

'''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__		(    SCREAMING_SNAKE_CASE   : int=None							, SCREAMING_SNAKE_CASE   : str=None    ):
  return field(default_factory=lambda: default							, metadata=__A    )



@dataclass
class 							lowercase_						:

  '''simple docstring'''





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




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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[str]    ):
  try:
    float(__A    )
    return True
  except ValueError:
    return False



class 							lowercase_						:

  '''simple docstring'''





  def __init__(  self ,			a_  )				->					List[Any]:


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

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





  def     snake_case_      (  self  )				->					Optional[int]:


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

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

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

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

        UpperCAmelCase    				=						np.asarray(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE  )[: len(__SCREAMING_SNAKE_CASE  )]
        plt.scatter(
            __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''  )
        plt.plot(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			'--'  )

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

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

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

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




def    lowerCamelCase__		(    ):
  UpperCAmelCase    				=						HfArgumentParser(__A    )
  UpperCAmelCase    				=						parser.parse_args_into_dataclasses()[0]
  UpperCAmelCase    				=						Plot(args=__A    )
  plot.plot()


if __name__ == "__main__":
  main()










'''simple docstring'''







import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCAmelCase									=   'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase									=   direct_transformers_import(PATH_TO_TRANSFORMERS)

__lowerCAmelCase									=   transformers.models.auto.configuration_auto.CONFIG_MAPPING

__lowerCAmelCase									=   {
    # used to compute the property `self.chunk_length`
    'EncodecConfig': ['overlap'],
    # used as `self.bert_model = BertModel(config, ...)`
    'DPRConfig': True,
    # not used in modeling files, but it's an important information
    'FSMTConfig': ['langs'],
    # used internally in the configuration class file
    'GPTNeoConfig': ['attention_types'],
    # used internally in the configuration class file
    'EsmConfig': ['is_folding_model'],
    # used during training (despite we don't have training script for these models yet)
    'Mask2FormerConfig': ['ignore_value'],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    'OneFormerConfig': ['ignore_value', 'norm'],
    # used during preprocessing and collation, see `collating_graphormer.py`
    'GraphormerConfig': ['spatial_pos_max'],
    # used internally in the configuration class file
    'T5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    'MT5Config': ['feed_forward_proj', 'tokenizer_class'],
    'UMT5Config': ['feed_forward_proj', 'tokenizer_class'],
    # used internally in the configuration class file
    'LongT5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    'SwitchTransformersConfig': ['feed_forward_proj'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'BioGptConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'GLPNConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'SegformerConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'CvtConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'PerceiverConfig': ['layer_norm_eps'],
    # used internally to calculate the feature size
    'InformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'TimeSeriesTransformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'AutoformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate `mlp_dim`
    'SamVisionConfig': ['mlp_ratio'],
    # For (head) training, but so far not implemented
    'ClapAudioConfig': ['num_classes'],
    # Not used, but providing useful information to users
    'SpeechT5HifiGanConfig': ['sampling_rate'],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        'CLIPSegConfig': True,
        'DeformableDetrConfig': True,
        'DetaConfig': True,
        'DinatConfig': True,
        'DonutSwinConfig': True,
        'EfficientFormerConfig': True,
        'FSMTConfig': True,
        'JukeboxConfig': True,
        'LayoutLMv2Config': True,
        'MaskFormerSwinConfig': True,
        'MT5Config': True,
        'NatConfig': True,
        'OneFormerConfig': True,
        'PerceiverConfig': True,
        'RagConfig': True,
        'SpeechT5Config': True,
        'SwinConfig': True,
        'Swin2SRConfig': True,
        'Swinv2Config': True,
        'SwitchTransformersConfig': True,
        'TableTransformerConfig': True,
        'TapasConfig': True,
        'TransfoXLConfig': True,
        'UniSpeechConfig': True,
        'UniSpeechSatConfig': True,
        'WavLMConfig': True,
        'WhisperConfig': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        'JukeboxPriorConfig': True,
        # TODO: @Younes (for `is_decoder`)
        'Pix2StructTextConfig': True,
    }
)





def 		_UpperCAmelCase (				__A				: Optional[Any]    ,		__A				: List[str]    ,		__A				: List[str]    ,		__A				: Union[str, Any]   ):
				a_		:	List[str]									=   False
				for attribute in attributes:
								for modeling_source in source_strings:
												# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
												if (
												    f'config.{attribute}' in modeling_source
												    or f'getattr(config, "{attribute}"' in modeling_source
												    or f'getattr(self.config, "{attribute}"' in modeling_source
												):
																a_		:	Tuple									=   True
												# Deal with multi-line cases
												elif (
												    re.search(
												        Rf'getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'    ,		__A    ,		)
												    is not None
												):
																a_		:	Union[str, Any]									=   True
												# `SequenceSummary` is called with `SequenceSummary(config)`
												elif attribute in [
												    "summary_type",
												    "summary_use_proj",
												    "summary_activation",
												    "summary_last_dropout",
												    "summary_proj_to_labels",
												    "summary_first_dropout",
												]:
																if "SequenceSummary" in modeling_source:
																				a_		:	Any									=   True
												if attribute_used:
																break
								if attribute_used:
												break

    # common and important attributes, even if they do not always appear in the modeling files
				a_		:	Tuple									=   [
				    '''bos_index''',
				    '''eos_index''',
				    '''pad_index''',
				    '''unk_index''',
				    '''mask_index''',
				    '''image_size''',
				    '''use_cache''',
				    '''out_features''',
				    '''out_indices''',
				]
				a_		:	Tuple									=   ['''encoder_no_repeat_ngram_size''']

				# Special cases to be allowed
				a_		:	Optional[int]									=   True
				if not attribute_used:
								a_		:	Optional[Any]									=   False
								for attribute in attributes:
												# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
												if attribute in ["is_encoder_decoder"] and default_value is True:
																a_		:	Dict									=   True
												elif attribute in ["tie_word_embeddings"] and default_value is False:
																a_		:	List[str]									=   True

												# Allow cases without checking the default value in the configuration class
												elif attribute in attributes_to_allow + attributes_used_in_generation:
																a_		:	Optional[int]									=   True
												elif attribute.endswith('''_token_id'''   ):
																a_		:	Tuple									=   True

												# configuration class specific cases
												if not case_allowed:
																a_		:	Dict									=   SPECIAL_CASES_TO_ALLOW.get(config_class.__name__    ,		[]   )
																a_		:	int									=   allowed_cases is True or attribute in allowed_cases

				return attribute_used or case_allowed





def 		_UpperCAmelCase (				__A				: Union[str, Any]   ):
				a_		:	str									=   dict(inspect.signature(config_class.__init__   ).parameters   )
				a_		:	List[str]									=   [x for x in list(signature.keys()   ) if x not in ['''self''', '''kwargs''']]
				a_		:	Optional[Any]									=   [signature[param].default for param in parameter_names]

				# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
				# as one variant is used, the test should pass
				a_		:	int									=   {}
				if len(config_class.attribute_map   ) > 0:
								a_		:	List[str]									=   {v: k for k, v in config_class.attribute_map.items()}

				# Get the path to modeling source files
				a_		:	Tuple									=   inspect.getsourcefile(__A   )
				a_		:	str									=   os.path.dirname(__A   )
				# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
				a_		:	Tuple									=   [os.path.join(__A    ,		__A   ) for fn in os.listdir(__A   ) if fn.startswith('''modeling_'''   )]

				# Get the source code strings
				a_		:	Dict									=   []
				for path in modeling_paths:
								if os.path.isfile(__A   ):
												with open(__A   ) as fp:
																modeling_sources.append(fp.read()   )

				a_		:	List[Any]									=   []
				for config_param, default_value in zip(__A    ,		__A   ):
								# `attributes` here is all the variant names for `config_param`
								a_		:	int									=   [config_param]
								# some configuration classes have non-empty `attribute_map`, and both names could be used in the
								# corresponding modeling files. As long as one of them appears, it is fine.
								if config_param in reversed_attribute_map:
												attributes.append(reversed_attribute_map[config_param]   )

								if not check_attribute_being_used(__A    ,		__A    ,		__A    ,		__A   ):
												unused_attributes.append(attributes[0]   )

				return sorted(__A   )





def 		_UpperCAmelCase (				):
				a_		:	Optional[int]									=   {}
				for _config_class in list(CONFIG_MAPPING.values()   ):
								# Skip deprecated models
								if "models.deprecated" in _config_class.__module__:
												continue
								# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
								a_		:	int									=   [
								    cls
								    for name, cls in inspect.getmembers(
								        inspect.getmodule(_config_class   )    ,		lambda __A   : inspect.isclass(__A   )
								        and issubclass(__A    ,		__A   )
								        and inspect.getmodule(__A   ) == inspect.getmodule(_config_class   )    ,		)
								]
								for config_class in config_classes_in_module:
												a_		:	int									=   check_config_attributes_being_used(__A   )
												if len(__A   ) > 0:
																a_		:	Dict									=   unused_attributes

				if len(__A   ) > 0:
								a_		:	Dict									=   '''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
								for name, attributes in configs_with_unused_attributes.items():
												error += f'{name}: {attributes}\n'

								raise ValueError(__A   )


if __name__ == "__main__":
		check_config_attributes()




