

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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int=None					, lowerCAmelCase:					Union[str, Any]=None	)				->    Union[str, Any]:
    return field(default_factory=lambda: default					, metadata=lowerCAmelCase	)

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







def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int	)				->    Union[str, Any]:
    try:
        int(lowerCAmelCase	)
        return True
    except ValueError:
        return False

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	)				->    Optional[Any]:
    try:
        float(lowerCAmelCase	)
        return True
    except ValueError:
        return False

class 			a					:


      def __init__(    self  ,   A_		):







          '''simple docstring'''


          _UpperCAmelCase    :						List[str]							=      args
          _UpperCAmelCase    :						Optional[int]							=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

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





      def     _UpperCAmelCase			(    self		):







          '''simple docstring'''


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

              ((_UpperCAmelCase)						,							(_UpperCAmelCase))    :						Optional[Any]							=      (
                  (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
              )

              _UpperCAmelCase    :						Optional[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:
                      _UpperCAmelCase    :						Dict							=      np.asarray(
                          [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]  ,   dtype=__lowerCAmelCase  ,   )
                  else:
                      _UpperCAmelCase    :						List[Any]							=      np.asarray(
                          [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]  ,   dtype=np.floataa  ,   )

                  ((_UpperCAmelCase)						,							(_UpperCAmelCase))    :						Optional[Any]							=      (
                      ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                  )

                  _UpperCAmelCase    :						List[str]							=      np.asarray(__lowerCAmelCase  ,   __lowerCAmelCase		)[: len(__lowerCAmelCase		)]
                  plt.scatter(
                      __lowerCAmelCase  ,   __lowerCAmelCase  ,   label=f'{label_model_name} - {inner_loop_label}: {inner_loop_value}'		)
                  plt.plot(__lowerCAmelCase  ,   __lowerCAmelCase  ,   "--"		)

              title_str += f' {label_model_name} vs.'

          _UpperCAmelCase    :						Any							=      title_str[:-4]
          _UpperCAmelCase    :						Dict							=      "Time in s" if self.args.is_time else "Memory in MB"

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

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







def 							__SCREAMING_SNAKE_CASE							(			)				->    Optional[int]:
    _UpperCAmelCase    :						int							=      HfArgumentParser(lowerCAmelCase	)
    _UpperCAmelCase    :						int							=      parser.parse_args_into_dataclasses()[0]
    _UpperCAmelCase    :						Optional[Any]							=      Plot(args=lowerCAmelCase	)
    plot.plot()


if __name__ == "__main__":
     main()





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class 			a					(		UpperCAmelCase			):
						_lowercase        =     ["image_processor", "tokenizer"]
						_lowercase        =     "OwlViTImageProcessor"
						_lowercase        =     ("CLIPTokenizer", "CLIPTokenizerFast")


						def __init__(    self  ,   A_=None  ,   A_=None  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."  ,   A_  ,   )
														_UpperCAmelCase    :						Union[str, Any]							=      kwargs.pop("feature_extractor"		)

										_UpperCAmelCase    :						Any							=      image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError("You need to specify an `image_processor`."		)
										if tokenizer is None:
														raise ValueError("You need to specify a `tokenizer`."		)

										super().__init__(A_  ,   A_		)


						def __call__(    self  ,   A_=None  ,   A_=None  ,   A_=None  ,   A_="max_length"  ,   A_="np"  ,   **A_		):







										'''simple docstring'''


										if text is None and query_images is None and images is None:
														raise ValueError(
														    "You have to specify at least one text or query image or image. All three cannot be none."		)

										if text is not None:
														if isinstance(A_  ,   A_		) or (isinstance(A_  ,   A_		) and not isinstance(text[0]  ,   A_		)):
																		_UpperCAmelCase    :						Optional[int]							=      [self.tokenizer(A_  ,   padding=A_  ,   return_tensors=A_  ,   **A_		)]

														elif isinstance(A_  ,   A_		) and isinstance(text[0]  ,   A_		):
																		_UpperCAmelCase    :						Optional[int]							=      []

																		# Maximum number of queries across batch
																		_UpperCAmelCase    :						Optional[Any]							=      max([len(A_		) for t in text]		)

																		# Pad all batch samples to max number of text queries
																		for t in text:
																						if len(A_		) != max_num_queries:
																										_UpperCAmelCase    :						Optional[int]							=      t + [" "] * (max_num_queries - len(A_		))

																						_UpperCAmelCase    :						str							=      self.tokenizer(A_  ,   padding=A_  ,   return_tensors=A_  ,   **A_		)
																						encodings.append(A_		)
														else:
																		raise TypeError("Input text should be a string, a list of strings or a nested list of strings"		)

														if return_tensors == "np":
																		_UpperCAmelCase    :						List[str]							=      np.concatenate([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Tuple							=      np.concatenate([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														elif return_tensors == "jax" and is_flax_available():
																		import jax.numpy as jnp

																		_UpperCAmelCase    :						Optional[Any]							=      jnp.concatenate([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						str							=      jnp.concatenate([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														elif return_tensors == "pt" and is_torch_available():
																		import torch

																		_UpperCAmelCase    :						str							=      torch.cat([encoding["input_ids"] for encoding in encodings]  ,   dim=0		)
																		_UpperCAmelCase    :						Dict							=      torch.cat([encoding["attention_mask"] for encoding in encodings]  ,   dim=0		)

														elif return_tensors == "tf" and is_tf_available():
																		import tensorflow as tf

																		_UpperCAmelCase    :						Union[str, Any]							=      tf.stack([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Optional[int]							=      tf.stack([encoding["attention_mask"] for encoding in encodings]  ,   axis=0		)

														else:
																		raise ValueError("Target return tensor type could not be returned"		)

														_UpperCAmelCase    :						Optional[int]							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      input_ids
														_UpperCAmelCase    :						Optional[Any]							=      attention_mask

										if query_images is not None:
														_UpperCAmelCase    :						int							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      self.image_processor(
														    A_  ,   return_tensors=A_  ,   **A_		).pixel_values
														_UpperCAmelCase    :						Optional[Any]							=      query_pixel_values

										if images is not None:
														_UpperCAmelCase    :						int							=      self.image_processor(A_  ,   return_tensors=A_  ,   **A_		)

										if text is not None and images is not None:
														_UpperCAmelCase    :						Optional[int]							=      image_features.pixel_values
														return encoding
										elif query_images is not None and images is not None:
														_UpperCAmelCase    :						Any							=      image_features.pixel_values
														return encoding
										elif text is not None or query_images is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**A_		)  ,   tensor_type=A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process_object_detection(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.image_processor.post_process_image_guided_detection(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.tokenizer.batch_decode(*A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


										return self.tokenizer.decode(*A_  ,   **A_		)


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,   A_  ,   )
										return self.image_processor_class





						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										warnings.warn(
										    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,   A_  ,   )
										return self.image_processor


