
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     UpperCamelCase      (					__lowerCamelCase  :				List[str]=None       ,      __lowerCamelCase  :				List[Any]=None					):
       return field(default_factory=lambda: default       ,      metadata=__lowerCamelCase					)






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



def     UpperCamelCase      (					__lowerCamelCase  :				List[str]					):
       try:
              int(__lowerCamelCase					)
              return True
       except ValueError:
              return False


def     UpperCamelCase      (					__lowerCamelCase  :				str					):
       try:
              float(__lowerCamelCase					)
              return True
       except ValueError:
              return False






class 						UpperCAmelCase	:



   def __init__(self	:      Tuple					,		snake_case__	:      int				)	->			Any:


          '''simple docstring'''


          snake_case :     List[Any]           =   args
          snake_case :     Optional[Any]           =   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

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



   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]				)	->			Optional[Any]:


          '''simple docstring'''


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

                 ((snake_case)       ,      (snake_case)) :     Tuple           =   (
                     (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                 )

                 snake_case :     int           =   (
                     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 :     int           =   np.asarray(
                                   [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,		dtype=snake_case__					,		)
                        else:
                               snake_case :     Dict           =   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)) :     List[Any]           =   (
                            ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                        )

                        snake_case :     Optional[int]           =   np.asarray(snake_case__					,		snake_case__				)[: len(snake_case__				)]
                        plt.scatter(
                            snake_case__					,		snake_case__					,		label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""				)
                        plt.plot(snake_case__					,		snake_case__					,		"--"				)

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

          snake_case :     Optional[int]           =   title_str[:-4]
          snake_case :     Optional[int]           =   "Time in s" if self.args.is_time else "Memory in MB"

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

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



def     UpperCamelCase      (					):
       snake_case :     Optional[Any]           =   HfArgumentParser(__lowerCamelCase					)
       snake_case :     str           =   parser.parse_args_into_dataclasses()[0]
       snake_case :     List[Any]           =   Plot(args=__lowerCamelCase					)
       plot.plot()


if __name__ == "__main__":
   main()







import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						UpperCAmelCase	(		A_ ,unittest.TestCase				):
   A__      :	Dict		  =  PreTrainedTokenizerFast
   A__      :	str		  =  False
   A__      :	List[Any]		  =  True
   A__      :	List[str]		  =  "tokenizer_file"



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			Union[str, Any]:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   False  # because we don't have pretrained_vocab_files_map
          super().setUp()
          snake_case :     Optional[int]           =   True

          snake_case :     List[str]           =   ["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
          snake_case :     Optional[Any]           =   "SaulLu/dummy-tokenizer-bytelevel-bpe"

          # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
          snake_case :     int           =   [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

          snake_case :     List[Any]           =   PreTrainedTokenizerFast.from_pretrained(model_paths[0]				)
          tokenizer.save_pretrained(self.tmpdirname				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			int:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			int:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			Union[str, Any]:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			List[str]:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[str]:


          '''simple docstring'''


          snake_case :     Optional[int]           =   self.tmpdirname
          # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
                        try:
                               snake_case :     Any           =   tempfile.mkdtemp()
                               snake_case :     int           =   self.rust_tokenizer_class.from_pretrained(snake_case__					,		**snake_case__				)

                               tokenizer.save_pretrained(self.tmpdirname				)
                               super().test_training_new_tokenizer()
                        finally:
                               # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                               # is restored
                               shutil.rmtree(self.tmpdirname				)
                               snake_case :     int           =   tmpdirname_orig



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			Dict:


          '''simple docstring'''


          snake_case :     Optional[int]           =   self.tmpdirname
          # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
                        try:
                               snake_case :     Union[str, Any]           =   tempfile.mkdtemp()
                               snake_case :     str           =   self.rust_tokenizer_class.from_pretrained(snake_case__					,		**snake_case__				)

                               tokenizer.save_pretrained(self.tmpdirname				)
                               super().test_training_new_tokenizer_with_special_tokens_change()
                        finally:
                               # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                               # is restored
                               shutil.rmtree(self.tmpdirname				)
                               snake_case :     Dict           =   tmpdirname_orig



   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			Union[str, Any]:


          '''simple docstring'''


          snake_case :     int           =   self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name				)

          snake_case :     int           =   ("a" for _ in range(10_00				))
          snake_case :     Optional[int]           =   tokenizer.train_new_from_iterator(text_iterator=snake_case__					,		length=10_00					,		vocab_size=50				)

          snake_case :     str           =   new_tokenizer.encode("a🤗"				)
          self.assertEqual(snake_case__					,		[64, 1_72, 2_53, 97, 2_45]				)






@require_tokenizers
class 						UpperCAmelCase	(		unittest.TestCase				):



   def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]				)	->			List[str]:


          '''simple docstring'''


          snake_case :     List[str]           =   AutoTokenizer.from_pretrained("bert-base-cased"				)
          snake_case :     Union[str, Any]           =   json.loads(tokenizer._tokenizer.to_str()				)
          snake_case :     Tuple           =   len(snake_case__				)

          with tempfile.TemporaryDirectory() as tmp_dir:
                 # Hack to save this in the tokenizer_config.json
                 snake_case :     List[str]           =   ["tokenizer.4.0.0.json"]
                 tokenizer.save_pretrained(snake_case__				)
                 json.dump(snake_case__					,		open(os.path.join(snake_case__					,		"tokenizer.4.0.0.json"				)					,		"w"				)				)

                 # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
                 snake_case :     Optional[Any]           =   AutoTokenizer.from_pretrained(snake_case__				)
                 self.assertEqual(len(snake_case__				)					,		len(snake_case__				) + 1				)
                 snake_case :     Union[str, Any]           =   json.loads(new_tokenizer._tokenizer.to_str()				)
                 self.assertIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)

                 # Will need to be adjusted if we reach v42 and this test is still here.
                 # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
                 shutil.move(os.path.join(snake_case__					,		"tokenizer.4.0.0.json"				)					,		os.path.join(snake_case__					,		"tokenizer.42.0.0.json"				)				)
                 snake_case :     Optional[Any]           =   ["tokenizer.42.0.0.json"]
                 tokenizer.save_pretrained(snake_case__				)
                 snake_case :     Dict           =   AutoTokenizer.from_pretrained(snake_case__				)
                 self.assertEqual(len(snake_case__				)					,		len(snake_case__				)				)
                 snake_case :     Optional[int]           =   json.loads(new_tokenizer._tokenizer.to_str()				)
                 self.assertNotIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[str]:


          '''simple docstring'''


          snake_case :     int           =   "hf-internal-testing/test-two-tokenizers"

          # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
          snake_case :     int           =   AutoTokenizer.from_pretrained(snake_case__				)
          self.assertEqual(len(snake_case__				)					,		2_89_97				)
          snake_case :     Optional[int]           =   json.loads(tokenizer._tokenizer.to_str()				)
          self.assertIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)

          # Testing an older version by monkey-patching the version in the module it's used.
          import transformers as old_transformers

          snake_case :     Dict           =   "3.0.0"
          snake_case :     Union[str, Any]           =   old_transformers.models.auto.AutoTokenizer.from_pretrained(snake_case__				)
          self.assertEqual(len(snake_case__				)					,		2_89_96				)
          snake_case :     Optional[Any]           =   json.loads(old_tokenizer._tokenizer.to_str()				)
          self.assertNotIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)






@require_tokenizers
class 						UpperCAmelCase	(		unittest.TestCase				):



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     Tuple           =   PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"				)
          snake_case :     List[Any]           =   "The Matrix is a 1999 science fiction action film."

          with concurrent.futures.ThreadPoolExecutor() as executor:
                 snake_case :     Union[str, Any]           =   [executor.submit(self.fetch					,		snake_case__					,		snake_case__				) for i in range(10				)]
                 snake_case :     Optional[Any]           =   [future.result() for future in futures]
                 self.assertEqual(snake_case__					,		[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10				)]				)



   def        _SCREAMING_SNAKE_CASE     (self	:      str					,		snake_case__	:      List[str]					,		snake_case__	:      str				)	->			List[str]:


          '''simple docstring'''


          return tokenizer.encode(snake_case__					,		truncation="longest_first"					,		padding="longest"				)





