

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_(							_snake_case   :  Dict=None       ,     _snake_case   :  List[Any]=None	):




			"""simple docstring"""






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






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






def        UpperCamelCase_(							_snake_case   :  Any	):




			"""simple docstring"""






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






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]	):




			"""simple docstring"""






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






class 	__magic_name__    :





			def __init__(		self					,  __snake_case					)   -> List[str]:




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

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





			def 		__magic_name__    (		self					)   -> Tuple:




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

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

									__a        =(
									    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        =np.asarray(
															    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,  dtype=__snake_case					,  )
												else:
															__a        =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))        =(
												    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
												)

												__a        =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.'

						__a        =title_str[:-4]
						__a        ='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_(							):




			"""simple docstring"""






			__a        =HfArgumentParser(_snake_case	)
			__a        =parser.parse_args_into_dataclasses()[0]
			__a        =Plot(args=_snake_case	)
			plot.plot()


if __name__ == "__main__":
						main()









import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase					:			Optional[int]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			List[str]  				=			{
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'git_vision_model'





			def __init__(		self					,  __snake_case=768					,  __snake_case=3072					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3					,  __snake_case=224					,  __snake_case=16					,  __snake_case="quick_gelu"					,  __snake_case=1e-5					,  __snake_case=0.0					,  __snake_case=0.02					,  **__snake_case					,  )   -> int:




						'''simple docstring'''
						super().__init__(**__snake_case					)

						__a        =hidden_size
						__a        =intermediate_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =num_channels
						__a        =patch_size
						__a        =image_size
						__a        =initializer_range
						__a        =attention_dropout
						__a        =layer_norm_eps
						__a        =hidden_act





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__snake_case					)

						# get the vision config dict if we are loading from GITConfig
						if config_dict.get('model_type'					) == "git":
									__a        =config_dict['vision_config']

						if "model_type" in config_dict and hasattr(cls					,  'model_type'					) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
									    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

						return cls.from_dict(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'git'





			def __init__(		self					,  __snake_case=None					,  __snake_case=3_0522					,  __snake_case=768					,  __snake_case=6					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1024					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0					,  __snake_case="absolute"					,  __snake_case=True					,  __snake_case=False					,  __snake_case=101					,  __snake_case=102					,  __snake_case=None					,  **__snake_case					,  )   -> Optional[int]:




						'''simple docstring'''
						super().__init__(bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  pad_token_id=__snake_case					,  **__snake_case					)

						if vision_config is None:
									__a        ={}
									logger.info('vision_config is None. initializing the GitVisionConfig with default values.'					)

						__a        =GitVisionConfig(**__snake_case					)
						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =position_embedding_type
						__a        =use_cache
						__a        =tie_word_embeddings
						__a        =num_image_with_embedding

						__a        =bos_token_id
						__a        =eos_token_id





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =copy.deepcopy(self.__dict__					)
						__a        =self.vision_config.to_dict()
						__a        =self.__class__.model_type
						return output






