


"""simple docstring"""







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Dict					      =    ["image_processor", "tokenizer"]
	snake_case__       :						int					      =    "BlipImageProcessor"
	snake_case__       :						str					      =    ("BertTokenizer", "BertTokenizerFast")




	def __init__(				self    :       Union[str, Any]    ,    UpperCAmelCase__    :       List[str]    ,    UpperCAmelCase__    :       Any     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       False
						super().__init__(UpperCAmelCase__    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       self.image_processor




	def __call__(				self    :       Optional[int]    ,    UpperCAmelCase__    :       ImageInput = None    ,    UpperCAmelCase__    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Union[bool, str, PaddingStrategy] = False    ,    UpperCAmelCase__    :       Union[bool, str, TruncationStrategy] = None    ,    UpperCAmelCase__    :       Optional[int] = None    ,    UpperCAmelCase__    :       int = 0    ,    UpperCAmelCase__    :       Optional[int] = None    ,    UpperCAmelCase__    :       Optional[bool] = None    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Optional[Union[str, TensorType]] = None    ,    **UpperCAmelCase__    :       List[Any]    ,    ) ->			BatchEncoding:
						if images is None and text is None:
											raise ValueError("You have to specify either images or text."     )

						# Get only text
						if images is None:
											__SCREAMING_SNAKE_CASE		     =       self.tokenizer
											__SCREAMING_SNAKE_CASE		     =       self.tokenizer(
											    text=UpperCAmelCase__    ,    add_special_tokens=UpperCAmelCase__    ,    padding=UpperCAmelCase__    ,    truncation=UpperCAmelCase__    ,    max_length=UpperCAmelCase__    ,    stride=UpperCAmelCase__    ,    pad_to_multiple_of=UpperCAmelCase__    ,    return_attention_mask=UpperCAmelCase__    ,    return_overflowing_tokens=UpperCAmelCase__    ,    return_special_tokens_mask=UpperCAmelCase__    ,    return_offsets_mapping=UpperCAmelCase__    ,    return_token_type_ids=UpperCAmelCase__    ,    return_length=UpperCAmelCase__    ,    verbose=UpperCAmelCase__    ,    return_tensors=UpperCAmelCase__    ,    **UpperCAmelCase__    ,    )
											return text_encoding

						# add pixel_values
						__SCREAMING_SNAKE_CASE		     =       self.image_processor(UpperCAmelCase__    ,    return_tensors=UpperCAmelCase__     )

						if text is not None:
											__SCREAMING_SNAKE_CASE		     =       self.tokenizer(
											    text=UpperCAmelCase__    ,    add_special_tokens=UpperCAmelCase__    ,    padding=UpperCAmelCase__    ,    truncation=UpperCAmelCase__    ,    max_length=UpperCAmelCase__    ,    stride=UpperCAmelCase__    ,    pad_to_multiple_of=UpperCAmelCase__    ,    return_attention_mask=UpperCAmelCase__    ,    return_overflowing_tokens=UpperCAmelCase__    ,    return_special_tokens_mask=UpperCAmelCase__    ,    return_offsets_mapping=UpperCAmelCase__    ,    return_token_type_ids=UpperCAmelCase__    ,    return_length=UpperCAmelCase__    ,    verbose=UpperCAmelCase__    ,    return_tensors=UpperCAmelCase__    ,    **UpperCAmelCase__    ,    )
						else:
											__SCREAMING_SNAKE_CASE		     =       None

						if text_encoding is not None:
											encoding_image_processor.update(UpperCAmelCase__     )

						return encoding_image_processor




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    *UpperCAmelCase__    :       Dict    ,    **UpperCAmelCase__    :       Optional[Any]     ) ->			str:
						return self.tokenizer.batch_decode(*UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    *UpperCAmelCase__    :       Tuple    ,    **UpperCAmelCase__    :       Optional[Any]     ) ->			Dict:
						return self.tokenizer.decode(*UpperCAmelCase__    ,    **UpperCAmelCase__     )




	@property
	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       self.tokenizer.model_input_names
						__SCREAMING_SNAKE_CASE		     =       self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )




"""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      UpperCAmelCase__ (lowerCAmelCase_=None						,		lowerCAmelCase_=None     ):




					'''simple docstring'''


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




@dataclass
class 				UpperCamelCase_			:

	"""simple docstring"""





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




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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




class 				UpperCamelCase_			:

	"""simple docstring"""





	def __init__(				self    :       str    ,    UpperCAmelCase__    :       Dict     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       args
						__SCREAMING_SNAKE_CASE		     =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}     )

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




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       plt.subplots()
						__SCREAMING_SNAKE_CASE		     =       "Time usage" if self.args.is_time else "Memory usage"
						__SCREAMING_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()     ):
											__SCREAMING_SNAKE_CASE		     =       sorted(set(self.result_dict[model_name]["bsz"]     )     )
											__SCREAMING_SNAKE_CASE		     =       sorted(set(self.result_dict[model_name]["seq_len"]     )     )
											__SCREAMING_SNAKE_CASE		     =       self.result_dict[model_name]["result"]

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

											__SCREAMING_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:
																					__SCREAMING_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:
																					__SCREAMING_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    ,    )

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

																__SCREAMING_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."""

						__SCREAMING_SNAKE_CASE		     =       title_str[:-4]
						__SCREAMING_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      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       HfArgumentParser(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       parser.parse_args_into_dataclasses()[0]
					__SCREAMING_SNAKE_CASE		     =       Plot(args=lowerCAmelCase_     )
					plot.plot()


if __name__ == "__main__":
		main()
