

'''simple docstring'''





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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


A__						=							logging.get_logger(__name__)

A__						=							{
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   List[Any]       				=  "blenderbot-small"
	_lowercase  :   Union[str, Any]       				=  ["past_key_values"]
	_lowercase  :   str       				=  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







	def __init__(   self: List[str]  , __UpperCamelCase: Any=5_02_65  , __UpperCamelCase: Union[str, Any]=5_12  , __UpperCamelCase: List[str]=8  , __UpperCamelCase: Optional[Any]=20_48  , __UpperCamelCase: int=16  , __UpperCamelCase: Union[str, Any]=8  , __UpperCamelCase: Union[str, Any]=20_48  , __UpperCamelCase: Optional[int]=16  , __UpperCamelCase: List[str]=0.0  , __UpperCamelCase: Any=0.0  , __UpperCamelCase: List[str]=True  , __UpperCamelCase: Optional[Any]=True  , __UpperCamelCase: List[str]="gelu"  , __UpperCamelCase: str=5_12  , __UpperCamelCase: List[str]=0.1  , __UpperCamelCase: Optional[int]=0.0  , __UpperCamelCase: List[str]=0.0  , __UpperCamelCase: Any=0.02  , __UpperCamelCase: Any=1  , __UpperCamelCase: Any=False  , __UpperCamelCase: Any=0  , __UpperCamelCase: Optional[int]=1  , __UpperCamelCase: List[str]=2  , __UpperCamelCase: Union[str, Any]=2  , **__UpperCamelCase: Tuple  , ):

						'''simple docstring'''





						__magic_name__							=    vocab_size
						__magic_name__							=    max_position_embeddings
						__magic_name__							=    d_model
						__magic_name__							=    encoder_ffn_dim
						__magic_name__							=    encoder_layers
						__magic_name__							=    encoder_attention_heads
						__magic_name__							=    decoder_ffn_dim
						__magic_name__							=    decoder_layers
						__magic_name__							=    decoder_attention_heads
						__magic_name__							=    dropout
						__magic_name__							=    attention_dropout
						__magic_name__							=    activation_dropout
						__magic_name__							=    activation_function
						__magic_name__							=    init_std
						__magic_name__							=    encoder_layerdrop
						__magic_name__							=    decoder_layerdrop
						__magic_name__							=    use_cache
						__magic_name__							=    encoder_layers
						__magic_name__							=    scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    pad_token_id=__UpperCamelCase  , bos_token_id=__UpperCamelCase  , eos_token_id=__UpperCamelCase  , is_encoder_decoder=__UpperCamelCase  , decoder_start_token_id=__UpperCamelCase  , forced_eos_token_id=__UpperCamelCase  , **__UpperCamelCase  , )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						if self.task in ["default", "seq2seq-lm"]:
											__magic_name__							=    OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
											    ]      )

											if self.use_past:
																__magic_name__							=    {0: 'batch'}
																__magic_name__							=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
											else:
																__magic_name__							=    {0: 'batch', 1: 'decoder_sequence'}
																__magic_name__							=    {0: 'batch', 1: 'decoder_sequence'}

											if self.use_past:
																self.fill_with_past_key_values_(__UpperCamelCase  , direction='inputs'      )
						elif self.task == "causal-lm":
											# TODO: figure this case out.
											__magic_name__							=    OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
											    ]      )
											if self.use_past:
																__magic_name__,	__magic_name__							=    self.num_layers
																for i in range(__UpperCamelCase      ):
																					__magic_name__							=    {0: 'batch', 2: 'past_sequence + sequence'}
																					__magic_name__							=    {0: 'batch', 2: 'past_sequence + sequence'}
						else:
											__magic_name__							=    OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
											        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
											        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
											    ]      )

						return common_inputs







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						if self.task in ["default", "seq2seq-lm"]:
											__magic_name__							=    super().outputs
						else:
											__magic_name__							=    super(__UpperCamelCase  , self      ).outputs
											if self.use_past:
																__magic_name__,	__magic_name__							=    self.num_layers
																for i in range(__UpperCamelCase      ):
																					__magic_name__							=    {0: 'batch', 2: 'past_sequence + sequence'}
																					__magic_name__							=    {0: 'batch', 2: 'past_sequence + sequence'}
						return common_outputs







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: PreTrainedTokenizer  , __UpperCamelCase: int = -1  , __UpperCamelCase: int = -1  , __UpperCamelCase: bool = False  , __UpperCamelCase: Optional[TensorType] = None  , ):

						'''simple docstring'''





						__magic_name__							=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )

						# Generate decoder inputs
						__magic_name__							=    seq_length if not self.use_past else 1
						__magic_name__							=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
						__magic_name__							=    dict(**__UpperCamelCase  , **__UpperCamelCase      )

						if self.use_past:
											if not is_torch_available():
																raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
											else:
																import torch
											__magic_name__,	__magic_name__							=    common_inputs['input_ids'].shape
											__magic_name__							=    common_inputs['decoder_input_ids'].shape[1]
											__magic_name__,	__magic_name__							=    self.num_attention_heads
											__magic_name__							=    (
											    batch,
											    num_encoder_attention_heads,
											    encoder_seq_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)
											__magic_name__							=    decoder_seq_length + 3
											__magic_name__							=    (
											    batch,
											    num_decoder_attention_heads,
											    decoder_past_length,
											    self._config.hidden_size // num_decoder_attention_heads,
											)

											__magic_name__							=    torch.cat(
											    [common_inputs['decoder_attention_mask'], torch.ones(__UpperCamelCase  , __UpperCamelCase      )]  , dim=1      )

											__magic_name__							=    []
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											__magic_name__,	__magic_name__							=    self.num_layers
											__magic_name__							=    min(__UpperCamelCase  , __UpperCamelCase      )
											__magic_name__							=    max(__UpperCamelCase  , __UpperCamelCase      ) - min_num_layers
											__magic_name__							=    'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

											for _ in range(__UpperCamelCase      ):
																common_inputs["past_key_values"].append(
																    (
																        torch.zeros(__UpperCamelCase      ),
																        torch.zeros(__UpperCamelCase      ),
																        torch.zeros(__UpperCamelCase      ),
																        torch.zeros(__UpperCamelCase      ),
																    )      )
											# TODO: test this.
											__magic_name__							=    encoder_shape if remaining_side_name == 'encoder' else decoder_shape
											for _ in range(__UpperCamelCase  , __UpperCamelCase      ):
																common_inputs["past_key_values"].append((torch.zeros(__UpperCamelCase      ), torch.zeros(__UpperCamelCase      ))      )
						return common_inputs







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: PreTrainedTokenizer  , __UpperCamelCase: int = -1  , __UpperCamelCase: int = -1  , __UpperCamelCase: bool = False  , __UpperCamelCase: Optional[TensorType] = None  , ):

						'''simple docstring'''





						__magic_name__							=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )

						if self.use_past:
											if not is_torch_available():
																raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
											else:
																import torch
											__magic_name__,	__magic_name__							=    common_inputs['input_ids'].shape
											# Not using the same length for past_key_values
											__magic_name__							=    seqlen + 2
											__magic_name__,	__magic_name__							=    self.num_layers
											__magic_name__,	__magic_name__							=    self.num_attention_heads
											__magic_name__							=    (
											    batch,
											    num_encoder_attention_heads,
											    past_key_values_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)

											__magic_name__							=    common_inputs['attention_mask'].dtype
											__magic_name__							=    torch.cat(
											    [common_inputs['attention_mask'], torch.ones(__UpperCamelCase  , __UpperCamelCase  , dtype=__UpperCamelCase      )]  , dim=1      )
											__magic_name__							=    [
											    (torch.zeros(__UpperCamelCase      ), torch.zeros(__UpperCamelCase      )) for _ in range(__UpperCamelCase      )
											]
						return common_inputs







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , __UpperCamelCase: PreTrainedTokenizer  , __UpperCamelCase: int = -1  , __UpperCamelCase: int = -1  , __UpperCamelCase: bool = False  , __UpperCamelCase: Optional[TensorType] = None  , ):

						'''simple docstring'''





						__magic_name__							=    compute_effective_axis_dimension(
						    __UpperCamelCase  , 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
						__magic_name__							=    tokenizer.num_special_tokens_to_add(__UpperCamelCase      )
						__magic_name__							=    compute_effective_axis_dimension(
						    __UpperCamelCase  , fixed_dimension=OnnxConfig.default_fixed_sequence  , num_token_to_add=__UpperCamelCase      )

						# Generate dummy inputs according to compute batch and sequence
						__magic_name__							=    [' '.join([tokenizer.unk_token]      ) * seq_length] * batch_size
						__magic_name__							=    dict(tokenizer(__UpperCamelCase  , return_tensors=__UpperCamelCase      )      )
						return common_inputs







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: PreTrainedTokenizer  , __UpperCamelCase: int = -1  , __UpperCamelCase: int = -1  , __UpperCamelCase: bool = False  , __UpperCamelCase: Optional[TensorType] = None  , ):

						'''simple docstring'''





						if self.task in ["default", "seq2seq-lm"]:
											__magic_name__							=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    __UpperCamelCase  , batch_size=__UpperCamelCase  , seq_length=__UpperCamelCase  , is_pair=__UpperCamelCase  , framework=__UpperCamelCase      )

						elif self.task == "causal-lm":
											__magic_name__							=    self._generate_dummy_inputs_for_causal_lm(
											    __UpperCamelCase  , batch_size=__UpperCamelCase  , seq_length=__UpperCamelCase  , is_pair=__UpperCamelCase  , framework=__UpperCamelCase      )
						else:
											__magic_name__							=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __UpperCamelCase  , batch_size=__UpperCamelCase  , seq_length=__UpperCamelCase  , is_pair=__UpperCamelCase  , framework=__UpperCamelCase      )

						return common_inputs







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: List[str]  , __UpperCamelCase: str  , __UpperCamelCase: int  , __UpperCamelCase: str      ):

						'''simple docstring'''





						if self.task in ["default", "seq2seq-lm"]:
											__magic_name__							=    super()._flatten_past_key_values_(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )
						else:
											__magic_name__							=    super(__UpperCamelCase  , self      )._flatten_past_key_values_(
											    __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )







import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler

@dataclass
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   int       				=  0
	_lowercase  :   bool       				=  False
	_lowercase  :   float       				=  3.0




class 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						self.assertDictEqual(MockClass().to_kwargs()  , {}      )
						self.assertDictEqual(MockClass(a=2      ).to_kwargs()  , {'a': 2}      )
						self.assertDictEqual(MockClass(a=2  , b=__UpperCamelCase      ).to_kwargs()  , {'a': 2, 'b': True}      )
						self.assertDictEqual(MockClass(a=2  , c=2.25      ).to_kwargs()  , {'a': 2, 'c': 2.25}      )







	@require_cuda
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    GradScalerKwargs(init_scale=10_24  , growth_factor=2      )
						AcceleratorState._reset_state()
						__magic_name__							=    Accelerator(mixed_precision='fp16'  , kwargs_handlers=[scaler_handler]      )
						print(accelerator.use_fpaa      )
						__magic_name__							=    accelerator.scaler

						# Check the kwargs have been applied
						self.assertEqual(scaler._init_scale  , 1024.0      )
						self.assertEqual(scaler._growth_factor  , 2.0      )

						# Check the other values are at the default
						self.assertEqual(scaler._backoff_factor  , 0.5      )
						self.assertEqual(scaler._growth_interval  , 20_00      )
						self.assertEqual(scaler._enabled  , __UpperCamelCase      )







	@require_multi_gpu
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__      )]
						execute_subprocess_async(__UpperCamelCase  , env=os.environ.copy()      )


if __name__ == "__main__":
						A__						=							DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						A__						=							Accelerator(kwargs_handlers=[ddp_scaler])
						A__						=							torch.nn.Linear(100, 200)
						A__						=							accelerator.prepare(model)

						# Check the values changed in kwargs
						A__						=							""
						A__						=							model.bucket_bytes_cap // (1024 * 1024)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)




