





'''simple docstring'''






import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 		lowerCamelCase  (      _A      ,   unittest.TestCase       ):
	snake_case_      =   GPTaTokenizer
	snake_case_      =   GPTaTokenizerFast
	snake_case_      =   True
	snake_case_      =   {"add_prefix_space": True}
	snake_case_      =   False



	def   _lowerCamelCase    (   self	):
			super().setUp()

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

			lowerCAmelCase     :    Any						=   os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["vocab_file"]	)
			lowerCAmelCase     :    Tuple						=   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(a_	) + "\n"	)
			with open(self.merges_file		,      "w"		,      encoding="utf-8"	) as fp:
					fp.write("\n".join(a_	)	)



	def   _lowerCamelCase    (   self		,      **a_	):
			kwargs.update(self.special_tokens_map	)
			return GPTaTokenizer.from_pretrained(self.tmpdirname		,      **a_	)



	def   _lowerCamelCase    (   self		,      **a_	):
			kwargs.update(self.special_tokens_map	)
			return GPTaTokenizerFast.from_pretrained(self.tmpdirname		,      **a_	)



	def   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    Tuple						=   "lower newer"
			lowerCAmelCase     :    int						=   "lower newer"
			return input_text, output_text



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   GPTaTokenizer(self.vocab_file		,      self.merges_file		,      **self.special_tokens_map	)
			lowerCAmelCase     :    Dict						=   "lower newer"
			lowerCAmelCase     :    Optional[int]						=   ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
			lowerCAmelCase     :    Union[str, Any]						=   tokenizer.tokenize(a_		,      add_prefix_space=a_	)
			self.assertListEqual(a_		,      a_	)

			lowerCAmelCase     :    Any						=   tokens + [tokenizer.unk_token]
			lowerCAmelCase     :    Optional[int]						=   [14, 15, 10, 9, 3, 2, 15, 19]
			self.assertListEqual(tokenizer.convert_tokens_to_ids(a_	)		,      a_	)



	def   _lowerCamelCase    (   self	):
			if not self.test_rust_tokenizer:
					return

			lowerCAmelCase     :    int						=   self.get_tokenizer()
			lowerCAmelCase     :    Any						=   self.get_rust_tokenizer(add_prefix_space=a_	)

			lowerCAmelCase     :    int						=   "lower newer"

			# Testing tokenization
			lowerCAmelCase     :    Dict						=   tokenizer.tokenize(a_		,      add_prefix_space=a_	)
			lowerCAmelCase     :    List[Any]						=   rust_tokenizer.tokenize(a_	)
			self.assertListEqual(a_		,      a_	)

			# Testing conversion to ids without special tokens
			lowerCAmelCase     :    Any						=   tokenizer.encode(a_		,      add_special_tokens=a_		,      add_prefix_space=a_	)
			lowerCAmelCase     :    str						=   rust_tokenizer.encode(a_		,      add_special_tokens=a_	)
			self.assertListEqual(a_		,      a_	)

			# Testing conversion to ids with special tokens
			lowerCAmelCase     :    Any						=   self.get_rust_tokenizer(add_prefix_space=a_	)
			lowerCAmelCase     :    List[Any]						=   tokenizer.encode(a_		,      add_prefix_space=a_	)
			lowerCAmelCase     :    int						=   rust_tokenizer.encode(a_	)
			self.assertListEqual(a_		,      a_	)

			# Testing the unknown token
			lowerCAmelCase     :    Optional[Any]						=   tokens + [rust_tokenizer.unk_token]
			lowerCAmelCase     :    str						=   [14, 15, 10, 9, 3, 2, 15, 19]
			self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a_	)		,      a_	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			# It's very difficult to mix/test pretokenization with byte-level
			# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
			pass



	def   _lowerCamelCase    (   self		,      a_=15	):
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
							lowerCAmelCase     :    Optional[Any]						=   self.rust_tokenizer_class.from_pretrained(a_		,      **a_	)

							# Simple input
							lowerCAmelCase     :    Tuple						=   "This is a simple input"
							lowerCAmelCase     :    List[str]						=   ["This is a simple input 1", "This is a simple input 2"]
							lowerCAmelCase     :    Tuple						=   ("This is a simple input", "This is a pair")
							lowerCAmelCase     :    Dict						=   [
							    ("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(a_		,      tokenizer_r.encode		,      a_		,      max_length=a_		,      padding="max_length"	)

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

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

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

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

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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   GPTaTokenizer.from_pretrained(self.tmpdirname		,      pad_token="<pad>"	)

			# Simple input
			lowerCAmelCase     :    str						=   "This is a simple input"
			lowerCAmelCase     :    str						=   ["This is a simple input looooooooong", "This is a simple input"]
			lowerCAmelCase     :    Tuple						=   ("This is a simple input", "This is a pair")
			lowerCAmelCase     :    Optional[Any]						=   [
			    ("This is a simple input loooooong", "This is a simple input"),
			    ("This is a simple pair loooooong", "This is a simple pair"),
			]

			lowerCAmelCase     :    List[str]						=   tokenizer.pad_token_id

			lowerCAmelCase     :    Tuple						=   tokenizer(a_		,      padding="max_length"		,      max_length=30		,      return_tensors="np"	)
			lowerCAmelCase     :    Tuple						=   tokenizer(a_		,      padding=a_		,      truncate=a_		,      return_tensors="np"	)
			lowerCAmelCase     :    Any						=   tokenizer(*a_		,      padding="max_length"		,      max_length=60		,      return_tensors="np"	)
			lowerCAmelCase     :    Tuple						=   tokenizer(a_		,      padding=a_		,      truncate=a_		,      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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   "$$$"
			lowerCAmelCase     :    List[str]						=   GPTaTokenizer.from_pretrained(self.tmpdirname		,      bos_token=a_		,      add_bos_token=a_	)

			lowerCAmelCase     :    int						=   "This is a simple input"
			lowerCAmelCase     :    str						=   ["This is a simple input 1", "This is a simple input 2"]

			lowerCAmelCase     :    Optional[int]						=   tokenizer.bos_token_id

			lowerCAmelCase     :    Dict						=   tokenizer(a_	)
			lowerCAmelCase     :    str						=   tokenizer(a_	)

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

			lowerCAmelCase     :    List[str]						=   tokenizer.decode(out_s.input_ids	)
			lowerCAmelCase     :    Optional[Any]						=   tokenizer.batch_decode(out_sa.input_ids	)

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



	def   _lowerCamelCase    (   self	):
			pass





	def   _lowerCamelCase    (   self	):
			# TODO: change to self.get_tokenizers() when the fast version is implemented
			lowerCAmelCase     :    str						=   [self.get_tokenizer(do_lower_case=a_		,      add_bos_token=a_	)]
			for tokenizer in tokenizers:
					with self.subTest(F'''{tokenizer.__class__.__name__}'''	):
							lowerCAmelCase     :    Dict						=   "Encode this."
							lowerCAmelCase     :    int						=   "This one too please."
							lowerCAmelCase     :    Tuple						=   tokenizer.encode(a_		,      add_special_tokens=a_	)
							encoded_sequence += tokenizer.encode(a_		,      add_special_tokens=a_	)
							lowerCAmelCase     :    Tuple						=   tokenizer.encode_plus(
							    a_		,      a_		,      add_special_tokens=a_		,      return_special_tokens_mask=a_		,      )
							lowerCAmelCase     :    Optional[int]						=   encoded_sequence_dict["input_ids"]
							lowerCAmelCase     :    List[Any]						=   encoded_sequence_dict["special_tokens_mask"]
							self.assertEqual(len(a_	)		,      len(a_	)	)

							lowerCAmelCase     :    Tuple						=   [
							    (x if not special_tokens_mask[i] else None) for i, x in enumerate(a_	)
							]
							lowerCAmelCase     :    str						=   [x for x in filtered_sequence if x is not None]
							self.assertEqual(a_		,      a_	)






@require_tokenizers
class 		lowerCamelCase  (      unittest.TestCase       ):



	def   _lowerCamelCase    (   self	):
			# More context:
			# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
			# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
			# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

			lowerCAmelCase     :    Optional[Any]						=   AutoTokenizer.from_pretrained("facebook/opt-350m"		,      from_slow=a_	)
			lowerCAmelCase     :    List[str]						=   "A photo of a cat"

			lowerCAmelCase     :    int						=   tokenizer.encode(
			    a_		,      )
			self.assertEqual(a_		,      [2, 250, 1_345, 9, 10, 4_758]	)
			tokenizer.save_pretrained("test_opt"	)

			lowerCAmelCase     :    str						=   AutoTokenizer.from_pretrained("./test_opt"	)
			lowerCAmelCase     :    List[str]						=   tokenizer.encode(
			    a_		,      )
			self.assertEqual(a_		,      [2, 250, 1_345, 9, 10, 4_758]	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[int]						=   AutoTokenizer.from_pretrained("facebook/opt-350m"		,      use_slow=a_	)
			lowerCAmelCase     :    List[str]						=   "A photo of a cat"

			lowerCAmelCase     :    str						=   tokenizer.encode(
			    a_		,      )
			# Same as above
			self.assertEqual(a_		,      [2, 250, 1_345, 9, 10, 4_758]	)





	@unittest.skip("This test is failing because of a bug in the fast tokenizer"	)
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   AutoTokenizer.from_pretrained("facebook/opt-350m"		,      from_slow=a_	)

			lowerCAmelCase     :    int						=   "bos"
			lowerCAmelCase     :    Union[str, Any]						=   tokenizer.get_vocab()["bos"]

			lowerCAmelCase     :    Union[str, Any]						=   "A photo of a cat"
			lowerCAmelCase     :    List[Any]						=   tokenizer.encode(
			    a_		,      )
			# We changed the bos token
			self.assertEqual(a_		,      [31_957, 250, 1_345, 9, 10, 4_758]	)
			tokenizer.save_pretrained("./tok"	)
			lowerCAmelCase     :    List[Any]						=   AutoTokenizer.from_pretrained("./tok"	)
			self.assertTrue(tokenizer.is_fast	)
			lowerCAmelCase     :    Optional[int]						=   tokenizer.encode(
			    a_		,      )
			self.assertEqual(a_		,      [31_957, 250, 1_345, 9, 10, 4_758]	)










'''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      __A						(							a_						:  Any=None      ,a_						:  List[Any]=None	):
		return field(default_factory=lambda: default      ,metadata=a_	)
@dataclass
class 		lowerCamelCase  :
	snake_case_      =   field(
	    metadata={"help": "The csv file to plot."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Disable logarithmic scale when plotting"}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={
	        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
	    }      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}      ,   )
	snake_case_      =   list_field(
	    default=_A      ,   metadata={"help": "List of model names that are used instead of the ones in the csv file."}       )






def      __A						(							a_						:  Tuple	):
		try:
				int(a_	)
				return True
		except ValueError:
				return False






def      __A						(							a_						:  int	):
		try:
				float(a_	)
				return True
		except ValueError:
				return False
class 		lowerCamelCase  :



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

			with open(self.args.csv_file		,      newline=""	) as csv_file:
					lowerCAmelCase     :    str						=   csv.DictReader(a_	)
					for row in reader:
							lowerCAmelCase     :    Tuple						=   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     :    Union[str, Any]						=   int(row["result"]	)
							elif can_convert_to_float(row["result"]	):
									# value is not None
									lowerCAmelCase     :    Optional[int]						=   float(row["result"]	)





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase	,		lowerCAmelCase     :    Any						=   plt.subplots()
			lowerCAmelCase     :    int						=   "Time usage" if self.args.is_time else "Memory usage"
			lowerCAmelCase     :    List[Any]						=   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     :    str						=   sorted(set(self.result_dict[model_name]["bsz"]	)	)
					lowerCAmelCase     :    List[str]						=   sorted(set(self.result_dict[model_name]["seq_len"]	)	)
					lowerCAmelCase     :    Union[str, Any]						=   self.result_dict[model_name]["result"]

					((lowerCAmelCase)	,		(lowerCAmelCase))     :    str						=   (
					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
					)

					lowerCAmelCase     :    Union[str, Any]						=   (
					    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     :    int						=   np.asarray(
									    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,      dtype=a_		,      )
							else:
									lowerCAmelCase     :    Any						=   np.asarray(
									    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,      dtype=np.floataa		,      )

							((lowerCAmelCase)	,		(lowerCAmelCase))     :    Any						=   (
							    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
							)

							lowerCAmelCase     :    Union[str, Any]						=   np.asarray(a_		,      a_	)[: len(a_	)]
							plt.scatter(
							    a_		,      a_		,      label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''	)
							plt.plot(a_		,      a_		,      "--"	)

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

			lowerCAmelCase     :    List[str]						=   title_str[:-4]
			lowerCAmelCase     :    List[Any]						=   "Time in s" if self.args.is_time else "Memory in MB"

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

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






def      __A						(							):
		lowerCAmelCase     :    Optional[Any]						=   HfArgumentParser(a_	)
		lowerCAmelCase     :    List[Any]						=   parser.parse_args_into_dataclasses()[0]
		lowerCAmelCase     :    str						=   Plot(args=a_	)
		plot.plot()


if __name__ == "__main__":
					main()



