


'''simple docstring'''


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
				import tensorflow as tf

				from transformers.models.swin.modeling_tf_swin import (
				    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSwinForImageClassification,
				    TFSwinForMaskedImageModeling,
				    TFSwinModel,
				)


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor





class 					lowercase__						:





							'''simple docstring'''


							def __init__(					self					,      __snake_case					,      __snake_case=13					,      __snake_case=32					,      __snake_case=2					,      __snake_case=3					,      __snake_case=16					,      __snake_case=[1, 2, 1]					,      __snake_case=[2, 2, 4]					,      __snake_case=2					,      __snake_case=2.0					,      __snake_case=True					,      __snake_case=0.0					,      __snake_case=0.0					,      __snake_case=0.1					,      __snake_case="gelu"					,      __snake_case=False					,      __snake_case=True					,      __snake_case=0.02					,      __snake_case=1e-5					,      __snake_case=True					,      __snake_case=None					,      __snake_case=True					,      __snake_case=10					,      __snake_case=8					,      ):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   parent
											_SCREAMING_SNAKE_CASE				: Tuple  	=   batch_size
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   image_size
											_SCREAMING_SNAKE_CASE				: int  	=   patch_size
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   num_channels
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   embed_dim
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   depths
											_SCREAMING_SNAKE_CASE				: Any  	=   num_heads
											_SCREAMING_SNAKE_CASE				: List[str]  	=   window_size
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   mlp_ratio
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   qkv_bias
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   hidden_dropout_prob
											_SCREAMING_SNAKE_CASE				: List[str]  	=   attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE				: str  	=   drop_path_rate
											_SCREAMING_SNAKE_CASE				: List[str]  	=   hidden_act
											_SCREAMING_SNAKE_CASE				: str  	=   use_absolute_embeddings
											_SCREAMING_SNAKE_CASE				: Tuple  	=   patch_norm
											_SCREAMING_SNAKE_CASE				: Tuple  	=   layer_norm_eps
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   initializer_range
											_SCREAMING_SNAKE_CASE				: int  	=   is_training
											_SCREAMING_SNAKE_CASE				: Any  	=   scope
											_SCREAMING_SNAKE_CASE				: Dict  	=   use_labels
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   type_sequence_label_size
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   encoder_stride






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

											_SCREAMING_SNAKE_CASE				: Tuple  	=   None
											if self.use_labels:
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   ids_tensor([self.batch_size]					,      self.type_sequence_label_size						)

											_SCREAMING_SNAKE_CASE				: int  	=   self.get_config()

											return config, pixel_values, labels






							def UpperCAmelCase_			(					self						):
											return SwinConfig(
											    image_size=self.image_size					,      patch_size=self.patch_size					,      num_channels=self.num_channels					,      embed_dim=self.embed_dim					,      depths=self.depths					,      num_heads=self.num_heads					,      window_size=self.window_size					,      mlp_ratio=self.mlp_ratio					,      qkv_bias=self.qkv_bias					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      drop_path_rate=self.drop_path_rate					,      hidden_act=self.hidden_act					,      use_absolute_embeddings=self.use_absolute_embeddings					,      path_norm=self.patch_norm					,      layer_norm_eps=self.layer_norm_eps					,      initializer_range=self.initializer_range					,      encoder_stride=self.encoder_stride					,      )






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   TFSwinModel(config=__snake_case						)
											_SCREAMING_SNAKE_CASE				: Tuple  	=   model(__snake_case						)

											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
											_SCREAMING_SNAKE_CASE				: Any  	=   int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

											self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, expected_seq_len, expected_dim)						)






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Dict  	=   TFSwinForMaskedImageModeling(config=__snake_case						)
											_SCREAMING_SNAKE_CASE				: Tuple  	=   model(__snake_case						)
											self.parent.assertEqual(
											    result.logits.shape					,      (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

											# test greyscale images
											_SCREAMING_SNAKE_CASE				: List[str]  	=   1
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   TFSwinForMaskedImageModeling(__snake_case						)

											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   model(__snake_case						)
											self.parent.assertEqual(result.logits.shape					,      (self.batch_size, 1, self.image_size, self.image_size)						)






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.type_sequence_label_size
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   TFSwinForImageClassification(__snake_case						)
											_SCREAMING_SNAKE_CASE				: str  	=   model(__snake_case					,      labels=__snake_case						)
											self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)						)

											# test greyscale images
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   1
											_SCREAMING_SNAKE_CASE				: List[str]  	=   TFSwinForImageClassification(__snake_case						)
											_SCREAMING_SNAKE_CASE				: List[str]  	=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
											_SCREAMING_SNAKE_CASE				: int  	=   model(__snake_case						)
											self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: str  	=   self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   config_and_inputs
											_SCREAMING_SNAKE_CASE				: Dict  	=   {"""pixel_values""": pixel_values}
											return config, inputs_dict





@require_tf
class 					lowercase__						(		_snake_case  ,  _snake_case  ,  unittest.TestCase      ):





							'''simple docstring'''


							A_							:   Dict   			=						(
							    (
							        TFSwinModel,
							        TFSwinForImageClassification,
							        TFSwinForMaskedImageModeling,
							    )
							    if is_tf_available()
							    else ()
							)
							A_							:   Optional[int]   			=						(
							    {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
							    if is_tf_available()
							    else {}
							)

							A_							:   Optional[Any]   			=						False
							A_							:   Dict   			=						False
							A_							:   Any   			=						False
							A_							:   str   			=						False






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: int  	=   TFSwinModelTester(self						)
											_SCREAMING_SNAKE_CASE				: Dict  	=   ConfigTester(self					,      config_class=__snake_case					,      embed_dim=37						)






							def UpperCAmelCase_			(					self						):
											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()






							def UpperCAmelCase_			(					self						):
											return






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: int  	=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Any  	=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*__snake_case						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__snake_case						)






							@unittest.skip(reason="""Swin does not use inputs_embeds"""						)
							def UpperCAmelCase_			(					self						):
											pass






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Dict  	=   self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE				: List[str]  	=   model_class(__snake_case						)
															self.assertIsInstance(model.get_input_embeddings()					,      tf.keras.layers.Layer						)
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(__snake_case					,      tf.keras.layers.Dense						)						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Any  	=   self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE				: int  	=   model_class(__snake_case						)
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   inspect.signature(model.call						)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															_SCREAMING_SNAKE_CASE				: int  	=   [*signature.parameters.keys()]

															_SCREAMING_SNAKE_CASE				: str  	=   ["""pixel_values"""]
															self.assertListEqual(arg_names[:1]					,      __snake_case						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Dict  	=   self.model_tester.prepare_config_and_inputs_for_common()
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   True

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE				: Dict  	=   True
															_SCREAMING_SNAKE_CASE				: Dict  	=   False
															_SCREAMING_SNAKE_CASE				: List[str]  	=   True
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   model_class(__snake_case						)
															_SCREAMING_SNAKE_CASE				: Tuple  	=   model(**self._prepare_for_class(__snake_case					,      __snake_case						)						)
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   outputs.attentions
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   len(self.model_tester.depths						)
															self.assertEqual(len(__snake_case						)					,      __snake_case						)

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															_SCREAMING_SNAKE_CASE				: str  	=   True
															_SCREAMING_SNAKE_CASE				: Any  	=   config.window_size**2
															_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   model_class(__snake_case						)
															_SCREAMING_SNAKE_CASE				: int  	=   model(**self._prepare_for_class(__snake_case					,      __snake_case						)						)
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   outputs.attentions
															self.assertEqual(len(__snake_case						)					,      __snake_case						)

															self.assertListEqual(
															    list(attentions[0].shape[-3:]						)					,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,      )
															_SCREAMING_SNAKE_CASE				: Tuple  	=   len(__snake_case						)

															# Check attention is always last and order is fine
															_SCREAMING_SNAKE_CASE				: Any  	=   True
															_SCREAMING_SNAKE_CASE				: List[str]  	=   True
															_SCREAMING_SNAKE_CASE				: int  	=   model_class(__snake_case						)
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   model(**self._prepare_for_class(__snake_case					,      __snake_case						)						)

															if hasattr(self.model_tester					,      """num_hidden_states_types"""						):
																			_SCREAMING_SNAKE_CASE				: Tuple  	=   self.model_tester.num_hidden_states_types
															else:
																			# also another +1 for reshaped_hidden_states
																			_SCREAMING_SNAKE_CASE				: str  	=   2
															self.assertEqual(out_len + added_hidden_states					,      len(__snake_case						)						)

															_SCREAMING_SNAKE_CASE				: Any  	=   outputs.attentions

															self.assertEqual(len(__snake_case						)					,      __snake_case						)

															self.assertListEqual(
															    list(self_attentions[0].shape[-3:]						)					,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,      )






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Dict  	=   model_class(__snake_case						)
											_SCREAMING_SNAKE_CASE				: Any  	=   model(**self._prepare_for_class(__snake_case					,      __snake_case						)						)
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   outputs.hidden_states

											_SCREAMING_SNAKE_CASE				: str  	=   getattr(
											    self.model_tester					,      """expected_num_hidden_layers"""					,      len(self.model_tester.depths						) + 1						)
											self.assertEqual(len(__snake_case						)					,      __snake_case						)

											# Swin has a different seq_length
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   to_atuple(config.patch_size						)

											_SCREAMING_SNAKE_CASE				: List[Any]  	=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]						)					,      [num_patches, self.model_tester.embed_dim]					,      )

											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   outputs.reshaped_hidden_states
											self.assertEqual(len(__snake_case						)					,      __snake_case						)

											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Optional[int]  	=   reshaped_hidden_states[0].shape

											_SCREAMING_SNAKE_CASE				: List[Any]  	=   tf.reshape(reshaped_hidden_states[0]					,      (batch_size, num_channels, height * width)						)
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   tf.transpose(__snake_case					,      (0, 2, 1)						)

											self.assertListEqual(
											    list(reshaped_hidden_states.shape[-2:]						)					,      [num_patches, self.model_tester.embed_dim]					,      )






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: List[Any]  	=   self.model_tester.prepare_config_and_inputs_for_common()

											_SCREAMING_SNAKE_CASE				: int  	=   to_atuple(self.model_tester.image_size						)

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   True
															self.check_hidden_states_output(__snake_case					,      __snake_case					,      __snake_case					,      __snake_case						)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															_SCREAMING_SNAKE_CASE				: str  	=   True

															self.check_hidden_states_output(__snake_case					,      __snake_case					,      __snake_case					,      __snake_case						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: List[str]  	=   self.model_tester.prepare_config_and_inputs_for_common()
											_SCREAMING_SNAKE_CASE				: int  	=   3

											_SCREAMING_SNAKE_CASE				: List[Any]  	=   to_atuple(self.model_tester.image_size						)
											_SCREAMING_SNAKE_CASE				: List[str]  	=   to_atuple(config.patch_size						)

											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
											_SCREAMING_SNAKE_CASE				: int  	=   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE				: int  	=   True
															self.check_hidden_states_output(__snake_case					,      __snake_case					,      __snake_case					,      (padded_height, padded_width)						)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   True
															self.check_hidden_states_output(__snake_case					,      __snake_case					,      __snake_case					,      (padded_height, padded_width)						)






							@slow
							def UpperCAmelCase_			(					self						):
											for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   TFSwinModel.from_pretrained(__snake_case						)
															self.assertIsNotNone(__snake_case						)





@require_vision
@require_tf
class 					lowercase__						(		unittest.TestCase      ):





							'''simple docstring'''


							@cached_property
							def UpperCAmelCase_			(					self						):
											return (
											    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""						)
											    if is_vision_available()
											    else None
											)






							@slow
							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Dict  	=   TFSwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""						)
											_SCREAMING_SNAKE_CASE				: str  	=   self.default_image_processor

											_SCREAMING_SNAKE_CASE				: Any  	=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   image_processor(images=__snake_case					,      return_tensors="""tf"""						)

											# forward pass
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   model(__snake_case						)

											# verify the logits
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   tf.TensorShape((1, 1000)						)
											self.assertEqual(outputs.logits.shape					,      __snake_case						)
											_SCREAMING_SNAKE_CASE				: int  	=   tf.constant([-0.0948, -0.6454, -0.0921]						)
											self.assertTrue(np.allclose(outputs.logits[0, :3]					,      __snake_case					,      atol=1e-4						)						)








'''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     snake_case_     (						SCREAMING_SNAKE_CASE__=None							,   SCREAMING_SNAKE_CASE__=None  ):







				"""simple docstring"""





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





@dataclass
class 					lowercase__						:





							'''simple docstring'''


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







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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





class 					lowercase__						:





							'''simple docstring'''


							def __init__(					self					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   args
											_SCREAMING_SNAKE_CASE				: Dict  	=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

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






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

															((_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE))				: Tuple  	=   (
															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
															)

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

																			_SCREAMING_SNAKE_CASE				: str  	=   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."""

											_SCREAMING_SNAKE_CASE				: str  	=   title_str[:-4]
											_SCREAMING_SNAKE_CASE				: int  	=   """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     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   HfArgumentParser(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: str  	=   parser.parse_args_into_dataclasses()[0]
				_SCREAMING_SNAKE_CASE				: int  	=   Plot(args=SCREAMING_SNAKE_CASE__  )
				plot.plot()


if __name__ == "__main__":
				main()




