase     :		int 						=						mapping.pop("""model.embed_positions.weight"""	)
    _lowerCamelCase						,							_lowerCamelCase     :		Optional[Any] 						=						torch_model.load_state_dict(__A   , strict=__A	)
    _lowerCamelCase     :		Any 						=						[
        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 						A__  (					__A	):







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







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		str 						=						get_tf_weights_as_numpy(__A	)
    _lowerCamelCase     :		int 						=						convert_bigbird_pegasus(__A   , __A	)
    torch_model.save_pretrained(__A	)


if __name__ == "__main__":
    lowerCAmelCase			:							Any     =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			:							int     =parser.parse_args()
    lowerCAmelCase			:							List[str]     ={}
    convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)





import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)




class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






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


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						["""a""", """b""", """c"""]

          # Defaults to last layer if both are None
          _lowerCamelCase						,							_lowerCamelCase     :		int 						=						get_aligned_output_features_output_indices(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""c"""])
          self.assertEqual(_UpperCamelCase    ,  [2])

          # Out indices set to match out features
          _lowerCamelCase						,							_lowerCamelCase     :		int 						=						get_aligned_output_features_output_indices(["""a""", """c"""]    ,  _UpperCamelCase    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""a""", """c"""])
          self.assertEqual(_UpperCamelCase    ,  [0, 2])

          # Out features set to match out indices
          _lowerCamelCase						,							_lowerCamelCase     :		Tuple 						=						get_aligned_output_features_output_indices(_UpperCamelCase    ,  [0, 2]    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""a""", """c"""])
          self.assertEqual(_UpperCamelCase    ,  [0, 2])

          # Out features selected from negative indices
          _lowerCamelCase						,							_lowerCamelCase     :		str 						=						get_aligned_output_features_output_indices(_UpperCamelCase    ,  [-3, -1]    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""a""", """c"""])
          self.assertEqual(_UpperCamelCase    ,  [-3, -1])



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->int:


          """simple docstring"""


          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0, 1)    ,  _UpperCamelCase)

          # Out features must be a list
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(("""a""", """b""")    ,  (0, 1)    ,  ["""a""", """b"""])

          # Out features must be a subset of stage names
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0, 1)    ,  ["""a"""])

          # Out indices must be a list or tuple
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(_UpperCamelCase    ,  0    ,  ["""a""", """b"""])

          # Out indices must be a subset of stage names
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(_UpperCamelCase    ,  (0, 1)    ,  ["""a"""])

          # Out features and out indices must be the same length
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0,)    ,  ["""a""", """b""", """c"""])

          # Out features should match out indices
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0, 2)    ,  ["""a""", """b""", """c"""])

          # Out features and out indices should be in order
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""b""", """a"""]    ,  (0, 1)    ,  ["""a""", """b"""])

          # Check passes with valid inputs
          verify_out_features_out_indices(["""a""", """b""", """d"""]    ,  (0, 1, -1)    ,  ["""a""", """b""", """c""", """d"""])




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


          """simple docstring"""


          _lowerCamelCase     :		int 						=						BackboneMixin()

          _lowerCamelCase     :		Union[str, Any] 						=						["""a""", """b""", """c"""]
          _lowerCamelCase     :		Tuple 						=						["""a""", """c"""]
          _lowerCamelCase     :		List[Any] 						=						[0, 2]

          # Check that the output features and indices are set correctly
          self.assertEqual(backbone.out_features    ,  ["""a""", """c"""])
          self.assertEqual(backbone.out_indices    ,  [0, 2])

          # Check out features and indices are updated correctly
          _lowerCamelCase     :		str 						=						["""a""", """b"""]
          self.assertEqual(backbone.out_features    ,  ["""a""", """b"""])
          self.assertEqual(backbone.out_indices    ,  [0, 1])

          _lowerCamelCase     :		Optional[int] 						=						[-3, -1]
          self.assertEqual(backbone.out_features    ,  ["""a""", """c"""])
          self.assertEqual(backbone.out_indices    ,  [-3, -1])




