
'''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			)       ->							str:
     '''simple docstring'''

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

@dataclass
class 		__lowercase			:
   lowerCamelCase     :						str		 =     field(
       metadata={"help": "The csv file to plot."} ,       )
   lowerCamelCase     :						bool		 =     field(
       default=_UpperCAmelCase ,       metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."} ,       )
   lowerCamelCase     :						bool		 =     field(
       default=_UpperCAmelCase ,       metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."} ,       )
   lowerCamelCase     :						bool		 =     field(
       default=_UpperCAmelCase ,       metadata={"help": "Disable logarithmic scale when plotting"} ,       )
   lowerCamelCase     :						bool		 =     field(
       default=_UpperCAmelCase ,       metadata={
           "help": "Whether the csv file has training results or inference results. Defaults to inference results."
       } ,       )
   lowerCamelCase     :						Optional[str]		 =     field(
       default=_UpperCAmelCase ,       metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."} ,       )
   lowerCamelCase     :						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			)       ->							Union[str, Any]:
     '''simple docstring'''

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




def lowercase_		(				_lowercase			)       ->							int:
     '''simple docstring'''

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

class 		__lowercase			:

   def __init__(self     ,				A ):
        lowerCamelCase_   : Tuple       =						args
        lowerCamelCase_   : int       =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )

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






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

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

             lowerCamelCase_   : Dict       =						(
                 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:
                       lowerCamelCase_   : Dict       =						np.asarray(
                           [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]     ,				dtype=lowerCAmelCase__     ,				)
                  else:
                       lowerCamelCase_   : str       =						np.asarray(
                           [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]     ,				dtype=np.floataa     ,				)

                  ((lowerCamelCase_), (lowerCamelCase_))   : str       =						(
                      ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
                  )

                  lowerCamelCase_   : List[Any]       =						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."""

        lowerCamelCase_   : List[Any]       =						title_str[:-4]
        lowerCamelCase_   : Optional[Any]       =						'''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 lowercase_		(				)       ->							Any:
     '''simple docstring'''

     lowerCamelCase_   : Optional[Any]       =						HfArgumentParser(_lowercase			)
     lowerCamelCase_   : Optional[int]       =						parser.parse_args_into_dataclasses()[0]
     lowerCamelCase_   : List[Any]       =						Plot(args=_lowercase			)
     plot.plot()


if __name__ == "__main__":
     main()




'''simple docstring'''


from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
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 import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
					from PIL import Image

					from transformers import ConvNextImageProcessor

class 		__lowercase			:

			def __init__(self     ,				A     ,				A=1_3     ,				A=3_2     ,				A=3     ,				A=4     ,				A=[1_0, 2_0, 3_0, 4_0]     ,				A=[2, 2, 3, 2]     ,				A=True     ,				A=True     ,				A=3_7     ,				A="gelu"     ,				A=1_0     ,				A=0.02     ,				A=3     ,				A=None     ,				):
								lowerCamelCase_   : List[str]       =						parent
								lowerCamelCase_   : List[Any]       =						batch_size
								lowerCamelCase_   : List[str]       =						image_size
								lowerCamelCase_   : List[str]       =						num_channels
								lowerCamelCase_   : Union[str, Any]       =						num_stages
								lowerCamelCase_   : Optional[Any]       =						hidden_sizes
								lowerCamelCase_   : Optional[int]       =						depths
								lowerCamelCase_   : Optional[Any]       =						is_training
								lowerCamelCase_   : Dict       =						use_labels
								lowerCamelCase_   : Optional[int]       =						intermediate_size
								lowerCamelCase_   : Union[str, Any]       =						hidden_act
								lowerCamelCase_   : Tuple       =						type_sequence_label_size
								lowerCamelCase_   : Optional[Any]       =						initializer_range
								lowerCamelCase_   : str       =						scope

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Optional[int]       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

								lowerCamelCase_   : List[Any]       =						self.get_config()

								return config, pixel_values, labels

			def     UpperCAmelCase__	(self ):
								return ConvNextConfig(
								    num_channels=self.num_channels     ,				hidden_sizes=self.hidden_sizes     ,				depths=self.depths     ,				num_stages=self.num_stages     ,				hidden_act=self.hidden_act     ,				is_decoder=A     ,				initializer_range=self.initializer_range     ,				)

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A ):
								lowerCamelCase_   : List[str]       =						TFConvNextModel(config=A )
								lowerCamelCase_   : Tuple       =						model(A     ,				training=A )
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape     ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)     ,				)

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A ):
								lowerCamelCase_   : Union[str, Any]       =						self.type_sequence_label_size
								lowerCamelCase_   : Dict       =						TFConvNextForImageClassification(A )
								lowerCamelCase_   : Optional[Any]       =						model(A     ,				labels=A     ,				training=A )
								self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.type_sequence_label_size) )






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : str       =						self.prepare_config_and_inputs()
								lowerCamelCase_, lowerCamelCase_, lowerCamelCase_   : List[str]       =						config_and_inputs
								lowerCamelCase_   : Tuple       =						{'''pixel_values''': pixel_values}
								return config, inputs_dict

@require_tf
class 		__lowercase			(					_lowercase ,       _lowercase ,       unittest.TestCase      ):
			lowerCamelCase     :						Tuple		 =     (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
			lowerCamelCase     :						List[str]		 =     (
			    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
			    if is_tf_available()
			    else {}
			)

			lowerCamelCase     :						List[Any]		 =     False
			lowerCamelCase     :						List[str]		 =     False
			lowerCamelCase     :						Optional[int]		 =     False
			lowerCamelCase     :						List[Any]		 =     False
			lowerCamelCase     :						List[Any]		 =     False

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						TFConvNextModelTester(self )
								lowerCamelCase_   : int       =						ConfigTester(
								    self     ,				config_class=A     ,				has_text_modality=A     ,				hidden_size=3_7     ,				)

			@unittest.skip(reason='''ConvNext does not use inputs_embeds''' )
			def     UpperCAmelCase__	(self ):
								pass

			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0     ,				reason='''TF does not support backprop for grouped convolutions on CPU.'''     ,				)
			@slow
			def     UpperCAmelCase__	(self ):
								super().test_keras_fit()

			@unittest.skip(reason='''ConvNext does not support input and output embeddings''' )
			def     UpperCAmelCase__	(self ):
								pass

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_, lowerCamelCase_   : Tuple       =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													lowerCamelCase_   : Union[str, Any]       =						model_class(A )
													lowerCamelCase_   : str       =						inspect.signature(model.call )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCamelCase_   : Optional[int]       =						[*signature.parameters.keys()]

													lowerCamelCase_   : str       =						['''pixel_values''']
													self.assertListEqual(arg_names[:1]     ,				A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[Any]       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A )

			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0     ,				reason='''TF does not support backprop for grouped convolutions on CPU.'''     ,				)
			def     UpperCAmelCase__	(self ):
								super().test_dataset_conversion()

			def     UpperCAmelCase__	(self ):
								def check_hidden_states_output(A     ,				A     ,				A ):
													lowerCamelCase_   : str       =						model_class(A )

													lowerCamelCase_   : Dict       =						model(**self._prepare_for_class(A     ,				A ) )
													lowerCamelCase_   : List[str]       =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													lowerCamelCase_   : Tuple       =						self.model_tester.num_stages
													self.assertEqual(len(A )     ,				expected_num_stages + 1 )

													# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:] )     ,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]     ,				)

								lowerCamelCase_, lowerCamelCase_   : Union[str, Any]       =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													lowerCamelCase_   : Optional[Any]       =						True
													check_hidden_states_output(A     ,				A     ,				A )

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

													check_hidden_states_output(A     ,				A     ,				A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_, lowerCamelCase_   : Dict       =						self.model_tester.prepare_config_and_inputs_for_common()

								def check_equivalence(A     ,				A     ,				A     ,				A={} ):
													lowerCamelCase_   : Tuple       =						model(A     ,				return_dict=A     ,				**A )
													lowerCamelCase_   : List[str]       =						model(A     ,				return_dict=A     ,				**A ).to_tuple()

													def recursive_check(A     ,				A ):
																		if isinstance(A     ,				(List, Tuple) ):
																							for tuple_iterable_value, dict_iterable_value in zip(A     ,				A ):
																												recursive_check(A     ,				A )
																		elif tuple_object is None:
																							return
																		else:
																							self.assertTrue(
																							    all(tf.equal(A     ,				A ) )     ,				msg=(
																							        '''Tuple and dict output are not equal. Difference:'''
																							        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
																							    )     ,				)

																		recursive_check(A     ,				A )

								for model_class in self.all_model_classes:
													lowerCamelCase_   : Optional[Any]       =						model_class(A )

													lowerCamelCase_   : Any       =						self._prepare_for_class(A     ,				A )
													lowerCamelCase_   : Any       =						self._prepare_for_class(A     ,				A )
													check_equivalence(A     ,				A     ,				A )

													lowerCamelCase_   : Optional[Any]       =						self._prepare_for_class(A     ,				A     ,				return_labels=A )
													lowerCamelCase_   : int       =						self._prepare_for_class(A     ,				A     ,				return_labels=A )
													check_equivalence(A     ,				A     ,				A )

													lowerCamelCase_   : Union[str, Any]       =						self._prepare_for_class(A     ,				A )
													lowerCamelCase_   : List[str]       =						self._prepare_for_class(A     ,				A )
													check_equivalence(A     ,				A     ,				A     ,				{'''output_hidden_states''': True} )

													lowerCamelCase_   : Optional[int]       =						self._prepare_for_class(A     ,				A     ,				return_labels=A )
													lowerCamelCase_   : List[str]       =						self._prepare_for_class(A     ,				A     ,				return_labels=A )
													check_equivalence(A     ,				A     ,				A     ,				{'''output_hidden_states''': True} )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : str       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*A )






			@slow
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						TFConvNextModel.from_pretrained('''facebook/convnext-tiny-224''' )
								self.assertIsNotNone(A )




def lowercase_		(				)       ->							Optional[Any]:
					'''simple docstring'''

					lowerCamelCase_   : Optional[Any]       =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
					return image






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

			@cached_property
			def     UpperCAmelCase__	(self ):
								return ConvNextImageProcessor.from_pretrained('''facebook/convnext-tiny-224''' ) if is_vision_available() else None






			@slow
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						TFConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224''' )

								lowerCamelCase_   : Tuple       =						self.default_image_processor
								lowerCamelCase_   : List[str]       =						prepare_img()
								lowerCamelCase_   : Optional[Any]       =						image_processor(images=A     ,				return_tensors='''tf''' )

								# forward pass
								lowerCamelCase_   : Optional[int]       =						model(**A )

								# verify the logits
								lowerCamelCase_   : List[str]       =						tf.TensorShape((1, 1_0_0_0) )
								self.assertEqual(outputs.logits.shape     ,				A )

								lowerCamelCase_   : Optional[int]       =						tf.constant([-0.02_60, -0.47_39, 0.19_11] )

								tf.debugging.assert_near(outputs.logits[0, :3]     ,				A     ,				atol=1E-4 )


