
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


a_						     =    logging.get_logger(__name__)


a_						     =    {
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}






class _UpperCamelCase							(							snake_case__   ):
			'''simple docstring'''







			lowerCamelCase__           ='''codegen'''
			lowerCamelCase__           ={
			    '''max_position_embeddings''': '''n_positions''',
			    '''hidden_size''': '''n_embd''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}
			def __init__(			self							: int      ,      a							: Union[str, Any]=5_0400      ,      a							: Any=2048      ,      a							: List[Any]=2048      ,      a							: Optional[Any]=4096      ,      a							: Optional[Any]=28      ,      a							: int=16      ,      a							: Optional[int]=64      ,      a							: List[Any]=None      ,      a							: List[str]="gelu_new"      ,      a							: Optional[Any]=0.0      ,      a							: Tuple=0.0      ,      a							: Dict=0.0      ,      a							: Tuple=1e-5      ,      a							: Dict=0.02      ,      a							: str=True      ,      a							: int=5_0256      ,      a							: Dict=5_0256      ,      a							: Any=False      ,      **a							: Union[str, Any]      ,      )							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      Tuple				     =       n_ctx
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       n_positions
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       n_embd
				SCREAMING_SNAKE_CASE   :      Any				     =       n_layer
				SCREAMING_SNAKE_CASE   :      Dict				     =       n_head
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       n_inner
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       rotary_dim
				SCREAMING_SNAKE_CASE   :      str				     =       activation_function
				SCREAMING_SNAKE_CASE   :      Any				     =       resid_pdrop
				SCREAMING_SNAKE_CASE   :      Tuple				     =       embd_pdrop
				SCREAMING_SNAKE_CASE   :      Tuple				     =       attn_pdrop
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       layer_norm_epsilon
				SCREAMING_SNAKE_CASE   :      str				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       use_cache

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       bos_token_id
				SCREAMING_SNAKE_CASE   :      Tuple				     =       eos_token_id

				super().__init__(
				    bos_token_id=_A      ,      eos_token_id=_A      ,      tie_word_embeddings=_A      ,      **_A	)






class _UpperCamelCase							(							snake_case__   ):
			'''simple docstring'''







			def __init__(			self							: int      ,      a							: int      ,      a							: int = "default"      ,      a							: Dict = None      ,      a							: List[Any] = False      ,      )							->       List[Any]:

				"""simple docstring"""

				super().__init__(_A      ,      task=_A      ,      patching_specs=_A      ,      use_past=_A	)
				if not getattr(self._config      ,      "pad_token_id"      ,      _A	):
					# TODO: how to do that better?
					SCREAMING_SNAKE_CASE   :      Dict				     =       0
			@property
			def 							__UpperCamelCase (			self							: List[str]	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
				if self.use_past:
					self.fill_with_past_key_values_(_A      ,      direction="inputs"	)
					SCREAMING_SNAKE_CASE   :      Tuple				     =       {0: 'batch', 1: 'past_sequence + sequence'}
				else:
					SCREAMING_SNAKE_CASE   :      int				     =       {0: 'batch', 1: 'sequence'}

				return common_inputs
			@property
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				return self._config.n_layer
			@property
			def 							__UpperCamelCase (			self							: int	)							->       int:

				"""simple docstring"""

				return self._config.n_head
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: Optional[int]      ,      a							: str = -1      ,      a							: Union[str, Any] = -1      ,      a							: Union[str, Any] = False      ,      a							: List[Any] = None      ,      )							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       super(_A      ,      self	).generate_dummy_inputs(
				    _A      ,      batch_size=_A      ,      seq_length=_A      ,      is_pair=_A      ,      framework=_A	)

				# We need to order the input in the way they appears in the forward()
				SCREAMING_SNAKE_CASE   :      str				     =       OrderedDict({"input_ids": common_inputs["input_ids"]}	)

				# Need to add the past_keys
				if self.use_past:
					if not is_torch_available():
						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
					else:
						import torch

						SCREAMING_SNAKE_CASE   :      Any				     =       common_inputs['input_ids'].shape
						# Not using the same length for past_key_values
						SCREAMING_SNAKE_CASE   :      Any				     =       seqlen + 2
						SCREAMING_SNAKE_CASE   :      List[str]				     =       (
						    batch,
						    self.num_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // self.num_attention_heads,
						)
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [
						    (torch.zeros(_A	), torch.zeros(_A	)) for _ in range(self.num_layers	)
						]

				SCREAMING_SNAKE_CASE   :      Tuple				     =       common_inputs['attention_mask']
				if self.use_past:
					SCREAMING_SNAKE_CASE   :      str				     =       ordered_inputs['attention_mask'].dtype
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.cat(
					    [ordered_inputs["attention_mask"], torch.ones(_A      ,      _A      ,      dtype=_A	)]      ,      dim=1	)

				return ordered_inputs






			@property
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       int:

				"""simple docstring"""

				return 13


import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)

def  lowerCamelCase__ (					_a):
	if hor == 128:
		SCREAMING_SNAKE_CASE   :      str				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (32, 128, 256)
		SCREAMING_SNAKE_CASE   :      List[str]				     =       ("UpResnetBlock1D", "UpResnetBlock1D")

	elif hor == 32:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (32, 64, 128, 256)
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
	SCREAMING_SNAKE_CASE   :      Any				     =       torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
	SCREAMING_SNAKE_CASE   :      int				     =       model.state_dict()
	SCREAMING_SNAKE_CASE   :      Dict				     =       {
	    "down_block_types": down_block_types,
	    "block_out_channels": block_out_channels,
	    "up_block_types": up_block_types,
	    "layers_per_block": 1,
	    "use_timestep_embedding": True,
	    "out_block_type": "OutConv1DBlock",
	    "norm_num_groups": 8,
	    "downsample_each_block": False,
	    "in_channels": 14,
	    "out_channels": 14,
	    "extra_in_channels": 0,
	    "time_embedding_type": "positional",
	    "flip_sin_to_cos": False,
	    "freq_shift": 1,
	    "sample_size": 65536,
	    "mid_block_type": "MidResTemporalBlock1D",
	    "act_fn": "mish",
	}
	SCREAMING_SNAKE_CASE   :      str				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
	SCREAMING_SNAKE_CASE   :      List[str]				     =       dict(zip(model.state_dict().keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      int				     =       state_dict.pop(_a)
	hf_value_function.load_state_dict(_a)

	torch.save(hf_value_function.state_dict()							,			f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin")
	with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json"							,			"w") as f:
		json.dump(_a							,			_a)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      int				     =       {
	    "in_channels": 14,
	    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
	    "up_block_types": (),
	    "out_block_type": "ValueFunction",
	    "mid_block_type": "ValueFunctionMidBlock1D",
	    "block_out_channels": (32, 64, 128, 256),
	    "layers_per_block": 1,
	    "downsample_each_block": True,
	    "sample_size": 65536,
	    "out_channels": 14,
	    "extra_in_channels": 0,
	    "time_embedding_type": "positional",
	    "use_timestep_embedding": True,
	    "flip_sin_to_cos": False,
	    "freq_shift": 1,
	    "norm_num_groups": 8,
	    "act_fn": "mish",
	}

	SCREAMING_SNAKE_CASE   :      Dict				     =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
	SCREAMING_SNAKE_CASE   :      Dict				     =       model
	SCREAMING_SNAKE_CASE   :      List[str]				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")

	SCREAMING_SNAKE_CASE   :      int				     =       dict(zip(state_dict.keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       state_dict.pop(_a)

	hf_value_function.load_state_dict(_a)

	torch.save(hf_value_function.state_dict()							,			"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin")
	with open("hub/hopper-medium-v2/value_function/config.json"							,			"w") as f:
		json.dump(_a							,			_a)


if __name__ == "__main__":
			unet(32)
			# unet(128)
			value_function()