
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        a_		(       __lowercase   :	List[Any]=None       ,					__lowercase   :	List[Any]=None ) ->  Tuple:
			return field(default_factory=lambda: default       ,					metadata=__lowercase )




@dataclass
class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



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






def        a_		(       __lowercase   :	Tuple ) ->  Optional[int]:
			try:
						int(__lowercase )
						return True
			except ValueError:
						return False






def        a_		(       __lowercase   :	Tuple ) ->  List[str]:
			try:
						float(__lowercase )
						return True
			except ValueError:
						return False




class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    Dict			,  lowercase	:    Dict	):


				'''simple docstring'''


				_snake_case		=			args
				_snake_case		=			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

				with open(self.args.csv_file			,  newline=''	) as csv_file:
							_snake_case		=			csv.DictReader(lowercase	)
							for row in reader:
										_snake_case		=			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
													_snake_case		=			int(row['result']	)
										elif can_convert_to_float(row['result']	):
													# value is not None
													_snake_case		=			float(row['result']	)





	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			plt.subplots()
				_snake_case		=			'Time usage' if self.args.is_time else 'Memory usage'
				_snake_case		=			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()	):
							_snake_case		=			sorted(set(self.result_dict[model_name]['bsz']	)	)
							_snake_case		=			sorted(set(self.result_dict[model_name]['seq_len']	)	)
							_snake_case		=			self.result_dict[model_name]['result']

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

							_snake_case		=			(
							    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:
													_snake_case		=			np.asarray(
													    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]			,  dtype=lowercase			,  )
										else:
													_snake_case		=			np.asarray(
													    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]			,  dtype=np.floataa			,  )

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

										_snake_case		=			np.asarray(lowercase			,  lowercase	)[: len(lowercase	)]
										plt.scatter(
										    lowercase			,  lowercase			,  label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''	)
										plt.plot(lowercase			,  lowercase			,  '--'	)

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

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

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

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






def        a_		(       ) ->  Dict:
			_snake_case		=			HfArgumentParser(__lowercase )
			_snake_case		=			parser.parse_args_into_dataclasses()[0]
			_snake_case		=			Plot(args=__lowercase )
			plot.plot()


if __name__ == "__main__":
			main()

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_pegasus import PegasusTokenizer
else:
			_lowerCamelCase       :				int       =			None


_lowerCamelCase       :				List[str]       =			logging.get_logger(__name__)


_lowerCamelCase       :				Tuple       =			'''▁'''

_lowerCamelCase       :				Optional[Any]       =			{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase       :				Any       =			{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

_lowerCamelCase       :				Optional[int]       =			{
    '''google/pegasus-xsum''': 512,
}




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		int       =					VOCAB_FILES_NAMES
	_UpperCAmelCase       :		Union[str, Any]       =					PRETRAINED_VOCAB_FILES_MAP
	_UpperCAmelCase       :		Union[str, Any]       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCAmelCase       :		Any       =					PegasusTokenizer
	_UpperCAmelCase       :		Dict       =					["input_ids", "attention_mask"]



	def __init__(  self	:    Tuple			,  lowercase	:    str=None			,  lowercase	:    Any=None			,  lowercase	:    List[Any]="<pad>"			,  lowercase	:    List[Any]="</s>"			,  lowercase	:    Tuple="<unk>"			,  lowercase	:    Any="<mask_2>"			,  lowercase	:    List[str]="<mask_1>"			,  lowercase	:    List[Any]=None			,  lowercase	:    Dict=103			,  **lowercase	:    Optional[Any]			,  ):


				'''simple docstring'''


				_snake_case		=			offset

				if additional_special_tokens is not None:
							if not isinstance(lowercase			,  lowercase	):
										raise TypeError(
										    f'''additional_special_tokens should be of type {type(lowercase	)}, but is'''
										    f''' {type(lowercase	)}'''	)

							_snake_case		=			(
							    ([mask_token_sent] + additional_special_tokens)
							    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
							    else additional_special_tokens
							)
							# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
							additional_special_tokens_extended += [
							    f'''<unk_{i}>''' for i in range(len(lowercase	)			,  self.offset - 1	)
							]

							if len(set(lowercase	)	) != len(lowercase	):
										raise ValueError(
										    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
										    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''	)
							_snake_case		=			additional_special_tokens_extended
				else:
							_snake_case		=			[mask_token_sent] if mask_token_sent is not None else []
							additional_special_tokens += [f'''<unk_{i}>''' for i in range(2			,  self.offset	)]

				super().__init__(
				    lowercase			,  tokenizer_file=lowercase			,  pad_token=lowercase			,  eos_token=lowercase			,  unk_token=lowercase			,  mask_token=lowercase			,  mask_token_sent=lowercase			,  offset=lowercase			,  additional_special_tokens=lowercase			,  **lowercase			,  )
				_snake_case		=			vocab_file
				_snake_case		=			False if not self.vocab_file else True



	def  A							(  self	:    List[str]			,  lowercase	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			set(self.all_special_ids	)  # call it once instead of inside list comp
				all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

				if all_special_ids != set(range(len(self.additional_special_tokens	) + 3	)	):
							raise ValueError(
							    'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
							    f''' {len(self.additional_special_tokens	)} additional_special_tokens, but got {all_special_ids}'''	)

				return [1 if x in all_special_ids else 0 for x in seq]



	def  A							(  self	:    List[Any]			,  lowercase	:    List			,  lowercase	:    Optional[List] = None			,  lowercase	:    bool = False	):


				'''simple docstring'''


				if already_has_special_tokens:
							return self._special_token_mask(lowercase	)
				elif token_ids_a is None:
							return self._special_token_mask(lowercase	) + [1]
				else:
							return self._special_token_mask(token_ids_a + token_ids_a	) + [1]



	def  A							(  self	:    Any			,  lowercase	:    Tuple			,  lowercase	:    Any=None	):


				'''simple docstring'''


				if token_ids_a is None:
							return token_ids_a + [self.eos_token_id]
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return token_ids_a + token_ids_a + [self.eos_token_id]





	def  A							(  self	:    int			,  lowercase	:    str			,  lowercase	:    Optional[str] = None	):


				'''simple docstring'''


				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
							    'tokenizer.'	)

				if not os.path.isdir(lowercase	):
							logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
							return
				_snake_case		=			os.path.join(
				    lowercase			,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

				if os.path.abspath(self.vocab_file	) != os.path.abspath(lowercase	):
							copyfile(self.vocab_file			,  lowercase	)

				return (out_vocab_file,)