





'''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__					:			Optional[Any]          =     logging.get_logger(__name__)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Optional[int]:





	try:
		with open(snake_case__					,				'rb'							) as flax_state_f:
			snake_case__	       =   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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Tuple:





	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 UpperCAmelCase_							: 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__	       =   jax.tree_util.tree_map(
		    lambda UpperCAmelCase_							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params					,				snake_case__							)

	snake_case__	       =   ''

	snake_case__	       =   flatten_dict(snake_case__					,				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(snake_case__					,				(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(snake_case__							):
				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(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__	       =   np.asarray(snake_case__							) if not isinstance(snake_case__					,				np.ndarray							) else flax_tensor
				snake_case__	       =   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__	       =   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








import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_=""    ,a_="train"						):







						"""simple docstring"""
						assert os.path.isdir(a_						)

						lowerCAmelCase__		  =  []
						lowerCAmelCase__		  =  os.listdir(a_						)
						for story_filename in story_filenames_list:
										if "summary" in story_filename:
														continue
										lowerCAmelCase__		  =  os.path.join(a_    ,a_						)
										if not os.path.isfile(a_						):
														continue
										self.documents.append(a_						)
		def __len__(							self						):







						"""simple docstring"""
						return len(self.documents						)



		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.documents[idx]
						lowerCAmelCase__		  =  document_path.split('/'						)[-1]
						with open(a_    ,encoding='utf-8'						) as source:
										lowerCAmelCase__		  =  source.read()
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  process_story(a_						)
						return document_name, story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: len(snake_case__					) != 0    ,			[line.strip() for line in raw_story.split('\n'					)]					)					)

				# for some unknown reason some lines miss a period, add it
				lowerCAmelCase__		  =  [_add_missing_period(snake_case__					) for line in nonempty_lines]

				# gather article lines
				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  deque(snake_case__					)
				while True:
								try:
												lowerCAmelCase__		  =  lines.popleft()
												if element.startswith('@highlight'					):
																break
												story_lines.append(snake_case__					)
								except IndexError:
												# if "@highlight" is absent from the file we pop
												# all elements until there is None, raising an exception.
												return story_lines, []

    # gather summary lines
				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: not t.startswith('@highlight'					)    ,			snake_case__					)					)

				return story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
				if line.startswith('@highlight'					):
								return line
				if line[-1] in END_TOKENS:
								return line
				return line + "."







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				if len(snake_case__					) > block_size:
								return sequence[:block_size]
				else:
								sequence.extend([pad_token_id] * (block_size - len(snake_case__					))					)
								return sequence







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  torch.ones_like(snake_case__					)
				lowerCAmelCase__		  =  sequence == pad_token_id
				lowerCAmelCase__		  =  0
				return mask







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in story_lines]
				lowerCAmelCase__		  =  [token for sentence in story_lines_token_ids for token in sentence]
				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in summary_lines]
				lowerCAmelCase__		  =  [token for sentence in summary_lines_token_ids for token in sentence]

				return story_token_ids, summary_token_ids







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Union[str, Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				for sequence in batch:
								lowerCAmelCase__		  =  -1
								lowerCAmelCase__		  =  []
								for s in sequence:
												if s == separator_token_id:
																sentence_num += 1
												embeddings.append(sentence_num % 2					)
								batch_embeddings.append(snake_case__					)
				return torch.tensor(snake_case__					)
