
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]=None			,				UpperCAmelCase__				:      Optional[int]=None       )  ->    Any:
							return field(default_factory=lambda: default			,				metadata=__lowercase       )





@dataclass
class       __A:
					SCREAMING_SNAKE_CASE			    =    field(
					    metadata={'''help''': '''The csv file to plot.'''}     ,		)
					SCREAMING_SNAKE_CASE			    =    field(
					    default=__A     ,		metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}     ,		)
					SCREAMING_SNAKE_CASE			    =    field(
					    default=__A     ,		metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}     ,		)
					SCREAMING_SNAKE_CASE			    =    field(
					    default=__A     ,		metadata={'''help''': '''Disable logarithmic scale when plotting'''}     ,		)
					SCREAMING_SNAKE_CASE			    =    field(
					    default=__A     ,		metadata={
					        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
					    }     ,		)
					SCREAMING_SNAKE_CASE			    =    field(
					    default=__A     ,		metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}     ,		)
					SCREAMING_SNAKE_CASE			    =    list_field(
					    default=__A     ,		metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict       )  ->    List[str]:




							try:
														int(__lowercase       )
														return True
							except ValueError:
														return False



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]       )  ->    Tuple:




							try:
														float(__lowercase       )
														return True
							except ValueError:
														return False





class       __A:

					def __init__(	self					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ):
												lowerCamelCase_  =					args
												lowerCamelCase_  =					defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}   )

												with open(self.args.csv_file   ,					newline=""""""   ) as csv_file:
																			lowerCamelCase_  =					csv.DictReader(__UpperCamelCase   )
																			for row in reader:
																										lowerCamelCase_  =					row['model']
																										self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]   )   )
																										self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]   )   )
																										if can_convert_to_int(row["""result"""]   ):
																																	# value is not None
																																	lowerCamelCase_  =					int(row["""result"""]   )
																										elif can_convert_to_float(row["""result"""]   ):
																																	# value is not None
																																	lowerCamelCase_  =					float(row["""result"""]   )



					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_  =					plt.subplots()
												lowerCamelCase_  =					'Time usage' if self.args.is_time else 'Memory usage'
												lowerCamelCase_  =					title_str + ' for training' if self.args.is_train else title_str + ' for inference'

												if not self.args.no_log_scale:
																			# set logarithm scales
																			ax.set_xscale("""log"""   )
																			ax.set_yscale("""log"""   )

												for axis in [ax.xaxis, ax.yaxis]:
																			axis.set_major_formatter(ScalarFormatter()   )

												for model_name_idx, model_name in enumerate(self.result_dict.keys()   ):
																			lowerCamelCase_  =					sorted(set(self.result_dict[model_name]["""bsz"""]   )   )
																			lowerCamelCase_  =					sorted(set(self.result_dict[model_name]["""seq_len"""]   )   )
																			lowerCamelCase_  =					self.result_dict[model_name]['result']

																			(lowerCamelCase_)  =					(
																			    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																			)

																			lowerCamelCase_  =					(
																			    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
																			)

																			for inner_loop_value in inner_loop_array:
																										if self.args.plot_along_batch:
																																	lowerCamelCase_  =					np.asarray(
																																	    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]   ,					dtype=__UpperCamelCase   ,					)
																										else:
																																	lowerCamelCase_  =					np.asarray(
																																	    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]   ,					dtype=np.floataa   ,					)

																										(lowerCamelCase_)  =					(
																										    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
																										)

																										lowerCamelCase_  =					np.asarray(__UpperCamelCase   ,					__UpperCamelCase   )[: len(__UpperCamelCase   )]
																										plt.scatter(
																										    __UpperCamelCase   ,					__UpperCamelCase   ,					label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''   )
																										plt.plot(__UpperCamelCase   ,					__UpperCamelCase   ,					"""--"""   )

																			title_str += F''' {label_model_name} vs.'''

												lowerCamelCase_  =					title_str[:-4]
												lowerCamelCase_  =					'Time in s' if self.args.is_time else 'Memory in MB'

												# plot
												plt.title(__UpperCamelCase   )
												plt.xlabel(__UpperCamelCase   )
												plt.ylabel(__UpperCamelCase   )
												plt.legend()

												if self.args.figure_png_file is not None:
																			plt.savefig(self.args.figure_png_file   )
												else:
																			plt.show()



def      __lowerCAmelCase						(							)  ->    Optional[Any]:
							lowerCamelCase_  =					HfArgumentParser(__lowercase       )
							lowerCamelCase_  =					parser.parse_args_into_dataclasses()[0]
							lowerCamelCase_  =					Plot(args=__lowercase       )
							plot.plot()


if __name__ == "__main__":
		main()







import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
		import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase        =  logging.get_logger(__name__)

lowercase        =  {'''vocab_file''': '''vocab.txt'''}

lowercase        =  {
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

lowercase        =  {
    '''openbmb/cpm-ant-10b''': 1_0_2_4,
}



def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[str]       )  ->    Dict:
							lowerCamelCase_							  =					collections.OrderedDict()
							with open(UpperCAmelCase__			,				"""r"""			,				encoding="""utf-8"""       ) as reader:
														lowerCamelCase_							  =					reader.readlines()
							for index, token in enumerate(UpperCAmelCase__       ):
														lowerCamelCase_							  =					token.rstrip("""\n"""       )
														lowerCamelCase_							  =					index
							return vocab





class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     Optional[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     List[str]="<unk>"   ,					__UpperCamelCase					:     List[str]=2_0_0   ):
												lowerCamelCase_							  =					vocab
												lowerCamelCase_							  =					unk_token
												lowerCamelCase_							  =					max_input_chars_per_word



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												if len(__UpperCamelCase   ) > self.max_input_chars_per_word:
																			return [self.unk_token]

												lowerCamelCase_							  =					0
												lowerCamelCase_							  =					[]
												while start < len(__UpperCamelCase   ):
																			lowerCamelCase_							  =					len(__UpperCamelCase   )
																			lowerCamelCase_							  =					None
																			while start < end:
																										lowerCamelCase_							  =					"""""".join(chars[start:end]   )
																										if substr in self.vocab:
																																	lowerCamelCase_							  =					substr
																																	break
																										end -= 1
																			if cur_substr is None:
																										sub_tokens.append(self.unk_token   )
																										start += 1
																			else:
																										sub_tokens.append(__UpperCamelCase   )
																										lowerCamelCase_							  =					end

												return sub_tokens





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE			    =    ['''input_ids''', '''attention_mask''']
					SCREAMING_SNAKE_CASE			    =    False

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Optional[int]="<d>"   ,					__UpperCamelCase					:     List[str]="</d>"   ,					__UpperCamelCase					:     Optional[Any]="<s>"   ,					__UpperCamelCase					:     Union[str, Any]="</s>"   ,					__UpperCamelCase					:     List[str]="<pad>"   ,					__UpperCamelCase					:     Union[str, Any]="<unk>"   ,					__UpperCamelCase					:     List[Any]="</n>"   ,					__UpperCamelCase					:     Tuple="</_>"   ,					__UpperCamelCase					:     Optional[Any]="left"   ,					**__UpperCamelCase					:     List[str]   ,					):
												requires_backends(self   ,					["""jieba"""]   )
												super().__init__(
												    bod_token=__UpperCamelCase   ,					eod_token=__UpperCamelCase   ,					bos_token=__UpperCamelCase   ,					eos_token=__UpperCamelCase   ,					pad_token=__UpperCamelCase   ,					unk_token=__UpperCamelCase   ,					line_token=__UpperCamelCase   ,					space_token=__UpperCamelCase   ,					padding_side=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
												lowerCamelCase_							  =					bod_token
												lowerCamelCase_							  =					eod_token
												lowerCamelCase_							  =					load_vocab(__UpperCamelCase   )
												lowerCamelCase_							  =					self.encoder[space_token]
												lowerCamelCase_							  =					self.encoder[line_token]

												del self.encoder[space_token]
												del self.encoder[line_token]

												lowerCamelCase_							  =					collections.OrderedDict(sorted(self.encoder.items()   ,					key=lambda __UpperCamelCase   : x[1]   )   )
												lowerCamelCase_							  =					{v: k for k, v in self.encoder.items()}

												lowerCamelCase_							  =					WordpieceTokenizer(vocab=self.encoder   ,					unk_token=self.unk_token   )

					@property
					def    lowercase__      (	self					:     Any   ):
												return self.encoder[self.bod_token]

					@property
					def    lowercase__      (	self					:     Any   ):
												return self.encoder[self.eod_token]

					@property
					def    lowercase__      (	self					:     int   ):
												return self.encoder["\n"]

					@property
					def    lowercase__      (	self					:     str   ):
												return len(self.encoder   )

					def    lowercase__      (	self					:     Any   ):
												return dict(self.encoder   ,					**self.added_tokens_encoder   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ):
												lowerCamelCase_							  =					[]
												for x in jieba.cut(__UpperCamelCase   ,					cut_all=__UpperCamelCase   ):
																			output_tokens.extend(self.wordpiece_tokenizer.tokenize(__UpperCamelCase   )   )
												return output_tokens

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     Dict   ,					**__UpperCamelCase					:     str   ):
												lowerCamelCase_							  =					[i for i in token_ids if i >= 0]
												lowerCamelCase_							  =					[
												    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
												]
												return super()._decode(__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Optional[Any]   ):
												return token in self.encoder

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     List[str]   ):
												return "".join(__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     int   ):
												return self.encoder.get(__UpperCamelCase   ,					self.encoder.get(self.unk_token   )   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Union[str, Any]   ):
												return self.decoder.get(__UpperCamelCase   ,					self.unk_token   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[str] = None   ):
												if os.path.isdir(__UpperCamelCase   ):
																			lowerCamelCase_							  =					os.path.join(
																			    __UpperCamelCase   ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
												else:
																			lowerCamelCase_							  =					(filename_prefix + """-""" if filename_prefix else """""") + save_directory
												lowerCamelCase_							  =					0
												if " " in self.encoder:
																			lowerCamelCase_							  =					self.encoder[""" """]
																			del self.encoder[" "]
												if "\n" in self.encoder:
																			lowerCamelCase_							  =					self.encoder["""\n"""]
																			del self.encoder["\n"]
												lowerCamelCase_							  =					collections.OrderedDict(sorted(self.encoder.items()   ,					key=lambda __UpperCamelCase   : x[1]   )   )
												with open(__UpperCamelCase   ,					"""w"""   ,					encoding="""utf-8"""   ) as writer:
																			for token, token_index in self.encoder.items():
																										if index != token_index:
																																	logger.warning(
																																	    F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																																	    """ Please check that the vocabulary is not corrupted!"""   )
																																	lowerCamelCase_							  =					token_index
																										writer.write(token + """\n"""   )
																										index += 1
												return (vocab_file,)

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     List[int] = None   ):
												if token_ids_a is None:
																			return [self.bos_token_id] + token_ids_a
												return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     Optional[List[int]] = None   ,					__UpperCamelCase					:     bool = False   ):

												if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=__UpperCamelCase   ,					token_ids_a=__UpperCamelCase   ,					already_has_special_tokens=__UpperCamelCase   )

												if token_ids_a is not None:
																			return [1] + ([0] * len(__UpperCamelCase   )) + [1] + ([0] * len(__UpperCamelCase   ))
												return [1] + ([0] * len(__UpperCamelCase   ))





