import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MobileNetVaForImageClassification, MobileNetVaModel
  from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor
class  A_			(							lowerCamelCase__			):

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	self.config_class(**self.inputs_dict							)
   self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,				"tf_padding"							)							)
   self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,				"depth_multiplier"							)							)
class  A_			:

  def __init__(		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=13  ,				__SCREAMING_SNAKE_CASE			:   Any=3  ,				__SCREAMING_SNAKE_CASE			:   str=32  ,				__SCREAMING_SNAKE_CASE			:   int=0.25  ,				__SCREAMING_SNAKE_CASE			:   List[str]=8  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Tuple=10_24  ,				__SCREAMING_SNAKE_CASE			:   int=32  ,				__SCREAMING_SNAKE_CASE			:   str="relu6"  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=0.1  ,				__SCREAMING_SNAKE_CASE			:   Any=0.02  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   str=True  ,				__SCREAMING_SNAKE_CASE			:   str=10  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				):
   __a							=	parent
   __a							=	batch_size
   __a							=	num_channels
   __a							=	image_size
   __a							=	depth_multiplier
   __a							=	min_depth
   __a							=	tf_padding
   __a							=	int(last_hidden_size * depth_multiplier							)
   __a							=	output_stride
   __a							=	hidden_act
   __a							=	classifier_dropout_prob
   __a							=	use_labels
   __a							=	is_training
   __a							=	num_labels
   __a							=	initializer_range
   __a							=	scope

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

   __a							=	None
   __a							=	None
   if self.use_labels:
    __a							=	ids_tensor([self.batch_size]  ,				self.num_labels							)
    __a							=	ids_tensor([self.batch_size, self.image_size, self.image_size]  ,				self.num_labels							)

   __a							=	self.get_config()

   return config, pixel_values, labels, pixel_labels

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   return MobileNetVaConfig(
       num_channels=self.num_channels  ,				image_size=self.image_size  ,				depth_multiplier=self.depth_multiplier  ,				min_depth=self.min_depth  ,				tf_padding=self.tf_padding  ,				hidden_act=self.hidden_act  ,				classifier_dropout_prob=self.classifier_dropout_prob  ,				initializer_range=self.initializer_range  ,				)

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Tuple							):
   __a							=	MobileNetVaModel(config=__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	model(__SCREAMING_SNAKE_CASE							)
   self.parent.assertEqual(
       result.last_hidden_state.shape  ,				(
           self.batch_size,
           self.last_hidden_size,
           self.image_size // self.output_stride,
           self.image_size // self.output_stride,
       )  ,				)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   List[str]							):
   __a							=	self.num_labels
   __a							=	MobileNetVaForImageClassification(__SCREAMING_SNAKE_CASE							)
   model.to(__SCREAMING_SNAKE_CASE							)
   model.eval()
   __a							=	model(__SCREAMING_SNAKE_CASE  ,				labels=__SCREAMING_SNAKE_CASE							)
   self.parent.assertEqual(result.logits.shape  ,				(self.batch_size, self.num_labels)							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	self.prepare_config_and_inputs()
   __a							=	config_and_inputs
   __a							=	{"pixel_values": pixel_values}
   return config, inputs_dict
@require_torch
class  A_			(							lowerCamelCase__      ,							lowerCamelCase__      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
  _SCREAMING_SNAKE_CASE     					=      (
      {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
      if is_torch_available()
      else {}
  )

  _SCREAMING_SNAKE_CASE     					=      False
  _SCREAMING_SNAKE_CASE     					=      False
  _SCREAMING_SNAKE_CASE     					=      False
  _SCREAMING_SNAKE_CASE     					=      False

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	MobileNetVaModelTester(self							)
   __a							=	MobileNetVaConfigTester(self  ,				config_class=__SCREAMING_SNAKE_CASE  ,				has_text_modality=__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[str]							):
   self.config_tester.run_common_tests()

  @unittest.skip(reason="MobileNetV1 does not use inputs_embeds"							)
  def 		_UpperCAmelCase (		self			:   int							):
   pass

  @unittest.skip(reason="MobileNetV1 does not support input and output embeddings"							)
  def 		_UpperCAmelCase (		self			:   List[Any]							):
   pass

  @unittest.skip(reason="MobileNetV1 does not output attentions"							)
  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   pass

  def 		_UpperCAmelCase (		self			:   Tuple							):
   __a							=	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __a							=	model_class(__SCREAMING_SNAKE_CASE							)
    __a							=	inspect.signature(model.forward							)
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __a							=	[*signature.parameters.keys()]

    __a							=	["pixel_values"]
    self.assertListEqual(arg_names[:1]  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):




   def check_hidden_states_output(__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]							):
    __a							=	model_class(__SCREAMING_SNAKE_CASE							)
    model.to(__SCREAMING_SNAKE_CASE							)
    model.eval()

    with torch.no_grad():
     __a							=	model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)							)

    __a							=	outputs.hidden_states

    __a							=	26
    self.assertEqual(len(__SCREAMING_SNAKE_CASE							)  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __a							=	True
    check_hidden_states_output(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

    # check that output_hidden_states also work using config
    del inputs_dict["output_hidden_states"]
    __a							=	True

    check_hidden_states_output(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE							)

  @slow
  def 		_UpperCAmelCase (		self			:   List[str]							):




   for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __a							=	MobileNetVaModel.from_pretrained(__SCREAMING_SNAKE_CASE							)
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE							)



def 	__A		(      ):



 """simple docstring"""


 __a							=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
 return image






@require_torch
@require_vision
class  A_			(							unittest.TestCase			):

  @cached_property
  def 		_UpperCAmelCase (		self			:   Dict							):
   return (
       MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"							) if is_vision_available() else None
   )

  @slow
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"							).to(__SCREAMING_SNAKE_CASE							)

   __a							=	self.default_image_processor
   __a							=	prepare_img()
   __a							=	image_processor(images=__SCREAMING_SNAKE_CASE  ,				return_tensors="pt"							).to(__SCREAMING_SNAKE_CASE							)

   # forward pass
   with torch.no_grad():
    __a							=	model(**__SCREAMING_SNAKE_CASE							)

   # verify the logits
   __a							=	torch.Size((1, 10_01)							)
   self.assertEqual(outputs.logits.shape  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	torch.tensor([-4.17_39, -1.12_33, 3.12_05]							).to(__SCREAMING_SNAKE_CASE							)

   self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,				__SCREAMING_SNAKE_CASE  ,				atol=1E-4							)							)









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    (       _lowerCAmelCase			:			Optional[int]=None					,					_lowerCAmelCase			:			List[Any]=None						)			->    Optional[Any]:
   return field(default_factory=lambda: default					,					metadata=_lowerCAmelCase						)


@dataclass
class        __magic_name__	:





     """simple docstring"""



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




def   __snake_case    (       _lowerCAmelCase			:			int						)			->    Optional[Any]:
   try:
      int(_lowerCAmelCase						)
      return True
   except ValueError:
      return False

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    Union[str, Any]:
   try:
      float(_lowerCAmelCase						)
      return True
   except ValueError:
      return False


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Any      ,   snake_case							:List[Any]	):





        '''simple docstring'''




        A_       :					Any        =      args
        A_       :					Dict        =      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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





     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




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

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

           A_       :					List[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:
                 A_       :					Dict        =      np.asarray(
                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,   dtype=snake_case      ,   )
              else:
                 A_       :					Union[str, Any]        =      np.asarray(
                     [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,   dtype=np.floataa      ,   )

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

              A_       :					List[Any]        =      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."

        A_       :					Optional[int]        =      title_str[:-4]
        A_       :					List[str]        =      "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    (       )			->    Any:
   A_       :					Any        =      HfArgumentParser(_lowerCAmelCase						)
   A_       :					str        =      parser.parse_args_into_dataclasses()[0]
   A_       :					Optional[Any]        =      Plot(args=_lowerCAmelCase						)
   plot.plot()


if __name__ == "__main__":
 main()
