from manim import *





class       lowercase__		(     _SCREAMING_SNAKE_CASE):


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							Rectangle(height=0.5					,	width=0.5		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							Rectangle(height=0.46					,	width=0.46		).set_stroke(width=0		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							[mem.copy() for i in range(6		)]
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[mem.copy() for i in range(6		)]
												SCREAMING_SNAKE_CASE							:					str           =							VGroup(*A_		).arrange(A_					,	buff=0		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							VGroup(*A_		).arrange(A_					,	buff=0		)
												SCREAMING_SNAKE_CASE							:					Dict           =							VGroup(A_					,	A_		).arrange(A_					,	buff=0		)
												SCREAMING_SNAKE_CASE							:					Dict           =							Text('''CPU'''					,	font_size=24		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							Group(A_					,	A_		).arrange(A_					,	buff=0.5					,	aligned_edge=A_		)
												cpu.move_to([-2.5, -0.5, 0]		)
												self.add(A_		)

												SCREAMING_SNAKE_CASE							:					Dict           =							[mem.copy() for i in range(4		)]
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							VGroup(*A_		).arrange(A_					,	buff=0		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							Text('''GPU'''					,	font_size=24		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							Group(A_					,	A_		).arrange(A_					,	buff=0.5					,	aligned_edge=A_		)
												gpu.move_to([-1, -1, 0]		)
												self.add(A_		)

												SCREAMING_SNAKE_CASE							:					Dict           =							[mem.copy() for i in range(6		)]
												SCREAMING_SNAKE_CASE							:					Any           =							VGroup(*A_		).arrange(A_					,	buff=0		)

												SCREAMING_SNAKE_CASE							:					Dict           =							Text('''Model'''					,	font_size=24		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							Group(A_					,	A_		).arrange(A_					,	buff=0.5					,	aligned_edge=A_		)
												model.move_to([3, -1.0, 0]		)
												self.add(A_		)

												SCREAMING_SNAKE_CASE							:					int           =							[]
												for i, rect in enumerate(A_		):
																		rect.set_stroke(A_		)
																		# target = fill.copy().set_fill(YELLOW, opacity=0.7)
																		# target.move_to(rect)
																		# self.add(target)

																		SCREAMING_SNAKE_CASE							:					List[Any]           =							Rectangle(height=0.46 / 4					,	width=0.46 / 3		).set_stroke(width=0.0		).set_fill(A_					,	opacity=0.7		)

																		if i == 0:
																								cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)					,	buff=0.02					,	direction=A_		)
																								cpu_target.set_x(cpu_target.get_x() + 0.1		)
																		elif i == 3:
																								cpu_target.next_to(cpu_targs[0]					,	direction=A_					,	buff=0.0		)
																		else:
																								cpu_target.next_to(cpu_targs[i - 1]					,	direction=A_					,	buff=0.0		)
																		self.add(A_		)
																		cpu_targs.append(A_		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							[mem.copy() for i in range(6		)]
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							VGroup(*A_		).arrange(A_					,	buff=0		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							Text('''Loaded Checkpoint'''					,	font_size=24		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							Group(A_					,	A_		).arrange(A_					,	aligned_edge=A_					,	buff=0.4		)
												checkpoint.move_to([3, 0.5, 0]		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							Square(side_length=2.2		)
												key.move_to([-5, 2, 0]		)

												SCREAMING_SNAKE_CASE							:					str           =							MarkupText(
												    f"""<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model"""					,	font_size=18					,	)

												key_text.move_to([-5, 2.4, 0]		)

												self.add(A_					,	A_		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							MarkupText(
												    f"""<span fgcolor=\'{BLUE}\'>●</span> Checkpoint"""					,	font_size=18					,	)

												blue_text.next_to(A_					,	DOWN * 2.4					,	aligned_edge=key_text.get_left()		)

												SCREAMING_SNAKE_CASE							:					Tuple           =							MarkupText(
												    f"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."""					,	font_size=24					,	)
												step_a.move_to([2, 2, 0]		)
												self.play(Write(A_		)					,	Write(A_		)		)

												self.play(Write(A_					,	run_time=1		)					,	Create(A_					,	run_time=1		)		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							[]
												SCREAMING_SNAKE_CASE							:					Any           =							[]
												for i, rect in enumerate(A_		):
																		SCREAMING_SNAKE_CASE							:					str           =							fill.copy().set_fill(A_					,	opacity=0.7		)
																		target.move_to(A_		)
																		first_animations.append(GrowFromCenter(A_					,	run_time=1		)		)

																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							target.copy()
																		cpu_target.generate_target()
																		if i < 5:
																								cpu_target.target.move_to(cpu_left_col_base[i + 1]		)
																		else:
																								cpu_target.target.move_to(cpu_right_col_base[i - 5]		)
																		second_animations.append(MoveToTarget(A_					,	run_time=1.5		)		)

												self.play(*A_		)
												self.play(*A_		)
												self.wait()






import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput





class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       str					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							dataset
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							process
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							params


						def __len__( self			:       Tuple		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       List[str]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.dataset[i]
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.process(UpperCamelCase__					,	**self.params		)
												return processed






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Dict					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[Any]=None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							loader
												SCREAMING_SNAKE_CASE							:					List[Any]           =							infer
												SCREAMING_SNAKE_CASE							:					int           =							params
												if loader_batch_size == 1:
																		# Let's spare some time by deactivating altogether
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							None
												SCREAMING_SNAKE_CASE							:					List[str]           =							loader_batch_size

												# Internal bookkeeping
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							None
												SCREAMING_SNAKE_CASE							:					int           =							None


						def __len__( self			:       int		):





												'''simple docstring'''





												return len(self.loader		)


						def __iter__( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							iter(self.loader		)
												return self


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												if isinstance(self._loader_batch_data					,	torch.Tensor		):
																		# Batch data is simple tensor, just fetch the slice
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self._loader_batch_data[self._loader_batch_index]
												else:
																		# Batch data is assumed to be BaseModelOutput (or dict)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{}
																		for k, element in self._loader_batch_data.items():
																								if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																														# Convert ModelOutput to tuple first
																														SCREAMING_SNAKE_CASE							:					Dict           =							element.to_tuple()
																														if isinstance(element[0]					,	torch.Tensor		):
																																				SCREAMING_SNAKE_CASE							:					Dict           =							tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																														elif isinstance(element[0]					,	np.ndarray		):
																																				SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tuple(np.expand_dims(el[self._loader_batch_index]					,	0		) for el in element		)
																														continue
																								if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																														# Those are stored as lists of tensors so need specific unbatching.
																														if isinstance(element[0]					,	torch.Tensor		):
																																				SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
																														elif isinstance(element[0]					,	np.ndarray		):
																																				SCREAMING_SNAKE_CASE							:					List[str]           =							tuple(np.expand_dims(el[self._loader_batch_index]					,	0		) for el in element		)
																														continue
																								if element is None:
																														# This can happen for optional data that get passed around
																														SCREAMING_SNAKE_CASE							:					int           =							None
																								elif isinstance(element[self._loader_batch_index]					,	torch.Tensor		):
																														# Take correct batch data, but make it looked like batch_size=1
																														# For compatibility with other methods within transformers

																														SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							element[self._loader_batch_index].unsqueeze(0		)
																								elif isinstance(element[self._loader_batch_index]					,	np.ndarray		):
																														# Take correct batch data, but make it looked like batch_size=1
																														# For compatibility with other methods within transformers
																														SCREAMING_SNAKE_CASE							:					Tuple           =							np.expand_dims(element[self._loader_batch_index]					,	0		)
																								else:
																														# This is typically a list, so no need to `unsqueeze`.
																														SCREAMING_SNAKE_CASE							:					Tuple           =							element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																		SCREAMING_SNAKE_CASE							:					Any           =							self._loader_batch_data.__class__(UpperCamelCase__		)
												self._loader_batch_index += 1
												return result



						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																		# We are currently unrolling a batch so we just need to return
																		# the current item within a batch
																		return self.loader_batch_item()

												# We're out of items within a batch
												SCREAMING_SNAKE_CASE							:					Tuple           =							next(self.iterator		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.infer(UpperCamelCase__					,	**self.params		)
												# We now have a batch of "inferred things".
												if self.loader_batch_size is not None:
																		# Try to infer the size of the batch
																		if isinstance(UpperCamelCase__					,	torch.Tensor		):
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							processed
																		else:
																								SCREAMING_SNAKE_CASE							:					int           =							list(processed.keys()		)[0]
																								SCREAMING_SNAKE_CASE							:					int           =							processed[key]
																		if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							len(UpperCamelCase__		)
																		else:
																								SCREAMING_SNAKE_CASE							:					Dict           =							first_tensor.shape[0]
																		if 0 < observed_batch_size < self.loader_batch_size:
																								# could be last batch so we can't unroll as many
																								# elements.
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							observed_batch_size
																		# Setting internal index to unwrap the batch
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							processed
																		SCREAMING_SNAKE_CASE							:					int           =							0
																		return self.loader_batch_item()
												else:
																		# We're not unrolling batches
																		return processed






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       str					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Union[str, Any]=None		):





												'''simple docstring'''





												super().__init__(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)


						def __iter__( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							iter(self.loader		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							None
												return self



						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												if self.subiterator is None:
																		SCREAMING_SNAKE_CASE							:					Dict           =							self.infer(next(self.iterator		)					,	**self.params		)
												try:
																		# Try to return next item
																		SCREAMING_SNAKE_CASE							:					Any           =							next(self.subiterator		)
												except StopIteration:
																		# When a preprocess iterator ends, we can start lookig at the next item
																		# ChunkIterator will keep feeding until ALL elements of iterator
																		# all have created their subiterator and have been iterating against.
																		#
																		# Another way to look at it, is we're basically flattening lists of lists
																		# into a single list, but with generators
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.infer(next(self.iterator		)					,	**self.params		)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							next(self.subiterator		)
												return processed






class       lowercase__		(     UpperCamelCase_):


						def __iter__( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							iter(self.loader		)
												return self



						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							False
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[]
												if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																		while self._loader_batch_index < self.loader_batch_size:
																								SCREAMING_SNAKE_CASE							:					Tuple           =							self.loader_batch_item()
																								SCREAMING_SNAKE_CASE							:					Any           =							item.pop('''is_last'''		)
																								accumulator.append(UpperCamelCase__		)
																								if is_last:
																														return accumulator

												while not is_last:
																		SCREAMING_SNAKE_CASE							:					Any           =							self.infer(next(self.iterator		)					,	**self.params		)
																		if self.loader_batch_size is not None:
																								if isinstance(UpperCamelCase__					,	torch.Tensor		):
																														SCREAMING_SNAKE_CASE							:					Tuple           =							processed
																								else:
																														SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							list(processed.keys()		)[0]
																														SCREAMING_SNAKE_CASE							:					List[str]           =							processed[key]
																								if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																														SCREAMING_SNAKE_CASE							:					List[Any]           =							len(UpperCamelCase__		)
																								else:
																														SCREAMING_SNAKE_CASE							:					int           =							first_tensor.shape[0]
																								if 0 < observed_batch_size < self.loader_batch_size:
																														# could be last batch so we can't unroll as many
																														# elements.
																														SCREAMING_SNAKE_CASE							:					List[str]           =							observed_batch_size
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							processed
																								SCREAMING_SNAKE_CASE							:					str           =							0
																								while self._loader_batch_index < self.loader_batch_size:
																														SCREAMING_SNAKE_CASE							:					Any           =							self.loader_batch_item()
																														SCREAMING_SNAKE_CASE							:					List[Any]           =							item.pop('''is_last'''		)
																														accumulator.append(UpperCamelCase__		)
																														if is_last:
																																				return accumulator
																		else:
																								SCREAMING_SNAKE_CASE							:					int           =							processed
																								SCREAMING_SNAKE_CASE							:					List[str]           =							item.pop('''is_last'''		)
																								accumulator.append(UpperCamelCase__		)
												return accumulator






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Dataset					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							dataset
												SCREAMING_SNAKE_CASE							:					Dict           =							key


						def __len__( self			:       Optional[int]		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       Dict					,	UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





												return self.dataset[i][self.key]






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       List[Any]					,	UpperCamelCase__			:       Dataset					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							dataset
												SCREAMING_SNAKE_CASE							:					List[str]           =							keya
												SCREAMING_SNAKE_CASE							:					Tuple           =							keya


						def __len__( self			:       List[str]		):





												'''simple docstring'''





												return len(self.dataset		)



						def __getitem__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Any		):





												'''simple docstring'''





												return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}





