





"""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 					lowercase__    (					lowercase_=None		,lowercase_=None			)					->		int:




      """simple docstring"""

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



@dataclass
class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:str	     =					field(
      metadata={"help": "The csv file to plot."}		,  )
  SCREAMING_SNAKE_CASE__							:bool	     =					field(
      default=_UpperCamelCase		,  metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}		,  )
  SCREAMING_SNAKE_CASE__							:bool	     =					field(
      default=_UpperCamelCase		,  metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}		,  )
  SCREAMING_SNAKE_CASE__							:bool	     =					field(
      default=_UpperCamelCase		,  metadata={"help": "Disable logarithmic scale when plotting"}		,  )
  SCREAMING_SNAKE_CASE__							:bool	     =					field(
      default=_UpperCamelCase		,  metadata={
          "help": "Whether the csv file has training results or inference results. Defaults to inference results."
      }		,  )
  SCREAMING_SNAKE_CASE__							:Optional[str]	     =					field(
      default=_UpperCamelCase		,  metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}		,  )
  SCREAMING_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 					lowercase__    (					lowercase_			)					->		Dict:




      """simple docstring"""

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




def 					lowercase__    (					lowercase_			)					->		Any:




      """simple docstring"""

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



class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def __init__(		self   :       int					,   __a   :       Dict	)		->				Optional[int]:
        _UpperCamelCase      :	List[str]						=       args
        _UpperCamelCase      :	List[str]						=       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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


  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Optional[Any]:
        _UpperCamelCase,  _UpperCamelCase      :	Dict						=       plt.subplots()
        _UpperCamelCase      :	List[str]						=       "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      :	Optional[Any]						=       sorted(set(self.result_dict[model_name]["bsz"]	)	)
              _UpperCamelCase      :	Dict						=       sorted(set(self.result_dict[model_name]["seq_len"]	)	)
              _UpperCamelCase      :	Any						=       self.result_dict[model_name]["result"]

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

              _UpperCamelCase      :	Tuple						=       (
                  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      :	List[Any]						=       np.asarray(
                              [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,   dtype=__a					,   )
                    else:
                          _UpperCamelCase      :	int						=       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))      :	Union[str, Any]						=       (
                        ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                    )

                    _UpperCamelCase      :	str						=       np.asarray(__a					,   __a	)[: len(__a	)]
                    plt.scatter(
                        __a					,   __a					,   label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''	)
                    plt.plot(__a					,   __a					,   "--"	)

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

        _UpperCamelCase      :	Tuple						=       title_str[:-4]
        _UpperCamelCase      :	int						=       "Time in s" if self.args.is_time else "Memory in MB"

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

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





def 					lowercase__    (					)					->		Optional[Any]:




      """simple docstring"""

      _UpperCamelCase      :	Optional[int]						=       HfArgumentParser(lowercase_			)
      _UpperCamelCase      :	List[str]						=       parser.parse_args_into_dataclasses()[0]
      _UpperCamelCase      :	Tuple						=       Plot(args=lowercase_			)
      plot.plot()


if __name__ == "__main__":
  main()







"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFLEDForConditionalGeneration, TFLEDModel



@require_tf
class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Any	     =					LEDConfig
  SCREAMING_SNAKE_CASE__							:str	     =					{}
  SCREAMING_SNAKE_CASE__							:List[str]	     =					"gelu"

  def __init__(		self   :       List[Any]					,   __a   :       Union[str, Any]					,   __a   :       List[Any]=13					,   __a   :       int=7					,   __a   :       str=True					,   __a   :       Any=False					,   __a   :       str=99					,   __a   :       str=32					,   __a   :       Union[str, Any]=2					,   __a   :       Optional[Any]=4					,   __a   :       List[Any]=37					,   __a   :       List[Any]=0.1					,   __a   :       Tuple=0.1					,   __a   :       Dict=20					,   __a   :       str=2					,   __a   :       Dict=1					,   __a   :       Any=0					,   __a   :       List[Any]=4					,   )		->				List[Any]:
        _UpperCamelCase      :	Optional[Any]						=       parent
        _UpperCamelCase      :	List[str]						=       batch_size
        _UpperCamelCase      :	str						=       seq_length
        _UpperCamelCase      :	str						=       is_training
        _UpperCamelCase      :	Any						=       use_labels
        _UpperCamelCase      :	Any						=       vocab_size
        _UpperCamelCase      :	List[str]						=       hidden_size
        _UpperCamelCase      :	Optional[Any]						=       num_hidden_layers
        _UpperCamelCase      :	Dict						=       num_attention_heads
        _UpperCamelCase      :	Optional[Any]						=       intermediate_size

        _UpperCamelCase      :	int						=       hidden_dropout_prob
        _UpperCamelCase      :	Dict						=       attention_probs_dropout_prob
        _UpperCamelCase      :	str						=       max_position_embeddings
        _UpperCamelCase      :	int						=       eos_token_id
        _UpperCamelCase      :	Dict						=       pad_token_id
        _UpperCamelCase      :	Optional[Any]						=       bos_token_id
        _UpperCamelCase      :	str						=       attention_window

        # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
        # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
        # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
        # because its local attention only attends to `self.attention_window` and one before and one after
        _UpperCamelCase      :	List[str]						=       self.attention_window + 2

        # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
        # the `test_attention_outputs` and `test_hidden_states_output` tests
        _UpperCamelCase      :	int						=       (
            self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
        )

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				str:
        _UpperCamelCase      :	Tuple						=       ids_tensor([self.batch_size, self.seq_length - 1]					,   self.vocab_size	)
        _UpperCamelCase      :	Optional[Any]						=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)					,   1	)
        _UpperCamelCase      :	Tuple						=       tf.concat([input_ids, eos_tensor]					,   axis=1	)

        _UpperCamelCase      :	Tuple						=       ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size	)

        _UpperCamelCase      :	List[Any]						=       self.config_cls(
            vocab_size=self.vocab_size					,   d_model=self.hidden_size					,   encoder_layers=self.num_hidden_layers					,   decoder_layers=self.num_hidden_layers					,   encoder_attention_heads=self.num_attention_heads					,   decoder_attention_heads=self.num_attention_heads					,   encoder_ffn_dim=self.intermediate_size					,   decoder_ffn_dim=self.intermediate_size					,   dropout=self.hidden_dropout_prob					,   attention_dropout=self.attention_probs_dropout_prob					,   max_position_embeddings=self.max_position_embeddings					,   eos_token_ids=[2]					,   bos_token_id=self.bos_token_id					,   pad_token_id=self.pad_token_id					,   decoder_start_token_id=self.pad_token_id					,   attention_window=self.attention_window					,   **self.config_updates					,   )
        _UpperCamelCase      :	Dict						=       prepare_led_inputs_dict(__a					,   __a					,   __a	)
        _UpperCamelCase      :	Union[str, Any]						=       tf.concat(
            [tf.zeros_like(__a	)[:, :-1], tf.ones_like(__a	)[:, -1:]]					,   axis=-1					,   )
        _UpperCamelCase      :	Union[str, Any]						=       global_attention_mask
        return config, inputs_dict


  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       List[Any]					,   __a   :       int	)		->				Tuple:
        _UpperCamelCase      :	Tuple						=       TFLEDModel(config=__a	).get_decoder()
        _UpperCamelCase      :	Tuple						=       inputs_dict["input_ids"]

        _UpperCamelCase      :	int						=       input_ids[:1, :]
        _UpperCamelCase      :	List[str]						=       inputs_dict["attention_mask"][:1, :]
        _UpperCamelCase      :	List[Any]						=       1

        # first forward pass
        _UpperCamelCase      :	Any						=       model(__a					,   attention_mask=__a					,   use_cache=__a	)

        _UpperCamelCase,  _UpperCamelCase      :	Union[str, Any]						=       outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        _UpperCamelCase      :	Any						=       ids_tensor((self.batch_size, 3)					,   config.vocab_size	)
        _UpperCamelCase      :	Any						=       tf.cast(ids_tensor((self.batch_size, 3)					,   2	)					,   tf.inta	)

        # append to next input_ids and
        _UpperCamelCase      :	List[str]						=       tf.concat([input_ids, next_tokens]					,   axis=-1	)
        _UpperCamelCase      :	Union[str, Any]						=       tf.concat([attention_mask, next_attn_mask]					,   axis=-1	)

        _UpperCamelCase      :	Tuple						=       model(__a					,   attention_mask=__a	)[0]
        _UpperCamelCase      :	int						=       model(__a					,   attention_mask=__a					,   past_key_values=__a	)[0]

        self.parent.assertEqual(next_tokens.shape[1]					,   output_from_past.shape[1]	)

        # select random slice
        _UpperCamelCase      :	List[Any]						=       int(ids_tensor((1,)					,   output_from_past.shape[-1]	)	)
        _UpperCamelCase      :	List[str]						=       output_from_no_past[:, -3:, random_slice_idx]
        _UpperCamelCase      :	Optional[int]						=       output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(__a					,   __a					,   rtol=1e-3	)





def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,lowercase_=None		,lowercase_=None		,lowercase_=None		,lowercase_=None		,)					->		Dict:




      """simple docstring"""

      if attention_mask is None:
            _UpperCamelCase      :	str						=       tf.cast(tf.math.not_equal(lowercase_		,config.pad_token_id			)		,tf.inta			)
      if decoder_attention_mask is None:
            _UpperCamelCase      :	str						=       tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape		,dtype=tf.inta			),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,config.pad_token_id			)		,tf.inta			),
                ]		,axis=-1		,)
      if head_mask is None:
            _UpperCamelCase      :	List[str]						=       tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
      if decoder_head_mask is None:
            _UpperCamelCase      :	List[str]						=       tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
      return {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
          "decoder_input_ids": decoder_input_ids,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
      }



@require_tf
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  _UpperCamelCase		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Any	     =					(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE__							:List[str]	     =					(TFLEDForConditionalGeneration,) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE__							:List[str]	     =					(
      {
          "conversational": TFLEDForConditionalGeneration,
          "feature-extraction": TFLEDModel,
          "summarization": TFLEDForConditionalGeneration,
          "text2text-generation": TFLEDForConditionalGeneration,
          "translation": TFLEDForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE__							:Tuple	     =					True
  SCREAMING_SNAKE_CASE__							:str	     =					False
  SCREAMING_SNAKE_CASE__							:Optional[Any]	     =					False
  SCREAMING_SNAKE_CASE__							:int	     =					False

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				List[Any]:
        _UpperCamelCase      :	int						=       TFLEDModelTester(self	)
        _UpperCamelCase      :	Any						=       ConfigTester(self					,   config_class=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Optional[Any]:
        self.config_tester.run_common_tests()

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				int:
        _UpperCamelCase      :	Tuple						=       self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				List[str]:
        _UpperCamelCase,  _UpperCamelCase      :	List[Any]						=       self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCamelCase      :	Optional[int]						=       tf.zeros_like(inputs_dict["attention_mask"]	)
        _UpperCamelCase      :	Union[str, Any]						=       2
        _UpperCamelCase      :	str						=       tf.where(
            tf.range(self.model_tester.seq_length	)[None, :] < num_global_attn_indices					,   1					,   inputs_dict["global_attention_mask"]					,   )

        _UpperCamelCase      :	Dict						=       True
        _UpperCamelCase      :	str						=       self.model_tester.seq_length
        _UpperCamelCase      :	Union[str, Any]						=       self.model_tester.encoder_seq_length

        def check_decoder_attentions_output(__a   :       Optional[int]	):
              _UpperCamelCase      :	Optional[int]						=       outputs.decoder_attentions
              self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)
              self.assertListEqual(
                  list(decoder_attentions[0].shape[-3:]	)					,   [self.model_tester.num_attention_heads, seq_length, seq_length]					,   )

        def check_encoder_attentions_output(__a   :       Optional[Any]	):
              _UpperCamelCase      :	Union[str, Any]						=       [t.numpy() for t in outputs.encoder_attentions]
              _UpperCamelCase      :	List[Any]						=       [t.numpy() for t in outputs.encoder_global_attentions]
              self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)
              self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)
              self.assertListEqual(
                  list(attentions[0].shape[-3:]	)					,   [self.model_tester.num_attention_heads, seq_length, seq_length]					,   )
              self.assertListEqual(
                  list(global_attentions[0].shape[-3:]	)					,   [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]					,   )

        for model_class in self.all_model_classes:
              _UpperCamelCase      :	Dict						=       True
              _UpperCamelCase      :	Optional[Any]						=       False
              _UpperCamelCase      :	int						=       False
              _UpperCamelCase      :	Optional[int]						=       model_class(__a	)
              _UpperCamelCase      :	int						=       model(self._prepare_for_class(__a					,   __a	)	)
              _UpperCamelCase      :	Any						=       len(__a	)
              self.assertEqual(config.output_hidden_states					,   __a	)
              check_encoder_attentions_output(__a	)

              if self.is_encoder_decoder:
                    _UpperCamelCase      :	Optional[Any]						=       model_class(__a	)
                    _UpperCamelCase      :	List[Any]						=       model(self._prepare_for_class(__a					,   __a	)	)
                    self.assertEqual(config.output_hidden_states					,   __a	)
                    check_decoder_attentions_output(__a	)

              # Check that output attentions can also be changed via the config
              del inputs_dict["output_attentions"]
              _UpperCamelCase      :	int						=       True
              _UpperCamelCase      :	Tuple						=       model_class(__a	)
              _UpperCamelCase      :	str						=       model(self._prepare_for_class(__a					,   __a	)	)
              self.assertEqual(config.output_hidden_states					,   __a	)
              check_encoder_attentions_output(__a	)

              # Check attention is always last and order is fine
              _UpperCamelCase      :	Any						=       True
              _UpperCamelCase      :	List[str]						=       True
              _UpperCamelCase      :	Tuple						=       model_class(__a	)
              _UpperCamelCase      :	int						=       model(self._prepare_for_class(__a					,   __a	)	)

              self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)					,   len(__a	)	)
              self.assertEqual(model.config.output_hidden_states					,   __a	)
              check_encoder_attentions_output(__a	)

  @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."	)
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Dict:
        pass


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Tuple:
        # TODO: Head-masking not yet implement
        pass





def 					lowercase__    (					lowercase_			)					->		Union[str, Any]:




      """simple docstring"""

      return tf.constant(lowercase_		,dtype=tf.intaa			)


lowerCamelCase__				  =   1E-4



@slow
@require_tf
class      __SCREAMING_SNAKE_CASE    (						unittest.TestCase   ):







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Union[str, Any]:
        _UpperCamelCase      :	Any						=       TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	).led

        # change to intended input here
        _UpperCamelCase      :	int						=       _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
        _UpperCamelCase      :	Tuple						=       _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
        _UpperCamelCase      :	Optional[Any]						=       prepare_led_inputs_dict(model.config					,   __a					,   __a	)
        _UpperCamelCase      :	Optional[int]						=       model(**__a	)[0]
        _UpperCamelCase      :	Optional[int]						=       (1, 1024, 768)
        self.assertEqual(output.shape					,   __a	)
        # change to expected output here
        _UpperCamelCase      :	Tuple						=       tf.convert_to_tensor(
            [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]					,   )
        tf.debugging.assert_near(output[:, :3, :3]					,   __a					,   atol=1e-3	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				str:
        _UpperCamelCase      :	Optional[int]						=       TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	)

        # change to intended input here
        _UpperCamelCase      :	Optional[int]						=       _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
        _UpperCamelCase      :	List[str]						=       _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]	)
        _UpperCamelCase      :	Optional[Any]						=       prepare_led_inputs_dict(model.config					,   __a					,   __a	)
        _UpperCamelCase      :	Union[str, Any]						=       model(**__a	)[0]
        _UpperCamelCase      :	int						=       (1, 1024, model.config.vocab_size)
        self.assertEqual(output.shape					,   __a	)
        # change to expected output here
        _UpperCamelCase      :	Optional[int]						=       tf.convert_to_tensor(
            [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]					,   )
        tf.debugging.assert_near(output[:, :3, :3]					,   __a					,   atol=1e-3					,   rtol=1e-3	)
