

"""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   _A  (  UpperCamelCase_					:	Union[str, Any]=None,      UpperCamelCase_					:	int=None)     ->						Tuple:
   '''simple docstring'''


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




@dataclass
class  _lowerCAmelCase  :







 """simple docstring"""







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







def   _A  (  UpperCamelCase_					:	Any)     ->						Dict:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	Optional[int])     ->						Optional[Any]:
   '''simple docstring'''


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




class  _lowerCAmelCase  :







 """simple docstring"""







 def __init__(      self							:      Optional[Any], UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						args
    __lowercase								=						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}     )

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



 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase		,__lowercase								=						plt.subplots()
    __lowercase								=						"Time usage" if self.args.is_time else "Memory usage"
    __lowercase								=						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()     ):
       __lowercase								=						sorted(set(self.result_dict[model_name]["bsz"]     )     )
       __lowercase								=						sorted(set(self.result_dict[model_name]["seq_len"]     )     )
       __lowercase								=						self.result_dict[model_name]["result"]

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

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

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

          __lowercase								=						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."""

    __lowercase								=						title_str[:-4]
    __lowercase								=						"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   _A  (  )     ->						List[str]:
   '''simple docstring'''


   __lowercase								=						HfArgumentParser(UpperCamelCase_)
   __lowercase								=						parser.parse_args_into_dataclasses()[0]
   __lowercase								=						Plot(args=UpperCamelCase_)
   plot.plot()


if __name__ == "__main__":
   main()







"""simple docstring"""





import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      List[str], UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Dict     ):
    __lowercase								=						dataset
    __lowercase								=						process
    __lowercase								=						params


 def __len__(      self							:      str     ):
    return len(self.dataset     )



 def __getitem__(      self							:      List[Any], UpperCAmelCase__							:      int     ):
    __lowercase								=						self.dataset[i]
    __lowercase								=						self.process(UpperCAmelCase__, **self.params     )
    return processed







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      Optional[Any], UpperCAmelCase__							:      Dict, UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      List[Any]=None     ):
    __lowercase								=						loader
    __lowercase								=						infer
    __lowercase								=						params
    if loader_batch_size == 1:
       # Let's spare some time by deactivating altogether
       __lowercase								=						None
    __lowercase								=						loader_batch_size

    # Internal bookkeeping
    __lowercase								=						None
    __lowercase								=						None


 def __len__(      self							:      str     ):
    return len(self.loader     )


 def __iter__(      self							:      List[str]     ):
    __lowercase								=						iter(self.loader     )
    return self


 def  _lowercase			(      self							:      Union[str, Any]     ):
    if isinstance(self._loader_batch_data, torch.Tensor     ):
       # Batch data is simple tensor, just fetch the slice
       __lowercase								=						self._loader_batch_data[self._loader_batch_index]
    else:
       # Batch data is assumed to be BaseModelOutput (or dict)
       __lowercase								=						{}
       for k, element in self._loader_batch_data.items():
          if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
             # Convert ModelOutput to tuple first
             __lowercase								=						element.to_tuple()
             if isinstance(element[0], torch.Tensor     ):
                __lowercase								=						tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
             elif isinstance(element[0], np.ndarray     ):
                __lowercase								=						tuple(np.expand_dims(el[self._loader_batch_index], 0     ) for el in element     )
             continue
          if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
             # Those are stored as lists of tensors so need specific unbatching.
             if isinstance(element[0], torch.Tensor     ):
                __lowercase								=						tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
             elif isinstance(element[0], np.ndarray     ):
                __lowercase								=						tuple(np.expand_dims(el[self._loader_batch_index], 0     ) for el in element     )
             continue
          if element is None:
             # This can happen for optional data that get passed around
             __lowercase								=						None
          elif isinstance(element[self._loader_batch_index], torch.Tensor     ):
             # Take correct batch data, but make it looked like batch_size=1
             # For compatibility with other methods within transformers

             __lowercase								=						element[self._loader_batch_index].unsqueeze(0     )
          elif isinstance(element[self._loader_batch_index], np.ndarray     ):
             # Take correct batch data, but make it looked like batch_size=1
             # For compatibility with other methods within transformers
             __lowercase								=						np.expand_dims(element[self._loader_batch_index], 0     )
          else:
             # This is typically a list, so no need to `unsqueeze`.
             __lowercase								=						element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
       __lowercase								=						self._loader_batch_data.__class__(UpperCAmelCase__     )
    self._loader_batch_index += 1
    return result



 def  _lowercase			(      self							:      Tuple     ):
    if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
       # We are currently unrolling a batch so we just need to return
       # the current item within a batch
       return self.loader_batch_item()

    # We're out of items within a batch
    __lowercase								=						next(self.iterator     )
    __lowercase								=						self.infer(UpperCAmelCase__, **self.params     )
    # We now have a batch of "inferred things".
    if self.loader_batch_size is not None:
       # Try to infer the size of the batch
       if isinstance(UpperCAmelCase__, torch.Tensor     ):
          __lowercase								=						processed
       else:
          __lowercase								=						list(processed.keys()     )[0]
          __lowercase								=						processed[key]
       if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
          __lowercase								=						len(UpperCAmelCase__     )
       else:
          __lowercase								=						first_tensor.shape[0]
       if 0 < observed_batch_size < self.loader_batch_size:
          # could be last batch so we can't unroll as many
          # elements.
          __lowercase								=						observed_batch_size
       # Setting internal index to unwrap the batch
       __lowercase								=						processed
       __lowercase								=						0
       return self.loader_batch_item()
    else:
       # We're not unrolling batches
       return processed







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      Union[str, Any], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      List[str], UpperCAmelCase__							:      int, UpperCAmelCase__							:      str=None     ):
    super().__init__(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__     )


 def __iter__(      self							:      str     ):
    __lowercase								=						iter(self.loader     )
    __lowercase								=						None
    return self



 def  _lowercase			(      self							:      int     ):
    if self.subiterator is None:
       __lowercase								=						self.infer(next(self.iterator     ), **self.params     )
    try:
       # Try to return next item
       __lowercase								=						next(self.subiterator     )
    except StopIteration:
       # When a preprocess iterator ends, we can start lookig at the next item
       # ChunkIterator will keep feeding until ALL elements of iterator
       # all have created their subiterator and have been iterating against.
       #
       # Another way to look at it, is we're basically flattening lists of lists
       # into a single list, but with generators
       __lowercase								=						self.infer(next(self.iterator     ), **self.params     )
       __lowercase								=						next(self.subiterator     )
    return processed







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __iter__(      self							:      int     ):
    __lowercase								=						iter(self.loader     )
    return self



 def  _lowercase			(      self							:      List[str]     ):
    # Extremely similar to PipelineIterator in its unpacking mechanism
    # BUT, we have an extra required item which is the presence of `is_last`
    # That is because everything is flattened by `PipelineChunkIterator` we
    # need to keep track of how to regroup here in the original `process`
    # boundaries so that `process` and `postprocess` see the same data.

    # This iterator accumulates items (possibly while unbatching) until it
    # its a `is_last` and then just passes it on to the caller.
    __lowercase								=						False
    __lowercase								=						[]
    if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
       while self._loader_batch_index < self.loader_batch_size:
          __lowercase								=						self.loader_batch_item()
          __lowercase								=						item.pop("is_last"     )
          accumulator.append(UpperCAmelCase__     )
          if is_last:
             return accumulator

    while not is_last:
       __lowercase								=						self.infer(next(self.iterator     ), **self.params     )
       if self.loader_batch_size is not None:
          if isinstance(UpperCAmelCase__, torch.Tensor     ):
             __lowercase								=						processed
          else:
             __lowercase								=						list(processed.keys()     )[0]
             __lowercase								=						processed[key]
          if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
             __lowercase								=						len(UpperCAmelCase__     )
          else:
             __lowercase								=						first_tensor.shape[0]
          if 0 < observed_batch_size < self.loader_batch_size:
             # could be last batch so we can't unroll as many
             # elements.
             __lowercase								=						observed_batch_size
          __lowercase								=						processed
          __lowercase								=						0
          while self._loader_batch_index < self.loader_batch_size:
             __lowercase								=						self.loader_batch_item()
             __lowercase								=						item.pop("is_last"     )
             accumulator.append(UpperCAmelCase__     )
             if is_last:
                return accumulator
       else:
          __lowercase								=						processed
          __lowercase								=						item.pop("is_last"     )
          accumulator.append(UpperCAmelCase__     )
    return accumulator







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      List[Any], UpperCAmelCase__							:      Dataset, UpperCAmelCase__							:      str     ):
    __lowercase								=						dataset
    __lowercase								=						key


 def __len__(      self							:      Optional[Any]     ):
    return len(self.dataset     )



 def __getitem__(      self							:      Union[str, Any], UpperCAmelCase__							:      Any     ):
    return self.dataset[i][self.key]







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      str, UpperCAmelCase__							:      Dataset, UpperCAmelCase__							:      str, UpperCAmelCase__							:      str     ):
    __lowercase								=						dataset
    __lowercase								=						keya
    __lowercase								=						keya


 def __len__(      self							:      Optional[int]     ):
    return len(self.dataset     )



 def __getitem__(      self							:      Dict, UpperCAmelCase__							:      Tuple     ):
    return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




