


import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
   from test_module.custom_tokenization_fast import CustomTokenizerFast







class lowerCAmelCase_		(				unittest.TestCase ):

 '''simple docstring'''

 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   0



 @slow
 def 		UpperCamelCase__  (			self      ):
       for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
             snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase      )
             self.assertIsNotNone(_UpperCAmelCase      )
             self.assertIsInstance(_UpperCAmelCase	,   (BertTokenizer, BertTokenizerFast)      )
             self.assertGreater(len(_UpperCAmelCase      )	,   0      )

       for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
             snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase      )
             self.assertIsNotNone(_UpperCAmelCase      )
             self.assertIsInstance(_UpperCAmelCase	,   (GPTaTokenizer, GPTaTokenizerFast)      )
             self.assertGreater(len(_UpperCAmelCase      )	,   0      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase      )
       self.assertIsInstance(_UpperCAmelCase	,   (BertTokenizer, BertTokenizerFast)      )
       self.assertEqual(tokenizer.vocab_size	,   12      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase      )
       self.assertIsInstance(_UpperCAmelCase	,   (RobertaTokenizer, RobertaTokenizerFast)      )
       self.assertEqual(tokenizer.vocab_size	,   20      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   AutoConfig.from_pretrained(_UpperCAmelCase      )
       self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )
       # Check that tokenizer_type ≠ model_type
       snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase	,   config=_UpperCAmelCase      )
       self.assertIsInstance(_UpperCAmelCase	,   (BertTokenizer, BertTokenizerFast)      )
       self.assertEqual(tokenizer.vocab_size	,   12      )



 def 		UpperCamelCase__  (			self      ):
       with tempfile.TemporaryDirectory() as tmp_dir:
             shutil.copy('''./tests/fixtures/vocab.txt'''	,   os.path.join(_UpperCAmelCase	,   '''vocab.txt'''      )      )

             snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase	,   tokenizer_type='''bert'''	,   use_fast=_UpperCAmelCase      )
             self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )

       with tempfile.TemporaryDirectory() as tmp_dir:
             shutil.copy('''./tests/fixtures/vocab.json'''	,   os.path.join(_UpperCAmelCase	,   '''vocab.json'''      )      )
             shutil.copy('''./tests/fixtures/merges.txt'''	,   os.path.join(_UpperCAmelCase	,   '''merges.txt'''      )      )

             snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase	,   tokenizer_type='''gpt2'''	,   use_fast=_UpperCAmelCase      )
             self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )



 @require_tokenizers
 def 		UpperCamelCase__  (			self      ):
       with tempfile.TemporaryDirectory() as tmp_dir:
             shutil.copy('''./tests/fixtures/vocab.txt'''	,   os.path.join(_UpperCAmelCase	,   '''vocab.txt'''      )      )

             snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase	,   tokenizer_type='''bert'''      )
             self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )

       with tempfile.TemporaryDirectory() as tmp_dir:
             shutil.copy('''./tests/fixtures/vocab.json'''	,   os.path.join(_UpperCAmelCase	,   '''vocab.json'''      )      )
             shutil.copy('''./tests/fixtures/merges.txt'''	,   os.path.join(_UpperCAmelCase	,   '''merges.txt'''      )      )

             snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase	,   tokenizer_type='''gpt2'''      )
             self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       with pytest.raises(_UpperCAmelCase      ):
             AutoTokenizer.from_pretrained('''./'''	,   tokenizer_type='''xxx'''      )



 @require_tokenizers
 def 		UpperCamelCase__  (			self      ):
       for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
             snake_case_ =   tokenizer_class.from_pretrained('''wietsedv/bert-base-dutch-cased'''      )
             self.assertIsInstance(_UpperCAmelCase	,   (BertTokenizer, BertTokenizerFast)      )

             if isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
                   self.assertEqual(tokenizer.basic_tokenizer.do_lower_case	,   _UpperCAmelCase      )
             else:
                   self.assertEqual(tokenizer.do_lower_case	,   _UpperCAmelCase      )

             self.assertEqual(tokenizer.model_max_length	,   5_12      )



 @require_tokenizers
 def 		UpperCamelCase__  (			self      ):
       for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
             with self.assertRaisesRegex(
                 _UpperCAmelCase	,   '''julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier'''	,   ):
                   snake_case_ =   tokenizer_class.from_pretrained('''julien-c/herlolip-not-exists'''      )



 def 		UpperCamelCase__  (			self      ):
       # tests: https://github.com/huggingface/transformers/pull/13251
       # 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
       # 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
       snake_case_ =   TOKENIZER_MAPPING.values()
       snake_case_ =   []

       for slow_tok, fast_tok in tokenizers:
             if slow_tok is not None:
                   tokenizer_names.append(slow_tok.__name__      )

             if fast_tok is not None:
                   tokenizer_names.append(fast_tok.__name__      )

       for tokenizer_name in tokenizer_names:
             # must find the right class
             tokenizer_class_from_name(_UpperCAmelCase      )



 @require_tokenizers
 def 		UpperCamelCase__  (			self      ):
       self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased'''	,   use_fast=_UpperCAmelCase      )	,   _UpperCAmelCase      )
       self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased'''      )	,   _UpperCAmelCase      )



 @require_tokenizers
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   AutoTokenizer.from_pretrained('''distilbert-base-uncased'''	,   do_lower_case=_UpperCAmelCase      )
       snake_case_ =   '''Hello, world. How are you?'''
       snake_case_ =   tokenizer.tokenize(_UpperCAmelCase      )
       self.assertEqual('''[UNK]'''	,   tokens[0]      )

       snake_case_ =   AutoTokenizer.from_pretrained('''microsoft/mpnet-base'''	,   do_lower_case=_UpperCAmelCase      )
       snake_case_ =   tokenizer.tokenize(_UpperCAmelCase      )
       self.assertEqual('''[UNK]'''	,   tokens[0]      )



 @require_tokenizers
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   AutoTokenizer.from_pretrained('''robot-test/dummy-tokenizer-fast-with-model-config'''      )
       self.assertEqual(type(_UpperCAmelCase      )	,   _UpperCAmelCase      )
       self.assertEqual(tokenizer.model_max_length	,   5_12      )
       self.assertEqual(tokenizer.vocab_size	,   3_00_00      )
       self.assertEqual(tokenizer.unk_token	,   '''[UNK]'''      )
       self.assertEqual(tokenizer.padding_side	,   '''right'''      )
       self.assertEqual(tokenizer.truncation_side	,   '''right'''      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase      )
       self.assertIsInstance(_UpperCAmelCase	,   (BertTokenizer, BertTokenizerFast)      )
       with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(_UpperCAmelCase      )
             snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase      )

       self.assertIsInstance(_UpperCAmelCase	,   tokenizer.__class__      )
       self.assertEqual(tokenizera.vocab_size	,   12      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   AutoTokenizer.from_pretrained('''ctrl'''      )
       # There is no fast CTRL so this always gives us a slow tokenizer.
       self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       # Check we can load the tokenizer config of an online model.
       snake_case_ =   get_tokenizer_config('''bert-base-cased'''      )
       snake_case_ =   config.pop('''_commit_hash'''	,   _UpperCAmelCase      )
       # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
       self.assertEqual(_UpperCAmelCase	,   {'''do_lower_case''': False}      )

       # This model does not have a tokenizer_config so we get back an empty dict.
       snake_case_ =   get_tokenizer_config(_UpperCAmelCase      )
       self.assertDictEqual(_UpperCAmelCase	,   {}      )

       # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
       snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase      )
       with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(_UpperCAmelCase      )
             snake_case_ =   get_tokenizer_config(_UpperCAmelCase      )

       # Check the class of the tokenizer was properly saved (note that it always saves the slow class).
       self.assertEqual(config['''tokenizer_class''']	,   '''BertTokenizer'''      )



 def 		UpperCamelCase__  (			self      ):
       try:
             AutoConfig.register('''custom'''	,   _UpperCAmelCase      )

             AutoTokenizer.register(_UpperCAmelCase	,   slow_tokenizer_class=_UpperCAmelCase      )
             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(_UpperCAmelCase      ):
                   AutoTokenizer.register(_UpperCAmelCase	,   slow_tokenizer_class=_UpperCAmelCase      )

             snake_case_ =   CustomTokenizer.from_pretrained(_UpperCAmelCase      )
             with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(_UpperCAmelCase      )

                   snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase      )
                   self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]



 @require_tokenizers
 def 		UpperCamelCase__  (			self      ):
       try:
             AutoConfig.register('''custom'''	,   _UpperCAmelCase      )

             # Can register in two steps
             AutoTokenizer.register(_UpperCAmelCase	,   slow_tokenizer_class=_UpperCAmelCase      )
             self.assertEqual(TOKENIZER_MAPPING[CustomConfig]	,   (CustomTokenizer, None)      )
             AutoTokenizer.register(_UpperCAmelCase	,   fast_tokenizer_class=_UpperCAmelCase      )
             self.assertEqual(TOKENIZER_MAPPING[CustomConfig]	,   (CustomTokenizer, CustomTokenizerFast)      )

             del TOKENIZER_MAPPING._extra_content[CustomConfig]
             # Can register in one step
             AutoTokenizer.register(
                 _UpperCAmelCase	,   slow_tokenizer_class=_UpperCAmelCase	,   fast_tokenizer_class=_UpperCAmelCase      )
             self.assertEqual(TOKENIZER_MAPPING[CustomConfig]	,   (CustomTokenizer, CustomTokenizerFast)      )

             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(_UpperCAmelCase      ):
                   AutoTokenizer.register(_UpperCAmelCase	,   fast_tokenizer_class=_UpperCAmelCase      )

             # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
             # and that model does not have a tokenizer.json
             with tempfile.TemporaryDirectory() as tmp_dir:
                   snake_case_ =   BertTokenizerFast.from_pretrained(_UpperCAmelCase      )
                   bert_tokenizer.save_pretrained(_UpperCAmelCase      )
                   snake_case_ =   CustomTokenizerFast.from_pretrained(_UpperCAmelCase      )

             with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(_UpperCAmelCase      )

                   snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase      )
                   self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )

                   snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase	,   use_fast=_UpperCAmelCase      )
                   self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]



 def 		UpperCamelCase__  (			self      ):
       # If remote code is not set, we will time out when asking whether to load the model.
       with self.assertRaises(_UpperCAmelCase      ):
             snake_case_ =   AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''      )
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(_UpperCAmelCase      ):
             snake_case_ =   AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''	,   trust_remote_code=_UpperCAmelCase      )

       snake_case_ =   AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''	,   trust_remote_code=_UpperCAmelCase      )
       self.assertTrue(tokenizer.special_attribute_present      )
       # Test tokenizer can be reloaded.
       with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(_UpperCAmelCase      )
             snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase	,   trust_remote_code=_UpperCAmelCase      )
       self.assertTrue(reloaded_tokenizer.special_attribute_present      )

       if is_tokenizers_available():
             self.assertEqual(tokenizer.__class__.__name__	,   '''NewTokenizerFast'''      )
             self.assertEqual(reloaded_tokenizer.__class__.__name__	,   '''NewTokenizerFast'''      )

             # Test we can also load the slow version
             snake_case_ =   AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''	,   trust_remote_code=_UpperCAmelCase	,   use_fast=_UpperCAmelCase      )
             self.assertTrue(tokenizer.special_attribute_present      )
             self.assertEqual(tokenizer.__class__.__name__	,   '''NewTokenizer'''      )
             # Test tokenizer can be reloaded.
             with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer.save_pretrained(_UpperCAmelCase      )
                   snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase	,   trust_remote_code=_UpperCAmelCase	,   use_fast=_UpperCAmelCase      )
             self.assertEqual(reloaded_tokenizer.__class__.__name__	,   '''NewTokenizer'''      )
             self.assertTrue(reloaded_tokenizer.special_attribute_present      )
       else:
             self.assertEqual(tokenizer.__class__.__name__	,   '''NewTokenizer'''      )
             self.assertEqual(reloaded_tokenizer.__class__.__name__	,   '''NewTokenizer'''      )



 @require_tokenizers
 def 		UpperCamelCase__  (			self      ):







       class lowerCAmelCase_		(				lowerCamelCase__ ):

        '''simple docstring'''

        __snake_case				=						False







       class lowerCAmelCase_		(				lowerCamelCase__ ):

        '''simple docstring'''

        __snake_case				=						NewTokenizer
        __snake_case				=						False

       try:
             AutoConfig.register('''custom'''	,   _UpperCAmelCase      )
             AutoTokenizer.register(_UpperCAmelCase	,   slow_tokenizer_class=_UpperCAmelCase      )
             AutoTokenizer.register(_UpperCAmelCase	,   fast_tokenizer_class=_UpperCAmelCase      )
             # If remote code is not set, the default is to use local
             snake_case_ =   AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''      )
             self.assertEqual(tokenizer.__class__.__name__	,   '''NewTokenizerFast'''      )
             self.assertFalse(tokenizer.special_attribute_present      )
             snake_case_ =   AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''	,   use_fast=_UpperCAmelCase      )
             self.assertEqual(tokenizer.__class__.__name__	,   '''NewTokenizer'''      )
             self.assertFalse(tokenizer.special_attribute_present      )

             # If remote code is disabled, we load the local one.
             snake_case_ =   AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''	,   trust_remote_code=_UpperCAmelCase      )
             self.assertEqual(tokenizer.__class__.__name__	,   '''NewTokenizerFast'''      )
             self.assertFalse(tokenizer.special_attribute_present      )
             snake_case_ =   AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''	,   trust_remote_code=_UpperCAmelCase	,   use_fast=_UpperCAmelCase      )
             self.assertEqual(tokenizer.__class__.__name__	,   '''NewTokenizer'''      )
             self.assertFalse(tokenizer.special_attribute_present      )

             # If remote is enabled, we load from the Hub
             snake_case_ =   AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''	,   trust_remote_code=_UpperCAmelCase      )
             self.assertEqual(tokenizer.__class__.__name__	,   '''NewTokenizerFast'''      )
             self.assertTrue(tokenizer.special_attribute_present      )
             snake_case_ =   AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''	,   trust_remote_code=_UpperCAmelCase	,   use_fast=_UpperCAmelCase      )
             self.assertEqual(tokenizer.__class__.__name__	,   '''NewTokenizer'''      )
             self.assertTrue(tokenizer.special_attribute_present      )

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   AutoTokenizer.from_pretrained(
           '''hf-internal-testing/test_dynamic_tokenizer_legacy'''	,   trust_remote_code=_UpperCAmelCase      )
       self.assertTrue(tokenizer.special_attribute_present      )
       if is_tokenizers_available():
             self.assertEqual(tokenizer.__class__.__name__	,   '''NewTokenizerFast'''      )

             # Test we can also load the slow version
             snake_case_ =   AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer_legacy'''	,   trust_remote_code=_UpperCAmelCase	,   use_fast=_UpperCAmelCase      )
             self.assertTrue(tokenizer.special_attribute_present      )
             self.assertEqual(tokenizer.__class__.__name__	,   '''NewTokenizer'''      )
       else:
             self.assertEqual(tokenizer.__class__.__name__	,   '''NewTokenizer'''      )



 def 		UpperCamelCase__  (			self      ):
       with self.assertRaisesRegex(
           _UpperCAmelCase	,   '''bert-base is not a local folder and is not a valid model identifier'''      ):
             snake_case_ =   AutoTokenizer.from_pretrained('''bert-base'''      )



 def 		UpperCamelCase__  (			self      ):
       with self.assertRaisesRegex(
           _UpperCAmelCase	,   R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''      ):
             snake_case_ =   AutoTokenizer.from_pretrained(_UpperCAmelCase	,   revision='''aaaaaa'''      )





 def 		UpperCamelCase__  (			self      ):
       # Make sure we have cached the tokenizer.
       snake_case_ =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )
       with RequestCounter() as counter:
             snake_case_ =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )
             self.assertEqual(counter.get_request_count	,   0      )
             self.assertEqual(counter.head_request_count	,   1      )
             self.assertEqual(counter.other_request_count	,   0      )



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					(SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      )->						Tuple:




      """simple docstring"""

      return field(default_factory=lambda: default      ,      metadata=SCREAMING_SNAKE_CASE      )







@dataclass
class lowerCAmelCase_		:

 '''simple docstring'''

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Optional[Any]:




      """simple docstring"""

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

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







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase      ):
       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(_UpperCAmelCase      )
             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 		UpperCamelCase__  (			self      ):
       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=_UpperCAmelCase	,   )
                   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(_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.'''

       snake_case_ =   title_str[:-4]
       snake_case_ =   '''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					()->						int:




      """simple docstring"""

      snake_case_ =   HfArgumentParser(SCREAMING_SNAKE_CASE      )
      snake_case_ =   parser.parse_args_into_dataclasses()[0]
      snake_case_ =   Plot(args=SCREAMING_SNAKE_CASE      )
      plot.plot()


if __name__ == "__main__":
   main()