



'''simple docstring'''






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__       (						_A=None ,			_A=None				):
  return field(default_factory=lambda: default ,			metadata=_A				)

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





  try:
    int(_A				)
    return True
  except ValueError:
    return False
def       lowerCamelCase__       (						_A				):





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

class 							a__:







    def __init__(      self :  List[str]						,			__snake_case :  Tuple	):
      a						:					Optional[int]        =     args
      a						:					str        =     defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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







    def 			lowercase_       (      self :  str	):
      a   ,	a						:					Dict        =     plt.subplots()
      a						:					List[Any]        =     'Time usage' if self.args.is_time else 'Memory usage'
      a						:					Optional[int]        =     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()	):
        a						:					List[str]        =     sorted(set(self.result_dict[model_name]['bsz']	)	)
        a						:					Optional[Any]        =     sorted(set(self.result_dict[model_name]['seq_len']	)	)
        a						:					int        =     self.result_dict[model_name]['result']

        ((a)   ,	(a))						:					Optional[int]        =     (
            (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
        )

        a						:					Dict        =     (
            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:
            a						:					Optional[int]        =     np.asarray(
                [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]						,			dtype=__lowercase						,			)
          else:
            a						:					str        =     np.asarray(
                [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]						,			dtype=np.floataa						,			)

          ((a)   ,	(a))						:					Optional[int]        =     (
              ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
          )

          a						:					Optional[int]        =     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."""

      a						:					Optional[int]        =     title_str[:-4]
      a						:					Union[str, Any]        =     '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       lowerCamelCase__       (						):
  a						:					Any        =     HfArgumentParser(_A				)
  a						:					Dict        =     parser.parse_args_into_dataclasses()[0]
  a						:					Dict        =     Plot(args=_A				)
  plot.plot()


if __name__ == "__main__":
     main()





import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 		UpperCAmelCase   (				UpperCAmelCase__       ,   unittest.TestCase			):







						'''simple docstring'''





						lowerCAmelCase_     		=   CodeGenTokenizer
						lowerCAmelCase_     		=   CodeGenTokenizerFast
						lowerCAmelCase_     		=   True
						lowerCAmelCase_     		=   {'''add_prefix_space''': True}
						lowerCAmelCase_     		=   False






						def        snake_case__						(						self							:     List[Any]						):






							"""simple docstring"""






							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							snake_case_		    =     [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							    "<|endoftext|>",
							]
							snake_case_		    =     dict(zip(__lowercase       ,	range(len(__lowercase						)						)						)						)
							snake_case_		    =     ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							snake_case_		    =     {"unk_token": "<unk>"}

							snake_case_		    =     os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES["vocab_file"]						)
							snake_case_		    =     os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES["merges_file"]						)
							with open(self.vocab_file       ,	"w"       ,	encoding="utf-8"						) as fp:
								fp.write(json.dumps(__lowercase						) + "\n"						)
							with open(self.merges_file       ,	"w"       ,	encoding="utf-8"						) as fp:
								fp.write("\n".join(__lowercase						)						)






						def        snake_case__						(						self							:     Union[str, Any]       ,	**__lowercase							:     List[str]						):






							"""simple docstring"""






							kwargs.update(self.special_tokens_map						)
							return CodeGenTokenizer.from_pretrained(self.tmpdirname       ,	**__lowercase						)






						def        snake_case__						(						self							:     Optional[Any]       ,	**__lowercase							:     Union[str, Any]						):






							"""simple docstring"""






							kwargs.update(self.special_tokens_map						)
							return CodeGenTokenizerFast.from_pretrained(self.tmpdirname       ,	**__lowercase						)






						def        snake_case__						(						self							:     Optional[int]       ,	__lowercase							:     List[str]						):






							"""simple docstring"""






							snake_case_		    =     "lower newer"
							snake_case_		    =     "lower newer"
							return input_text, output_text






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     CodeGenTokenizer(self.vocab_file       ,	self.merges_file       ,	**self.special_tokens_map						)
							snake_case_		    =     "lower newer"
							snake_case_		    =     ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
							snake_case_		    =     tokenizer.tokenize(__lowercase       ,	add_prefix_space=__lowercase						)
							self.assertListEqual(__lowercase       ,	__lowercase						)

							snake_case_		    =     tokens + [tokenizer.unk_token]
							snake_case_		    =     [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase						)       ,	__lowercase						)






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							if not self.test_rust_tokenizer:
								return

							snake_case_		    =     self.get_tokenizer()
							snake_case_		    =     self.get_rust_tokenizer(add_prefix_space=__lowercase						)

							snake_case_		    =     "lower newer"

							# Testing tokenization
							snake_case_		    =     tokenizer.tokenize(__lowercase       ,	add_prefix_space=__lowercase						)
							snake_case_		    =     rust_tokenizer.tokenize(__lowercase						)
							self.assertListEqual(__lowercase       ,	__lowercase						)

							# Testing conversion to ids without special tokens
							snake_case_		    =     tokenizer.encode(__lowercase       ,	add_special_tokens=__lowercase       ,	add_prefix_space=__lowercase						)
							snake_case_		    =     rust_tokenizer.encode(__lowercase       ,	add_special_tokens=__lowercase						)
							self.assertListEqual(__lowercase       ,	__lowercase						)

							# Testing conversion to ids with special tokens
							snake_case_		    =     self.get_rust_tokenizer(add_prefix_space=__lowercase						)
							snake_case_		    =     tokenizer.encode(__lowercase       ,	add_prefix_space=__lowercase						)
							snake_case_		    =     rust_tokenizer.encode(__lowercase						)
							self.assertListEqual(__lowercase       ,	__lowercase						)

							# Testing the unknown token
							snake_case_		    =     tokens + [rust_tokenizer.unk_token]
							snake_case_		    =     [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowercase						)       ,	__lowercase						)






						def        snake_case__						(						self							:     Any       ,	*__lowercase							:     Union[str, Any]       ,	**__lowercase							:     Tuple						):






							"""simple docstring"""






							pass






						def        snake_case__						(						self							:     int       ,	__lowercase							:     str=15						):






							"""simple docstring"""






							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"						):
									snake_case_		    =     self.rust_tokenizer_class.from_pretrained(__lowercase       ,	**__lowercase						)

									# Simple input
									snake_case_		    =     "This is a simple input"
									snake_case_		    =     ["This is a simple input 1", "This is a simple input 2"]
									snake_case_		    =     ("This is a simple input", "This is a pair")
									snake_case_		    =     [
									    ("This is a simple input 1", "This is a simple input 2"),
									    ("This is a simple pair 1", "This is a simple pair 2"),
									]

									# Simple input tests
									self.assertRaises(__lowercase       ,	tokenizer_r.encode       ,	__lowercase       ,	max_length=__lowercase       ,	padding="max_length"						)

									# Simple input
									self.assertRaises(__lowercase       ,	tokenizer_r.encode_plus       ,	__lowercase       ,	max_length=__lowercase       ,	padding="max_length"						)

									# Simple input
									self.assertRaises(
									    __lowercase       ,	tokenizer_r.batch_encode_plus       ,	__lowercase       ,	max_length=__lowercase       ,	padding="max_length"       ,	)

									# Pair input
									self.assertRaises(__lowercase       ,	tokenizer_r.encode       ,	__lowercase       ,	max_length=__lowercase       ,	padding="max_length"						)

									# Pair input
									self.assertRaises(__lowercase       ,	tokenizer_r.encode_plus       ,	__lowercase       ,	max_length=__lowercase       ,	padding="max_length"						)

									# Pair input
									self.assertRaises(
									    __lowercase       ,	tokenizer_r.batch_encode_plus       ,	__lowercase       ,	max_length=__lowercase       ,	padding="max_length"       ,	)






						def        snake_case__						(						self							:     str						):






							"""simple docstring"""






							snake_case_		    =     CodeGenTokenizer.from_pretrained(self.tmpdirname       ,	pad_token="<pad>"						)

							# Simple input
							snake_case_		    =     "This is a simple input"
							snake_case_		    =     ["This is a simple input looooooooong", "This is a simple input"]
							snake_case_		    =     ("This is a simple input", "This is a pair")
							snake_case_		    =     [
							    ("This is a simple input loooooong", "This is a simple input"),
							    ("This is a simple pair loooooong", "This is a simple pair"),
							]

							snake_case_		    =     tokenizer.pad_token_id

							snake_case_		    =     tokenizer(__lowercase       ,	padding="max_length"       ,	max_length=30       ,	return_tensors="np"						)
							snake_case_		    =     tokenizer(__lowercase       ,	padding=__lowercase       ,	truncate=__lowercase       ,	return_tensors="np"						)
							snake_case_		    =     tokenizer(*__lowercase       ,	padding="max_length"       ,	max_length=60       ,	return_tensors="np"						)
							snake_case_		    =     tokenizer(__lowercase       ,	padding=__lowercase       ,	truncate=__lowercase       ,	return_tensors="np"						)

							# s
							# test single string max_length padding
							self.assertEqual(out_s["input_ids"].shape[-1]       ,	30						)
							self.assertTrue(pad_token_id in out_s["input_ids"]						)
							self.assertTrue(0 in out_s["attention_mask"]						)

							# s2
							# test automatic padding
							self.assertEqual(out_sa["input_ids"].shape[-1]       ,	33						)
							# long slice doesn't have padding
							self.assertFalse(pad_token_id in out_sa["input_ids"][0]						)
							self.assertFalse(0 in out_sa["attention_mask"][0]						)
							# short slice does have padding
							self.assertTrue(pad_token_id in out_sa["input_ids"][1]						)
							self.assertTrue(0 in out_sa["attention_mask"][1]						)

							# p
							# test single pair max_length padding
							self.assertEqual(out_p["input_ids"].shape[-1]       ,	60						)
							self.assertTrue(pad_token_id in out_p["input_ids"]						)
							self.assertTrue(0 in out_p["attention_mask"]						)

							# p2
							# test automatic padding pair
							self.assertEqual(out_pa["input_ids"].shape[-1]       ,	52						)
							# long slice pair doesn't have padding
							self.assertFalse(pad_token_id in out_pa["input_ids"][0]						)
							self.assertFalse(0 in out_pa["attention_mask"][0]						)
							# short slice pair does have padding
							self.assertTrue(pad_token_id in out_pa["input_ids"][1]						)
							self.assertTrue(0 in out_pa["attention_mask"][1]						)






						def        snake_case__						(						self							:     Tuple						):






							"""simple docstring"""






							snake_case_		    =     "$$$"
							snake_case_		    =     CodeGenTokenizer.from_pretrained(self.tmpdirname       ,	bos_token=__lowercase       ,	add_bos_token=__lowercase						)

							snake_case_		    =     "This is a simple input"
							snake_case_		    =     ["This is a simple input 1", "This is a simple input 2"]

							snake_case_		    =     tokenizer.bos_token_id

							snake_case_		    =     tokenizer(__lowercase						)
							snake_case_		    =     tokenizer(__lowercase						)

							self.assertEqual(out_s.input_ids[0]       ,	__lowercase						)
							self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids						)						)

							snake_case_		    =     tokenizer.decode(out_s.input_ids						)
							snake_case_		    =     tokenizer.batch_decode(out_sa.input_ids						)

							self.assertEqual(decode_s.split()[0]       ,	__lowercase						)
							self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa						)						)






						@slow
						def        snake_case__						(						self							:     Tuple						):






							"""simple docstring"""






							snake_case_		    =     CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono"						)

							snake_case_		    =     "\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"
							snake_case_		    =     "\nif len_a > len_b:      result = a\nelse:      result = b"

							snake_case_		    =     tokenizer.encode(__lowercase						)
							snake_case_		    =     ["^#", re.escape("<|endoftext|>"						), "^'''", "^\"\"\"", "\n\n\n"]
							snake_case_		    =     tokenizer.decode(__lowercase       ,	truncate_before_pattern=__lowercase						)
							self.assertEqual(__lowercase       ,	__lowercase						)






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							pass
