
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


_UpperCAmelCase   :					List[Any]    =			logging.get_logger(__name__)

_UpperCAmelCase   :					str    =			{
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}


class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			Optional[Any]      	=							"blenderbot"
		__lowercase			:			Optional[Any]      	=							["past_key_values"]
		__lowercase			:			Union[str, Any]      	=							{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



		def __init__(					self							, A_=8_008							, A_=128							, A_=2							, A_=10_240							, A_=32							, A_=24							, A_=10_240							, A_=32							, A_=0.0							, A_=0.0							, A_=True							, A_=True							, A_="gelu"							, A_=2_560							, A_=0.1							, A_=0.0							, A_=0.0							, A_=0.02							, A_=1							, A_=False							, A_=0							, A_=1							, A_=2							, A_=3							, A_=2							, **A_							, )    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	vocab_size
				UpperCamelCase            =	max_position_embeddings
				UpperCamelCase            =	d_model
				UpperCamelCase            =	encoder_ffn_dim
				UpperCamelCase            =	encoder_layers
				UpperCamelCase            =	encoder_attention_heads
				UpperCamelCase            =	decoder_ffn_dim
				UpperCamelCase            =	decoder_layers
				UpperCamelCase            =	decoder_attention_heads
				UpperCamelCase            =	dropout
				UpperCamelCase            =	attention_dropout
				UpperCamelCase            =	activation_dropout
				UpperCamelCase            =	activation_function
				UpperCamelCase            =	init_std
				UpperCamelCase            =	encoder_layerdrop
				UpperCamelCase            =	decoder_layerdrop
				UpperCamelCase            =	use_cache
				UpperCamelCase            =	encoder_layers
				UpperCamelCase            =	scale_embedding  # scale factor will be sqrt(d_model) if True

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






class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):



		@property
		def __UpperCamelCase					(					self			)    ->   Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase            =	OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ]			)
						if self.use_past:
								UpperCamelCase            =	{0: 'batch'}
								UpperCamelCase            =	{0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
								UpperCamelCase            =	{0: 'batch', 1: 'decoder_sequence'}
								UpperCamelCase            =	{0: 'batch', 1: 'decoder_sequence'}
						if self.use_past:
								self.fill_with_past_key_values_(A_							, direction='inputs'			)
				elif self.task == "causal-lm":
						UpperCamelCase            =	OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ]			)
						if self.use_past:
								UpperCamelCase      ,    UpperCamelCase            =	self.num_layers
								for i in range(A_			):
										UpperCamelCase            =	{0: 'batch', 2: 'past_sequence + sequence'}
										UpperCamelCase            =	{0: 'batch', 2: 'past_sequence + sequence'}
				else:
						UpperCamelCase            =	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
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
		def __UpperCamelCase					(					self			)    ->   Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase            =	super().outputs
				else:
						UpperCamelCase            =	super(A_							, self			).outputs
						if self.use_past:
								UpperCamelCase      ,    UpperCamelCase            =	self.num_layers
								for i in range(A_			):
										UpperCamelCase            =	{0: 'batch', 2: 'past_sequence + sequence'}
										UpperCamelCase            =	{0: 'batch', 2: 'past_sequence + sequence'}
				return common_outputs



		def __UpperCamelCase					(					self							, A_							, A_ = -1							, A_ = -1							, A_ = False							, A_ = None							, )    ->   Mapping[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    A_							, A_							, A_							, A_							, A_			)
				# Generate decoder inputs
				UpperCamelCase            =	seq_length if not self.use_past else 1
				UpperCamelCase            =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    A_							, A_							, A_							, A_							, A_			)
				UpperCamelCase            =	{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
				UpperCamelCase            =	dict(**A_							, **A_			)

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

						for _ in range(A_			):
								common_inputs["past_key_values"].append(
								    (
								        torch.zeros(A_			),
								        torch.zeros(A_			),
								        torch.zeros(A_			),
								        torch.zeros(A_			),
								    )			)
				return common_inputs



		def __UpperCamelCase					(					self							, A_							, A_ = -1							, A_ = -1							, A_ = False							, A_ = None							, )    ->   Mapping[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    A_							, A_							, A_							, A_							, A_			)

				if self.use_past:
						if not is_torch_available():
								raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'			)
						else:
								import torch
						UpperCamelCase      ,    UpperCamelCase            =	common_inputs['input_ids'].shape
						UpperCamelCase            =	seqlen
						UpperCamelCase      ,    UpperCamelCase            =	self.num_layers
						UpperCamelCase      ,    UpperCamelCase            =	self.num_attention_heads
						UpperCamelCase            =	(
						    batch,
						    num_encoder_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)
						UpperCamelCase            =	common_inputs['attention_mask'].dtype
						UpperCamelCase            =	torch.cat(
						    [common_inputs['attention_mask'], torch.ones(A_							, A_							, dtype=A_			)]							, dim=1			)
						UpperCamelCase            =	[
						    (torch.zeros(A_			), torch.zeros(A_			)) for _ in range(A_			)
						]
				return common_inputs



		def __UpperCamelCase					(					self							, A_							, A_ = -1							, A_ = -1							, A_ = False							, A_ = None							, )    ->   Mapping[str, Any]:
				"""simple docstring"""
				# Copied from OnnxConfig.generate_dummy_inputs
				# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				UpperCamelCase            =	compute_effective_axis_dimension(
				    A_							, 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
				UpperCamelCase            =	tokenizer.num_special_tokens_to_add(A_			)
				UpperCamelCase            =	compute_effective_axis_dimension(
				    A_							, fixed_dimension=OnnxConfig.default_fixed_sequence							, num_token_to_add=A_			)

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



		def __UpperCamelCase					(					self							, A_							, A_ = -1							, A_ = -1							, A_ = False							, A_ = None							, )    ->   Mapping[str, Any]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase            =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
						    A_							, batch_size=A_							, seq_length=A_							, is_pair=A_							, framework=A_			)

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

				return common_inputs



		def __UpperCamelCase					(					self							, A_							, A_							, A_							, A_			)    ->   int:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase            =	super()._flatten_past_key_values_(A_							, A_							, A_							, A_			)
				else:
						UpperCamelCase            =	super(A_							, self			)._flatten_past_key_values_(
						    A_							, A_							, A_							, A_			)



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   Dict:
				"""simple docstring"""
				if direction not in ["inputs", "outputs"]:
						raise ValueError(F'''direction must either be "inputs" or "outputs", but {direction} was given'''			)

				UpperCamelCase            =	'past_key_values' if direction == 'inputs' else 'present'
				UpperCamelCase      ,    UpperCamelCase            =	self.num_layers

				UpperCamelCase            =	'past_encoder_sequence'
				UpperCamelCase            =	'past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

				for i in range(A_			):
						UpperCamelCase            =	{0: 'batch', 2: decoder_sequence}
						UpperCamelCase            =	{0: 'batch', 2: decoder_sequence}
						UpperCamelCase            =	{0: 'batch', 2: encoder_sequence}
						UpperCamelCase            =	{0: 'batch', 2: encoder_sequence}


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_UpperCAmelCase   :					str    =			100_000_000_000
_UpperCAmelCase   :					Optional[int]    =			10_000

_UpperCAmelCase  ,_UpperCAmelCase   :					Optional[int]    =			os.path.split(__file__)
_UpperCAmelCase   :					Optional[int]    =			os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


def  A			(   lowercase       ,			lowercase					)  ->    datasets.Dataset:
		'''simple docstring'''

		UpperCamelCase            =	pa.Table.from_pydict({'col': [0] * chunk_size}					)
		UpperCamelCase            =	pa.concat_tables([table] * (num_examples // chunk_size)					)
		return datasets.Dataset(lowercase       ,			fingerprint='table_100B'					)


@dataclass
class 							lowercase   :
		__lowercase			:			int
		__lowercase			:			int
		__lowercase			:			int
		__lowercase			:			int



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	np.random.default_rng(self.seed			)
				UpperCamelCase            =	rng.integers(low=self.low							, high=self.high							, size=self.size			).tolist()



		def __iter__(					self			)    ->   List[str]:
				"""simple docstring"""
				return iter(self._sampled_values			)



		def __len__(					self			)    ->   int:
				"""simple docstring"""
				return self.size






@get_duration
def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	dataset[0]


@get_duration
def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	dataset[-1]


@get_duration
def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	dataset[range(len(lowercase					) // 2       ,			len(lowercase					) // 2 + 1_024					)]


@get_duration
def  A			(   lowercase					)  ->    Any:
		'''simple docstring'''

		UpperCamelCase            =	dataset[RandIter(0       ,			len(lowercase					)       ,			1_024       ,			seed=42					)]


def  A			(   )  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	{'num examples': SPEED_TEST_N_EXAMPLES}
		UpperCamelCase            =	(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
		print('generating dataset'					)
		UpperCamelCase            =	generate_100B_dataset(num_examples=lowercase       ,			chunk_size=lowercase					)
		print('Functions'					)
		for func in functions:
				print(func.__name__					)
				UpperCamelCase            =	func(lowercase					)

		with open(lowercase       ,			'wb'					) as f:
				f.write(json.dumps(lowercase					).encode('utf-8'					)					)


if __name__ == "__main__":  # useful to run the profiler
					benchmark_table_100B()
