


import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


lowerCAmelCase       =							False
try:
			lowerCAmelCase       =							_is_package_available('google.colab')
except ModuleNotFoundError:
			pass


@input.register
class        _a							:

							def __init__(							self:	List[str]    ,						UpperCamelCase_:	str = None    ,						UpperCamelCase_:	list = []							)			->   str:





									"""simple docstring"""







									lowercase__            =      0
									lowercase__            =      choices
									lowercase__            =      prompt
									if sys.platform == "win32":
											lowercase__            =      '''*'''
									else:
											lowercase__            =      '''➔ '''

							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str = ""							)			->   Any:





									"""simple docstring"""







									if sys.platform != "win32":
											writeColor(self.choices[index]    ,						32    ,						UpperCamelCase_							)
									else:
											forceWrite(self.choices[index]    ,						UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	int							)			->   Tuple:





									"""simple docstring"""







									if index == self.position:
											forceWrite(f' {self.arrow_char} '							)
											self.write_choice(UpperCamelCase_							)
									else:
											forceWrite(f'    {self.choices[index]}'							)
									reset_cursor()

							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	Direction    ,						UpperCamelCase_:	int = 1							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      self.position
									if direction == Direction.DOWN:
											if self.position + 1 >= len(self.choices							):
													return
											self.position += num_spaces
									else:
											if self.position - 1 < 0:
													return
											self.position -= num_spaces
									clear_line()
									self.print_choice(UpperCamelCase_							)
									move_cursor(UpperCamelCase_    ,						direction.name							)
									self.print_choice(self.position							)

							@input.mark(KEYMAP['''up''']							)
							def       lowerCamelCase_ (							self:	Optional[int]							)			->   Optional[Any]:





									"""simple docstring"""







									self.move_direction(Direction.UP							)

							@input.mark(KEYMAP['''down''']							)
							def       lowerCamelCase_ (							self:	Tuple							)			->   Union[str, Any]:





									"""simple docstring"""







									self.move_direction(Direction.DOWN							)

							@input.mark(KEYMAP['''newline''']							)
							def       lowerCamelCase_ (							self:	Optional[int]							)			->   int:





									"""simple docstring"""







									move_cursor(len(self.choices							) - self.position    ,						'''DOWN'''							)
									return self.position

							@input.mark(KEYMAP['''interrupt''']							)
							def       lowerCamelCase_ (							self:	List[Any]							)			->   Any:





									"""simple docstring"""







									move_cursor(len(self.choices							) - self.position    ,						'''DOWN'''							)
									raise KeyboardInterrupt

							@input.mark_multiple(*[KEYMAP[str(UpperCamelCase_							)] for number in range(10							)]							)
							def       lowerCamelCase_ (							self:	List[str]							)			->   List[Any]:





									"""simple docstring"""







									lowercase__            =      int(chr(self.current_selection							)							)
									lowercase__            =      index - self.position
									if index == self.position:
											return
									if index < len(self.choices							):
											if self.position > index:
													self.move_direction(Direction.UP    ,						-movement							)
											elif self.position < index:
													self.move_direction(Direction.DOWN    ,						UpperCamelCase_							)
											else:
													return
									else:
											return


							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	int = 0							)			->   List[str]:





									"""simple docstring"""







									if self.prompt:
											linebreak()
											forceWrite(self.prompt    ,						'''\n'''							)
											if in_colab:
													forceWrite('''Please input a choice index (starting from 0), and press enter'''    ,						'''\n'''							)
											else:
													forceWrite('''Please select a choice using the arrow or number keys, and selecting with enter'''    ,						'''\n'''							)
									lowercase__            =      default_choice
									for i in range(len(self.choices							)							):
											self.print_choice(UpperCamelCase_							)
											forceWrite('''\n'''							)
									move_cursor(len(self.choices							) - self.position    ,						'''UP'''							)
									with cursor.hide():
											while True:
													if in_colab:
															try:
																	lowercase__            =      int(builtins.input()							)
															except ValueError:
																	lowercase__            =      default_choice
													else:
															lowercase__            =      self.handle_input()
													if choice is not None:
															reset_cursor()
															for _ in range(len(self.choices							) + 1							):
																	move_cursor(1    ,						'''UP'''							)
																	clear_line()
															self.write_choice(UpperCamelCase_    ,						'''\n'''							)
															return choice






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


lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		try:
				with open(SCREAMING_SNAKE_CASE						,				'''rb'''     ) as flax_state_f:
						lowercase__            =      from_bytes(SCREAMING_SNAKE_CASE						,				flax_state_f.read()     )
		except UnpicklingError as e:
				try:
						with open(SCREAMING_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(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""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
		lowercase__            =      flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE     : x.dtype == jnp.bfloataa						,				SCREAMING_SNAKE_CASE     )     ).values()
		if any(SCREAMING_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.'''     )
				lowercase__            =      jax.tree_util.tree_map(
				    lambda SCREAMING_SNAKE_CASE     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params						,				SCREAMING_SNAKE_CASE     )

		lowercase__            =      ''''''

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

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

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

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

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(SCREAMING_SNAKE_CASE     ):
								lowercase__            =      (
								    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'''     )
								)

				lowercase__            =      '''.'''.join(SCREAMING_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
								lowercase__            =      np.asarray(SCREAMING_SNAKE_CASE     ) if not isinstance(SCREAMING_SNAKE_CASE						,				np.ndarray     ) else flax_tensor
								lowercase__            =      torch.from_numpy(SCREAMING_SNAKE_CASE     )
								# remove from missing keys
								missing_keys.remove(SCREAMING_SNAKE_CASE     )
				else:
						# weight is not expected by PyTorch model
						unexpected_keys.append(SCREAMING_SNAKE_CASE     )

		pt_model.load_state_dict(SCREAMING_SNAKE_CASE     )

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

		if len(SCREAMING_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(SCREAMING_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


