
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


lowercase_       :						Any     					=	[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]





def    _lowerCAmelCase    (     lowerCamelCase__							:							int				)  ->    int:
  for pegasus_name, hf_name in PATTERNS:
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = k.replace(lowerCamelCase__,						lowerCamelCase__				)
  return k





def    _lowerCAmelCase    (     lowerCamelCase__							:							dict,						lowerCamelCase__							:							dict				)  ->    PegasusForConditionalGeneration:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = DEFAULTS.copy()
  cfg_kwargs.update(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				int							    = PegasusConfig(**lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = PegasusForConditionalGeneration(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = torch_model.model.state_dict()
  _SCREAMING_SNAKE_CASE   :				str							    = {}
  for k, v in tf_weights.items():
    _SCREAMING_SNAKE_CASE   :				Dict							    = rename_state_dict_key(lowerCamelCase__				)
    if new_k not in sd:
      raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})'''				)

    if "dense" in k or "proj" in new_k:
      _SCREAMING_SNAKE_CASE   :				List[Any]							    = v.T
    _SCREAMING_SNAKE_CASE   :				Tuple							    = torch.tensor(lowerCamelCase__,						dtype=sd[new_k].dtype				)
    assert v.shape == sd[new_k].shape, f'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
  # make sure embedding.padding_idx is respected
  _SCREAMING_SNAKE_CASE   :				List[str]							    = torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]				)
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = mapping["shared.weight"]
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = mapping["shared.weight"]
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = {k: torch.zeros_like(lowerCamelCase__				) for k, v in sd.items() if k.endswith("bias"				) and k not in mapping}
  mapping.update(**lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[Any]							    = torch_model.model.load_state_dict(lowerCamelCase__,						strict=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = [
      k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.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    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any]="./ckpt/aeslc/model.ckpt-32000"				)  ->    Dict:
  _SCREAMING_SNAKE_CASE   :				Tuple							    = tf.train.list_variables(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = {}
  _SCREAMING_SNAKE_CASE   :				Dict							    = ["Adafactor", "global_step"]
  for name, shape in tqdm(lowerCamelCase__,						desc="converting tf checkpoint to dict"				):
    _SCREAMING_SNAKE_CASE   :				Any							    = any(pat in name for pat in ignore_name				)
    if skip_key:
      continue
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = tf.train.load_variable(lowerCamelCase__,						lowerCamelCase__				)
    _SCREAMING_SNAKE_CASE   :				Tuple							    = array
  return tf_weights





def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str				)  ->    Union[str, Any]:
  # save tokenizer first
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = Path(lowerCamelCase__				).parent.name
  _SCREAMING_SNAKE_CASE   :				Tuple							    = task_specific_params[f'''summarization_{dataset}''']["max_position_embeddings"]
  _SCREAMING_SNAKE_CASE   :				Tuple							    = PegasusTokenizer.from_pretrained("sshleifer/pegasus",						model_max_length=lowerCamelCase__				)
  assert tok.model_max_length == desired_max_model_length
  tok.save_pretrained(lowerCamelCase__				)

  # convert model
  _SCREAMING_SNAKE_CASE   :				str							    = get_tf_weights_as_numpy(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = task_specific_params[f'''summarization_{dataset}''']
  if dataset == "large":
    _SCREAMING_SNAKE_CASE   :				Dict							    = task_specific_params
  _SCREAMING_SNAKE_CASE   :				List[str]							    = convert_pegasus(lowerCamelCase__,						lowerCamelCase__				)
  torch_model.save_pretrained(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Any							    = torch_model.state_dict()
  sd.pop("model.decoder.embed_positions.weight"				)
  sd.pop("model.encoder.embed_positions.weight"				)
  torch.save(lowerCamelCase__,						Path(lowerCamelCase__				) / "pytorch_model.bin"				)


if __name__ == "__main__":
      lowercase_       :						Any     					=	argparse.ArgumentParser()
      # Required parameters
      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.''')
      lowercase_       :						Union[str, Any]     					=	parser.parse_args()
      if args.save_dir is None:
            lowercase_       :						Union[str, Any]     					=	Path(args.tf_ckpt_path).parent.name
            lowercase_       :						Any     					=	os.path.join('''pegasus''', dataset)
      convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





"""simple docstring"""
from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)
@flax.struct.dataclass
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			42
@flax_register_to_config
class 			UpperCamelCase      (							nn.Module      ,       __SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE  ):
     A__      =			32
     A__      =			4
     A__      =			4
     A__      =			(
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "DownBlock2D",
     )
     A__      =			("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
     A__      =			False
     A__      =			(320, 640, 1280, 1280)
     A__      =			2
     A__      =			8
     A__      =			None
     A__      =			1280
     A__      =			0.0
     A__      =			False
     A__      =			jnp.floataa
     A__      =			True
     A__      =			0
     A__      =			False
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = (1, self.in_channels, self.sample_size, self.sample_size)
       _SCREAMING_SNAKE_CASE   :				int							    = jnp.zeros(snake_case__  ,     dtype=jnp.floataa				)
       _SCREAMING_SNAKE_CASE   :				str							    = jnp.ones((1,)  ,     dtype=jnp.intaa				)
       _SCREAMING_SNAKE_CASE   :				Any							    = jnp.zeros((1, 1, self.cross_attention_dim)  ,     dtype=jnp.floataa				)

       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Tuple							    = jax.random.split(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = {"params": params_rng, "dropout": dropout_rng}

       return self.init(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)["params"]
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.block_out_channels
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = block_out_channels[0] * 4

       if self.num_attention_heads is not None:
         raise ValueError(
             "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."				)

       # If `num_attention_heads` is not defined (which is the case for most models)
       # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
       # The reason for this behavior is to correct for incorrectly named variables that were introduced
       # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
       # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
       # which is why we correct for the naming here.
       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.num_attention_heads or self.attention_head_dim

       # input
       _SCREAMING_SNAKE_CASE   :				int							    = nn.Conv(
           block_out_channels[0]  ,     kernel_size=(3, 3)  ,     strides=(1, 1)  ,     padding=((1, 1), (1, 1))  ,     dtype=self.dtype  ,     )

       # time
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = FlaxTimesteps(
           block_out_channels[0]  ,     flip_sin_to_cos=self.flip_sin_to_cos  ,     freq_shift=self.config.freq_shift				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = FlaxTimestepEmbedding(snake_case__  ,     dtype=self.dtype				)

       _SCREAMING_SNAKE_CASE   :				str							    = self.only_cross_attention
       if isinstance(snake_case__  ,     snake_case__				):
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = (only_cross_attention,) * len(self.down_block_types				)

       if isinstance(snake_case__  ,     snake_case__				):
         _SCREAMING_SNAKE_CASE   :				int							    = (num_attention_heads,) * len(self.down_block_types				)

       # down
       _SCREAMING_SNAKE_CASE   :				Tuple							    = []
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = block_out_channels[0]
       for i, down_block_type in enumerate(self.down_block_types				):
         _SCREAMING_SNAKE_CASE   :				str							    = output_channel
         _SCREAMING_SNAKE_CASE   :				List[str]							    = block_out_channels[i]
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = i == len(snake_case__				) - 1

         if down_block_type == "CrossAttnDownBlock2D":
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = FlaxCrossAttnDownBlockaD(
               in_channels=snake_case__  ,     out_channels=snake_case__  ,     dropout=self.dropout  ,     num_layers=self.layers_per_block  ,     num_attention_heads=num_attention_heads[i]  ,     add_downsample=not is_final_block  ,     use_linear_projection=self.use_linear_projection  ,     only_cross_attention=only_cross_attention[i]  ,     use_memory_efficient_attention=self.use_memory_efficient_attention  ,     dtype=self.dtype  ,     )
         else:
           _SCREAMING_SNAKE_CASE   :				str							    = FlaxDownBlockaD(
               in_channels=snake_case__  ,     out_channels=snake_case__  ,     dropout=self.dropout  ,     num_layers=self.layers_per_block  ,     add_downsample=not is_final_block  ,     dtype=self.dtype  ,     )

         down_blocks.append(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = down_blocks

       # mid
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = FlaxUNetMidBlockaDCrossAttn(
           in_channels=block_out_channels[-1]  ,     dropout=self.dropout  ,     num_attention_heads=num_attention_heads[-1]  ,     use_linear_projection=self.use_linear_projection  ,     use_memory_efficient_attention=self.use_memory_efficient_attention  ,     dtype=self.dtype  ,     )

       # up
       _SCREAMING_SNAKE_CASE   :				int							    = []
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = list(reversed(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Any							    = list(reversed(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Any							    = list(reversed(snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = reversed_block_out_channels[0]
       for i, up_block_type in enumerate(self.up_block_types				):
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = output_channel
         _SCREAMING_SNAKE_CASE   :				List[str]							    = reversed_block_out_channels[i]
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = reversed_block_out_channels[min(i + 1  ,     len(snake_case__				) - 1				)]

         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = i == len(snake_case__				) - 1

         if up_block_type == "CrossAttnUpBlock2D":
           _SCREAMING_SNAKE_CASE   :				int							    = FlaxCrossAttnUpBlockaD(
               in_channels=snake_case__  ,     out_channels=snake_case__  ,     prev_output_channel=snake_case__  ,     num_layers=self.layers_per_block + 1  ,     num_attention_heads=reversed_num_attention_heads[i]  ,     add_upsample=not is_final_block  ,     dropout=self.dropout  ,     use_linear_projection=self.use_linear_projection  ,     only_cross_attention=only_cross_attention[i]  ,     use_memory_efficient_attention=self.use_memory_efficient_attention  ,     dtype=self.dtype  ,     )
         else:
           _SCREAMING_SNAKE_CASE   :				str							    = FlaxUpBlockaD(
               in_channels=snake_case__  ,     out_channels=snake_case__  ,     prev_output_channel=snake_case__  ,     num_layers=self.layers_per_block + 1  ,     add_upsample=not is_final_block  ,     dropout=self.dropout  ,     dtype=self.dtype  ,     )

         up_blocks.append(snake_case__				)
         _SCREAMING_SNAKE_CASE   :				Any							    = output_channel
       _SCREAMING_SNAKE_CASE   :				int							    = up_blocks

       # out
       _SCREAMING_SNAKE_CASE   :				str							    = nn.GroupNorm(num_groups=32  ,     epsilon=1E-5				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = nn.Conv(
           self.out_channels  ,     kernel_size=(3, 3)  ,     strides=(1, 1)  ,     padding=((1, 1), (1, 1))  ,     dtype=self.dtype  ,     )




     def __call__(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__=None  ,     snake_case__=None  ,     snake_case__ = True  ,     snake_case__ = False  ,     ):




       """simple docstring"""


       if not isinstance(snake_case__  ,     jnp.ndarray				):
         _SCREAMING_SNAKE_CASE   :				Any							    = jnp.array([timesteps]  ,     dtype=jnp.intaa				)
       elif isinstance(snake_case__  ,     jnp.ndarray				) and len(timesteps.shape				) == 0:
         _SCREAMING_SNAKE_CASE   :				Tuple							    = timesteps.astype(dtype=jnp.floataa				)
         _SCREAMING_SNAKE_CASE   :				Tuple							    = jnp.expand_dims(snake_case__  ,     0				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.time_proj(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.time_embedding(snake_case__				)

       # 2. pre-process
       _SCREAMING_SNAKE_CASE   :				Dict							    = jnp.transpose(snake_case__  ,     (0, 2, 3, 1)				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.conv_in(snake_case__				)

       # 3. down
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = (sample,)
       for down_block in self.down_blocks:
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				str							    = down_block(snake_case__  ,     snake_case__  ,     snake_case__  ,     deterministic=not train				)
         else:
           _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = down_block(snake_case__  ,     snake_case__  ,     deterministic=not train				)
         down_block_res_samples += res_samples

       if down_block_additional_residuals is not None:
         _SCREAMING_SNAKE_CASE   :				str							    = ()

         for down_block_res_sample, down_block_additional_residual in zip(
             snake_case__  ,     snake_case__				):
           down_block_res_sample += down_block_additional_residual
           new_down_block_res_samples += (down_block_res_sample,)

         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_down_block_res_samples

       # 4. mid
       _SCREAMING_SNAKE_CASE   :				Dict							    = self.mid_block(snake_case__  ,     snake_case__  ,     snake_case__  ,     deterministic=not train				)

       if mid_block_additional_residual is not None:
         sample += mid_block_additional_residual

       # 5. up
       for up_block in self.up_blocks:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = down_block_res_samples[-(self.layers_per_block + 1) :]
         _SCREAMING_SNAKE_CASE   :				int							    = down_block_res_samples[: -(self.layers_per_block + 1)]
         if isinstance(snake_case__  ,     snake_case__				):
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = up_block(
               snake_case__  ,     temb=snake_case__  ,     encoder_hidden_states=snake_case__  ,     res_hidden_states_tuple=snake_case__  ,     deterministic=not train  ,     )
         else:
           _SCREAMING_SNAKE_CASE   :				List[str]							    = up_block(snake_case__  ,     temb=snake_case__  ,     res_hidden_states_tuple=snake_case__  ,     deterministic=not train				)

        # 6. post-process
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.conv_norm_out(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = nn.silu(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.conv_out(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = jnp.transpose(snake_case__  ,     (0, 3, 1, 2)				)

       if not return_dict:
         return (sample,)

       return FlaxUNetaDConditionOutput(sample=snake_case__				)



