
'''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


lowercase__						       =      logging.get_logger(__name__)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[Any]:


		'''simple docstring'''


		try:
				with open(__snake_case ,       '''rb'''			) as flax_state_f:
						snake_case  :       List[Any]     			=						from_bytes(__snake_case ,       flax_state_f.read()			)
		except UnpicklingError as e:
				try:
						with open(__snake_case			) 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(__snake_case ,       __snake_case			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Optional[Any]:


		'''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  :       int     			=						flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE__			: x.dtype == jnp.bfloataa ,       __snake_case			)			).values()
		if any(__snake_case			):
				# 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  :       Optional[Any]     			=						jax.tree_util.tree_map(
				    lambda SCREAMING_SNAKE_CASE__			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params ,       __snake_case			)

		snake_case  :       str     			=						''''''

		snake_case  :       str     			=						flatten_dict(__snake_case ,       sep='''.'''			)
		snake_case  :       Optional[int]     			=						pt_model.state_dict()

		# keep track of unexpected & missing keys
		snake_case  :       Any     			=						[]
		snake_case  :       Optional[int]     			=						set(pt_model_dict.keys()			)

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				snake_case  :       Optional[Any]     			=						flax_key_tuple.split('''.'''			)

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

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(__snake_case			):
								snake_case  :       Optional[int]     			=						(
								    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  :       str     			=						'''.'''.join(__snake_case			)

				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  :       int     			=						np.asarray(__snake_case			) if not isinstance(__snake_case ,       np.ndarray			) else flax_tensor
								snake_case  :       Optional[Any]     			=						torch.from_numpy(__snake_case			)
								# remove from missing keys
								missing_keys.remove(__snake_case			)
				else:
						# weight is not expected by PyTorch model
						unexpected_keys.append(__snake_case			)

		pt_model.load_state_dict(__snake_case			)

		# re-transform missing_keys to list
		snake_case  :       Optional[Any]     			=						list(__snake_case			)

		if len(__snake_case			) > 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(__snake_case			) > 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



'''simple docstring'''

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def snake_case_      (					*__snake_case  :							Optional[int])			->					int:
   if not isinstance(__snake_case	,   __snake_case):
      lowerCAmelCase_			=      list(__snake_case)
   for i in range(len(__snake_case)):
      lowerCAmelCase_			=      None
   gc.collect()
   if is_xpu_available():
      torch.xpu.empty_cache()
   elif is_npu_available():
      torch.npu.empty_cache()
   else:
      torch.cuda.empty_cache()
   return objects



def snake_case_      (					__snake_case  :							Exception)			->					bool:
   lowerCAmelCase_			=      [
       '''CUDA out of memory.''',  # CUDA OOM
       '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
       '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
   ]
   if isinstance(__snake_case	,   __snake_case) and len(exception.args) == 1:
      return any(err in exception.args[0] for err in _statements)
   return False






def snake_case_      (					__snake_case  :							callable = None	,   __snake_case  :							int = 128)			->					List[Any]:
   if function is None:
      return functools.partial(__snake_case	,   starting_batch_size=__snake_case)

   lowerCAmelCase_			=      starting_batch_size

   def decorator(*__snake_case  :							List[str]	,   **__snake_case  :							Union[str, Any]):
      nonlocal batch_size
      gc.collect()
      if is_xpu_available():
         torch.xpu.empty_cache()
      elif is_npu_available():
         torch.npu.empty_cache()
      else:
         torch.cuda.empty_cache()
      lowerCAmelCase_			=      list(inspect.signature(__snake_case).parameters.keys())
      # Guard against user error
      if len(__snake_case) < (len(__snake_case) + 1):
         lowerCAmelCase_			=      ''', '''.join([F'''{arg}={value}''' for arg, value in zip(params[1:]	,   args[1:])])
         raise TypeError(
             F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
             F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`''')
      while True:
         if batch_size == 0:
            raise RuntimeError('''No executable batch size found, reached zero.''')
         try:
            return function(__snake_case	,   *__snake_case	,   **__snake_case)
         except Exception as e:
            if should_reduce_batch_size(__snake_case):
               gc.collect()
               if is_xpu_available():
                  torch.xpu.empty_cache()
               elif is_npu_available():
                  torch.npu.empty_cache()
               else:
                  torch.cuda.empty_cache()
               batch_size //= 2
            else:
               raise

   return decorator





