


'''simple docstring'''




from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_a   :   List[Any]												=   logging.get_logger(__name__)





def   _a		(lowercase__			:   int							,   lowercase__			:   Optional[Any]						)			->			Dict:






     """simple docstring"""
     try:
          with open(lowercase__							,   'rb'						) as flax_state_f:
               __snake_case           =						from_bytes(lowercase__							,   flax_state_f.read()						)
     except UnpicklingError as e:
          try:
               with open(lowercase__						) as f:
                    if f.read().startswith('version'						):
                         raise OSError(
                             'You seem to have cloned a repository without having git-lfs installed. Please'
                             ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
                             ' folder you cloned.'						)
                    else:
                         raise ValueError from e
          except (UnicodeDecodeError, ValueError):
               raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. '						)

     return load_flax_weights_in_pytorch_model(lowercase__							,   lowercase__						)





def   _a		(lowercase__			:   int							,   lowercase__			:   Dict						)			->			Tuple:






     """simple docstring"""

     try:
          import torch  # noqa: F401
     except ImportError:
          logger.error(
              'Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'
              ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
              ' instructions.'						)
          raise

     # check if we have bf16 weights
     __snake_case           =						flatten_dict(jax.tree_util.tree_map(lambda lowercase__						: x.dtype == jnp.bfloataa							,   lowercase__						)						).values()
     if any(lowercase__						):
          # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

          # and bf16 is not fully supported in PT yet.
          logger.warning(
              'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
              'before loading those in PyTorch model.'						)
          __snake_case           =						jax.tree_util.tree_map(
              lambda lowercase__						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params							,   lowercase__						)

     __snake_case           =						''

     __snake_case           =						flatten_dict(lowercase__							,   sep='.'						)
     __snake_case           =						pt_model.state_dict()

     # keep track of unexpected & missing keys
     __snake_case           =						[]
     __snake_case           =						set(pt_model_dict.keys()						)

     for flax_key_tuple, flax_tensor in flax_state_dict.items():
          __snake_case           =						flax_key_tuple.split('.'						)

          if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
               __snake_case           =						flax_key_tuple_array[:-1] + ['weight']
               __snake_case           =						jnp.transpose(lowercase__							,   (3, 2, 0, 1)						)
          elif flax_key_tuple_array[-1] == "kernel":
               __snake_case           =						flax_key_tuple_array[:-1] + ['weight']
               __snake_case           =						flax_tensor.T
          elif flax_key_tuple_array[-1] == "scale":
               __snake_case           =						flax_key_tuple_array[:-1] + ['weight']

          if "time_embedding" not in flax_key_tuple_array:
               for i, flax_key_tuple_string in enumerate(lowercase__						):
                    __snake_case           =						(
                        flax_key_tuple_string.replace('_0'							,   '.0'						)
                        .replace('_1'							,   '.1'						)
                        .replace('_2'							,   '.2'						)
                        .replace('_3'							,   '.3'						)
                        .replace('_4'							,   '.4'						)
                        .replace('_5'							,   '.5'						)
                        .replace('_6'							,   '.6'						)
                        .replace('_7'							,   '.7'						)
                        .replace('_8'							,   '.8'						)
                        .replace('_9'							,   '.9'						)
                    )

          __snake_case           =						'.'.join(lowercase__						)

          if flax_key in pt_model_dict:
               if flax_tensor.shape != pt_model_dict[flax_key].shape:
                    raise ValueError(
                        f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
                        f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'						)
               else:
                    # add weight to pytorch dict
                    __snake_case           =						np.asarray(lowercase__						) if not isinstance(lowercase__							,   np.ndarray						) else flax_tensor
                    __snake_case           =						torch.from_numpy(lowercase__						)
                    # remove from missing keys
                    missing_keys.remove(lowercase__						)
          else:
               # weight is not expected by PyTorch model
               unexpected_keys.append(lowercase__						)

     pt_model.load_state_dict(lowercase__						)

     # re-transform missing_keys to list
     __snake_case           =						list(lowercase__						)

     if len(lowercase__						) > 0:
          logger.warning(
              'Some weights of the Flax model were not used when initializing the PyTorch model'
              f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
              f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
              ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
              f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
              ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
              ' FlaxBertForSequenceClassification model).'						)
     if len(lowercase__						) > 0:
          logger.warning(
              f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
              f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
              ' use it for predictions and inference.'						)

     return pt_model







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

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


A_			:					List[Any]    			=     logging.get_logger(__name__)

A_			:					Any    			=     {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  List[str]    				=			'''gpt_neo'''
					UpperCAmelCase__:  str    				=			['''past_key_values''']
					UpperCAmelCase__:  Dict    				=			{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






					def __init__(					self       ,			A__=5_0257       ,			A__=2048       ,			A__=2048       ,			A__=24       ,			A__=[[["global", "local"], 12]]       ,			A__=16       ,			A__=None       ,			A__=256       ,			A__="gelu_new"       ,			A__=0.0       ,			A__=0.0       ,			A__=0.0       ,			A__=0.1       ,			A__=1e-5       ,			A__=0.0_2       ,			A__=True       ,			A__=5_0256       ,			A__=5_0256       ,			**A__       ,			):
						A__				:       Optional[Any]	  =						vocab_size
						A__				:       Optional[int]	  =						max_position_embeddings
						A__				:       Optional[int]	  =						hidden_size
						A__				:       Dict	  =						num_layers
						A__				:       List[str]	  =						num_heads
						A__				:       Optional[int]	  =						intermediate_size
						A__				:       Union[str, Any]	  =						window_size
						A__				:       Optional[int]	  =						activation_function
						A__				:       Optional[Any]	  =						resid_dropout
						A__				:       Optional[int]	  =						embed_dropout
						A__				:       Any	  =						attention_dropout
						A__				:       Optional[Any]	  =						classifier_dropout
						A__				:       int	  =						layer_norm_epsilon
						A__				:       Tuple	  =						initializer_range
						A__				:       str	  =						use_cache

						A__				:       List[str]	  =						bos_token_id
						A__				:       List[Any]	  =						eos_token_id

						A__				:       Any	  =						attention_types
						A__				:       List[str]	  =						self.expand_attention_types_params(A__     )

						if len(self.attention_layers     ) != self.num_layers:
							raise ValueError(
							    """Configuration for convolutional module is incorrect. """
							    """It is required that `len(config.attention_layers)` == `config.num_layers` """
							    F"""but is `len(config.attention_layers) = {len(self.attention_layers     )}`, """
							    F"""`config.num_layers = {self.num_layers}`. """
							    """`config.attention_layers` is prepared using `config.attention_types`. """
							    """Please verify the value of `config.attention_types` argument."""     )

						super().__init__(bos_token_id=A__       ,			eos_token_id=A__       ,			**A__     )






					@staticmethod
					def     __A     (					A__     ):
						A__				:       int	  =						[]
						for item in attention_types:
							for _ in range(item[1]     ):
								attentions.extend(item[0]     )
						return attentions



def 		UpperCamelCase   (lowercase_:					Dict       , lowercase_:					Any       , lowercase_:					Union[str, Any]       , lowercase_:					Optional[Any]   )	-> Dict:
	import torch

	A__				:       List[Any]	  =						input.size()
	A__				:       List[str]	  =						len(lowercase_   )
	A__				:       Union[str, Any]	  =						shape[dimension]

	A__				:       Optional[int]	  =						torch.arange(0       , lowercase_       , lowercase_   )
	A__				:       str	  =						torch.div(sizedim - size       , lowercase_       , rounding_mode="""floor"""   ) + 1
	A__				:       Any	  =						torch.arange(lowercase_   ) + low_indices[:min_length][:, None]

	A__				:       Union[str, Any]	  =						[slice(lowercase_   )] * rank
	A__				:       Any	  =						indices
	A__				:       List[str]	  =						input[s]

	A__				:       Dict	  =						list(range(0       , rank + 1   )   )
	perm.append(perm.pop(dimension + 1   )   )

	return sliced.permute(lowercase_   )

def 		UpperCamelCase   (lowercase_:					str       , lowercase_:					int   )	-> int:
	import torch

	A__				:       Union[str, Any]	  =						torch.arange(1       , lowercase_   )
	A__				:       str	  =						torch.remainder(lowercase_       , lowercase_   )
	A__				:       Optional[Any]	  =						remainders == 0
	A__				:       Any	  =						candidates[divisor_indices]
	A__				:       Optional[Any]	  =						torch.max(lowercase_   )
	return largest_divisor, torch.div(lowercase_       , lowercase_       , rounding_mode="""floor"""   )



class        _a		(__magic_name__							):


					'''simple docstring'''






					@property
					def     __A     (					self     ):
						A__				:       Any	  =						OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}     )
						if self.use_past:
							self.fill_with_past_key_values_(A__       ,			direction="""inputs"""     )
							A__				:       Optional[Any]	  =						{0: """batch""", 1: """past_sequence + sequence"""}
						else:
							A__				:       List[str]	  =						{0: """batch""", 1: """sequence"""}

						return common_inputs






					@property
					def     __A     (					self     ):
						return self._config.num_heads






					def     __A     (					self       ,			A__       ,			A__ = -1       ,			A__ = -1       ,			A__ = False       ,			A__ = None       ,			):
						A__				:       int	  =						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()
						A__				:       Optional[int]	  =						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

								A__  ,  A__				:       Dict	  =						common_inputs["""input_ids"""].shape
								# Not using the same length for past_key_values
								A__				:       int	  =						seqlen + 2
								A__				:       int	  =						(
								    batch,
								    self.num_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // self.num_attention_heads,
								)
								A__				:       Dict	  =						[
								    (torch.zeros(A__     ), torch.zeros(A__     )) for _ in range(self.num_layers     )
								]

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

						return ordered_inputs






					@property
					def     __A     (					self     ):
						return 13



