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


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

       # separating decoder weights
       snake_case			:  List[Any]     							= {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"       )}
       snake_case			:  Union[str, Any]     							= {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			:  List[str]     							= [k.endswith(__lowerCamelCase       ) for ending in KEYS_TO_IGNORE]
              if any(__lowerCamelCase       ):
                     continue
              snake_case			:  List[str]     							= DECODER_PATTERNS
              snake_case			:  int     							= 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[int]     							= v.T
              snake_case			:  Tuple     							= 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			:  str     							= [k.endswith(__lowerCamelCase       ) for ending in KEYS_TO_IGNORE]
              if any(__lowerCamelCase       ):
                     continue
              snake_case			:  Union[str, Any]     							= REMAINING_PATTERNS
              snake_case			:  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			:  List[Any]     							= v.T
              snake_case			:  Dict     							= 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			:  Tuple     							= mapping["model.embed_positions.weight"]
       snake_case			:  str     							= mapping.pop("model.embed_positions.weight"       )
       snake_case							,   snake_case			:  int     							= torch_model.load_state_dict(__lowerCamelCase							,   strict=__lowerCamelCase       )
       snake_case			:  Union[str, 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      UpperCamelCase   (       __lowerCamelCase	:					Tuple       ):
       snake_case			:  Dict     							= tf.train.list_variables(__lowerCamelCase       )
       snake_case			:  Union[str, Any]     							= {}
       snake_case			:  Optional[int]     							= ["global_step"]
       for name, shape in tqdm(__lowerCamelCase							,   desc="converting tf checkpoint to dict"       ):
              snake_case			:  List[str]     							= any(pat in name for pat in ignore_name       )
              if skip_key:
                     continue
              snake_case			:  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			:  Dict     							= get_tf_weights_as_numpy(__lowerCamelCase       )
       snake_case			:  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)







from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_pt_objects import *  # noqa F403
else:
   from .scheduling_consistency_models import CMStochasticIterativeScheduler
   from .scheduling_ddim import DDIMScheduler
   from .scheduling_ddim_inverse import DDIMInverseScheduler
   from .scheduling_ddim_parallel import DDIMParallelScheduler
   from .scheduling_ddpm import DDPMScheduler
   from .scheduling_ddpm_parallel import DDPMParallelScheduler
   from .scheduling_deis_multistep import DEISMultistepScheduler
   from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
   from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
   from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
   from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
   from .scheduling_euler_discrete import EulerDiscreteScheduler
   from .scheduling_heun_discrete import HeunDiscreteScheduler
   from .scheduling_ipndm import IPNDMScheduler
   from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
   from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
   from .scheduling_karras_ve import KarrasVeScheduler
   from .scheduling_pndm import PNDMScheduler
   from .scheduling_repaint import RePaintScheduler
   from .scheduling_sde_ve import ScoreSdeVeScheduler
   from .scheduling_sde_vp import ScoreSdeVpScheduler
   from .scheduling_unclip import UnCLIPScheduler
   from .scheduling_unipc_multistep import UniPCMultistepScheduler
   from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
   from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_objects import *  # noqa F403
else:
   from .scheduling_ddim_flax import FlaxDDIMScheduler
   from .scheduling_ddpm_flax import FlaxDDPMScheduler
   from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
   from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
   from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
   from .scheduling_pndm_flax import FlaxPNDMScheduler
   from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
   from .scheduling_utils_flax import (
       FlaxKarrasDiffusionSchedulers,
       FlaxSchedulerMixin,
       FlaxSchedulerOutput,
       broadcast_to_shape_from_left,
   )


try:
   if not (is_torch_available() and is_scipy_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
   from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
   if not (is_torch_available() and is_torchsde_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
   from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler





