'''simple docstring'''




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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


_lowercase						: Tuple						=						logging.get_logger(__name__)

_lowercase						: Any						=						{
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}





class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       '''perceiver'''







			def __init__(	self				:  List[str]				,			lowercase_				:  Optional[int]=256				,			lowercase_				:  List[str]=1280				,			lowercase_				:  int=768				,			lowercase_				:  List[str]=1				,			lowercase_				:  List[str]=26				,			lowercase_				:  List[str]=8				,			lowercase_				:  Any=8				,			lowercase_				:  str=None				,			lowercase_				:  Dict=None				,			lowercase_				:  Union[str, Any]="kv"				,			lowercase_				:  Union[str, Any]=1				,			lowercase_				:  Dict=1				,			lowercase_				:  List[Any]="gelu"				,			lowercase_				:  Optional[int]=0.1				,			lowercase_				:  Dict=0.02				,			lowercase_				:  List[str]=1E-12				,			lowercase_				:  Optional[Any]=True				,			lowercase_				:  Optional[int]=262				,			lowercase_				:  Any=2048				,			lowercase_				:  Union[str, Any]=56				,			lowercase_				:  Dict=[368, 496]				,			lowercase_				:  str=16				,			lowercase_				:  Dict=1920				,			lowercase_				:  List[Any]=16				,			lowercase_				:  List[Any]=[1, 16, 224, 224]				,			**lowercase_				:  int				,			):
										super().__init__(**lowercase_    )

										lowercase_					:			Tuple										=       num_latents
										lowercase_					:			List[str]										=       d_latents
										lowercase_					:			Any										=       d_model
										lowercase_					:			Any										=       num_blocks
										lowercase_					:			str										=       num_self_attends_per_block
										lowercase_					:			List[Any]										=       num_self_attention_heads
										lowercase_					:			Optional[int]										=       num_cross_attention_heads
										lowercase_					:			int										=       qk_channels
										lowercase_					:			List[str]										=       v_channels
										lowercase_					:			Optional[int]										=       cross_attention_shape_for_attention
										lowercase_					:			str										=       self_attention_widening_factor
										lowercase_					:			List[Any]										=       cross_attention_widening_factor
										lowercase_					:			Optional[Any]										=       hidden_act
										lowercase_					:			Dict										=       attention_probs_dropout_prob
										lowercase_					:			Optional[Any]										=       initializer_range
										lowercase_					:			int										=       layer_norm_eps
										lowercase_					:			List[str]										=       use_query_residual
										# masked language modeling attributes
										lowercase_					:			Any										=       vocab_size
										lowercase_					:			List[Any]										=       max_position_embeddings
										# image classification attributes
										lowercase_					:			Tuple										=       image_size
										# flow attributes
										lowercase_					:			str										=       train_size
										# multimodal autoencoding attributes
										lowercase_					:			int										=       num_frames
										lowercase_					:			Union[str, Any]										=       audio_samples_per_frame
										lowercase_					:			Dict										=       samples_per_patch
										lowercase_					:			Any										=       output_shape





class     __magic_name__						(    _UpperCAmelCase):







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										if self.task == "multiple-choice":
																	lowercase_					:			Any										=       {0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
																	lowercase_					:			Dict										=       {0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""inputs""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]    )







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										return 1E-4







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]				,			lowercase_				:  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]				,			lowercase_				:  int = -1				,			lowercase_				:  int = -1				,			lowercase_				:  int = -1				,			lowercase_				:  bool = False				,			lowercase_				:  Optional[TensorType] = None				,			lowercase_				:  int = 3				,			lowercase_				:  int = 40				,			lowercase_				:  int = 40				,			):
										# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

										if isinstance(lowercase_				,			lowercase_    ):
																	# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																	lowercase_					:			Optional[int]										=       compute_effective_axis_dimension(
																	    lowercase_				,			fixed_dimension=OnnxConfig.default_fixed_batch				,			num_token_to_add=0    )
																	# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
																	lowercase_					:			Union[str, Any]										=       preprocessor.num_special_tokens_to_add(lowercase_    )
																	lowercase_					:			List[Any]										=       compute_effective_axis_dimension(
																	    lowercase_				,			fixed_dimension=OnnxConfig.default_fixed_sequence				,			num_token_to_add=lowercase_    )
																	# Generate dummy inputs according to compute batch and sequence
																	lowercase_					:			Optional[int]										=       [""" """.join(["""a"""]    ) * seq_length] * batch_size
																	lowercase_					:			Tuple										=       dict(preprocessor(lowercase_				,			return_tensors=lowercase_    )    )
																	lowercase_					:			Dict										=       inputs.pop("""input_ids"""    )
																	return inputs
										elif isinstance(lowercase_				,			lowercase_    ) and preprocessor.model_input_names[0] == "pixel_values":
																	# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																	lowercase_					:			Any										=       compute_effective_axis_dimension(lowercase_				,			fixed_dimension=OnnxConfig.default_fixed_batch    )
																	lowercase_					:			Optional[int]										=       self._generate_dummy_images(lowercase_				,			lowercase_				,			lowercase_				,			lowercase_    )
																	lowercase_					:			Tuple										=       dict(preprocessor(images=lowercase_				,			return_tensors=lowercase_    )    )
																	lowercase_					:			Optional[Any]										=       inputs.pop("""pixel_values"""    )
																	return inputs
										else:
																	raise ValueError(
																	    """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."""    )






'''simple docstring'''




import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler





class     __magic_name__						:







			def __init__(	self				:  str				,			lowercase_				:  Dict    ):
										if isinstance(lowercase_				,			lowercase_    ):
																	# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
																	# modified it, it will not be accepted here again, since `auto` values would have been overridden
																	lowercase_					:			List[Any]										=       deepcopy(lowercase_    )
										elif os.path.exists(lowercase_    ):
																	with io.open(lowercase_				,			"""r"""				,			encoding="""utf-8"""    ) as f:
																								lowercase_					:			Union[str, Any]										=       json.load(lowercase_    )
										else:
																	try:
																								lowercase_					:			int										=       baseaa.urlsafe_baadecode(lowercase_    ).decode("""utf-8"""    )
																								lowercase_					:			str										=       json.loads(lowercase_    )
																	except (UnicodeDecodeError, AttributeError, ValueError):
																								raise ValueError(
																								    f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'''    )

										lowercase_					:			Any										=       config

										self.set_stage_and_offload()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										# zero stage - this is done as early as possible, before model is created, to allow
										# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
										# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
										lowercase_					:			Tuple										=       self.get_value("""zero_optimization.stage"""				,			-1    )

										# offload
										lowercase_					:			str										=       False
										if self.is_zeroa() or self.is_zeroa():
																	lowercase_					:			Dict										=       set(["""cpu""", """nvme"""]    )
																	lowercase_					:			List[Any]										=       set(
																	    [
																	        self.get_value("""zero_optimization.offload_optimizer.device"""    ),
																	        self.get_value("""zero_optimization.offload_param.device"""    ),
																	    ]    )
																	if len(offload_devices & offload_devices_valid    ) > 0:
																								lowercase_					:			Tuple										=       True







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Any    ):
										lowercase_					:			Optional[Any]										=       self.config

										# find the config node of interest if it exists
										lowercase_					:			Tuple										=       ds_key_long.split("""."""    )
										lowercase_					:			Union[str, Any]										=       nodes.pop()
										for node in nodes:
																	lowercase_					:			List[str]										=       config.get(lowercase_    )
																	if config is None:
																								return None, ds_key

										return config, ds_key







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  List[str]				,			lowercase_				:  List[str]=None    ):
										lowercase_						,						lowercase_					:			List[Any]										=       self.find_config_node(lowercase_    )
										if config is None:
																	return default
										return config.get(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[int]				,			lowercase_				:  int=False    ):
										lowercase_					:			int										=       self.config

										# find the config node of interest if it exists
										lowercase_					:			Dict										=       ds_key_long.split("""."""    )
										for node in nodes:
																	lowercase_					:			List[Any]										=       config
																	lowercase_					:			Dict										=       config.get(lowercase_    )
																	if config is None:
																								if must_exist:
																															raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}'''    )
																								else:
																															return

        # if found remove it
										if parent_config is not None:
																	parent_config.pop(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]				,			lowercase_				:  Tuple    ):
										lowercase_					:			str										=       self.get_value(lowercase_    )
										return False if value is None else bool(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Union[str, Any]										=       self.get_value(lowercase_    )
										return False if value is None else not bool(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										return self._stage == 2







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										return self._stage == 3







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return self._offload





class     __magic_name__						:







			def __init__(	self				:  Any				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Any										=       engine







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  int				,			**lowercase_				:  str    ):
										# runs backpropagation and handles mixed precision
										self.engine.backward(lowercase_				,			**lowercase_    )

										# Deepspeed's `engine.step` performs the following operations:
										# - gradient accumulation check
										# - gradient clipping
										# - optimizer step
										# - zero grad
										# - checking overflow
										# - lr_scheduler step (only if engine.lr_scheduler is not None)
										self.engine.step()
										# and this plugin overrides the above calls with no-ops when Accelerate runs under
										# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
										# training loop that works transparently under many training regimes.





class     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  Tuple    ):
										super().__init__(lowercase_				,			device_placement=lowercase_				,			scaler=lowercase_    )
										lowercase_					:			Any										=       hasattr(self.optimizer				,			"""overflow"""    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Tuple=None    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										if self.__has_overflow__:
																	return self.optimizer.overflow
										return False





class     __magic_name__						(    _UpperCAmelCase):







			def __init__(	self				:  Dict				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Tuple    ):
										super().__init__(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





class     __magic_name__						:







			def __init__(	self				:  Any				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  List[str]=0.0_01				,			lowercase_				:  List[str]=0				,			**lowercase_				:  List[Any]    ):
										lowercase_					:			str										=       params
										lowercase_					:			List[Any]										=       lr
										lowercase_					:			int										=       weight_decay
										lowercase_					:			Union[str, Any]										=       kwargs





class     __magic_name__						:







			def __init__(	self				:  Tuple				,			lowercase_				:  Optional[Any]				,			lowercase_				:  List[str]=None				,			lowercase_				:  int=0				,			**lowercase_				:  int    ):
										lowercase_					:			Union[str, Any]										=       optimizer
										lowercase_					:			List[str]										=       total_num_steps
										lowercase_					:			Dict										=       warmup_num_steps
										lowercase_					:			Dict										=       kwargs





