

import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 torch import nn

			from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
			from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image






class      lowerCamelCase_      :




	def __init__(     self						:     Any					,      __A						:     Any					,      __A						:     Optional[Any]=3					,      __A						:     List[str]=32					,      __A						:     Tuple=3					,      __A						:     Optional[Any]=10					,      __A						:     Optional[Any]=[8, 16, 32, 64]					,      __A						:     int=[1, 1, 2, 1]					,      __A						:     Any=True					,      __A						:     int=True					,      __A						:     Tuple="relu"					,      __A						:     Tuple=3					,      __A						:     Dict=None					,      __A						:     Optional[int]=["stage2", "stage3", "stage4"]					,      __A						:     Dict=[2, 3, 4]					,      __A						:     int=1					,      ):
		__A :			Any		    =			parent
		__A :			str		    =			batch_size
		__A :			Any		    =			image_size
		__A :			Any		    =			num_channels
		__A :			List[str]		    =			embeddings_size
		__A :			Optional[Any]		    =			hidden_sizes
		__A :			List[Any]		    =			depths
		__A :			Dict		    =			is_training
		__A :			Dict		    =			use_labels
		__A :			Optional[Any]		    =			hidden_act
		__A :			Union[str, Any]		    =			num_labels
		__A :			Optional[int]		    =			scope
		__A :			Union[str, Any]		    =			len(__A							)
		__A :			Dict		    =			out_features
		__A :			str		    =			out_indices
		__A :			int		    =			num_groups




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			Any		    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

		__A :			int		    =			None
		if self.use_labels:
			__A :			Optional[int]		    =			ids_tensor([self.batch_size]					,      self.num_labels							)

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

		return config, pixel_values, labels




	def        lowerCAmelCase_       (     self						:     int							):
		return BitConfig(
		    num_channels=self.num_channels					,      embeddings_size=self.embeddings_size					,      hidden_sizes=self.hidden_sizes					,      depths=self.depths					,      hidden_act=self.hidden_act					,      num_labels=self.num_labels					,      out_features=self.out_features					,      out_indices=self.out_indices					,      num_groups=self.num_groups					,      )




	def        lowerCAmelCase_       (     self						:     str					,      __A						:     str					,      __A						:     Optional[Any]					,      __A						:     str							):
		__A :			List[str]		    =			BitModel(config=__A							)
		model.to(__A							)
		model.eval()
		__A :			str		    =			model(__A							)
		self.parent.assertEqual(
		    result.last_hidden_state.shape					,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,      )




	def        lowerCAmelCase_       (     self						:     Any					,      __A						:     str					,      __A						:     List[Any]					,      __A						:     Any							):
		__A :			Dict		    =			self.num_labels
		__A :			Optional[Any]		    =			BitForImageClassification(__A							)
		model.to(__A							)
		model.eval()
		__A :			Any		    =			model(__A					,      labels=__A							)
		self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Optional[Any]					,      __A						:     Optional[Any]					,      __A						:     List[Any]							):
		__A :			List[Any]		    =			BitBackbone(config=__A							)
		model.to(__A							)
		model.eval()
		__A :			Tuple		    =			model(__A							)

		# verify feature maps
		self.parent.assertEqual(len(result.feature_maps							)					,      len(config.out_features							)							)
		self.parent.assertListEqual(list(result.feature_maps[0].shape							)					,      [self.batch_size, self.hidden_sizes[1], 4, 4]							)

		# verify channels
		self.parent.assertEqual(len(model.channels							)					,      len(config.out_features							)							)
		self.parent.assertListEqual(model.channels					,      config.hidden_sizes[1:]							)

		# verify backbone works with out_features=None
		__A :			Union[str, Any]		    =			None
		__A :			List[str]		    =			BitBackbone(config=__A							)
		model.to(__A							)
		model.eval()
		__A :			str		    =			model(__A							)

		# verify feature maps
		self.parent.assertEqual(len(result.feature_maps							)					,      1							)
		self.parent.assertListEqual(list(result.feature_maps[0].shape							)					,      [self.batch_size, self.hidden_sizes[-1], 1, 1]							)

		# verify channels
		self.parent.assertEqual(len(model.channels							)					,      1							)
		self.parent.assertListEqual(model.channels					,      [config.hidden_sizes[-1]]							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			int		    =			self.prepare_config_and_inputs()
		__A    ,				__A    ,				__A :			Union[str, Any]		    =			config_and_inputs
		__A :			Optional[int]		    =			{"""pixel_values""": pixel_values}
		return config, inputs_dict






@require_torch
class      lowerCamelCase_      (	_lowercase						,      _lowercase						,      unittest.TestCase	):
	_lowercase		:   Tuple     					=				(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
	_lowercase		:   List[Any]     					=				(
	    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_lowercase		:   int     					=				False
	_lowercase		:   str     					=				False
	_lowercase		:   List[str]     					=				False
	_lowercase		:   List[str]     					=				False
	_lowercase		:   Any     					=				False




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Any		    =			BitModelTester(self							)
		__A :			List[Any]		    =			ConfigTester(self					,      config_class=__A					,      has_text_modality=__A							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		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        lowerCAmelCase_       (     self						:     Tuple							):
		return




	@unittest.skip(reason="""Bit does not output attentions"""							)
	def        lowerCAmelCase_       (     self						:     Tuple							):
		pass




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




	@unittest.skip(reason="""Bit does not support input and output embeddings"""							)
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		pass




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A    ,				__A :			Optional[Any]		    =			self.model_tester.prepare_config_and_inputs_for_common()

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

			__A :			int		    =			["""pixel_values"""]
			self.assertListEqual(arg_names[:1]					,      __A							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			List[str]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__A							)




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Optional[Any]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_backbone(*__A							)




	def        lowerCAmelCase_       (     self						:     int							):
		__A    ,				__A :			int		    =			self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__A :			int		    =			model_class(config=__A							)
			for name, module in model.named_modules():
				if isinstance(__A					,      (nn.BatchNormad, nn.GroupNorm)							):
					self.assertTrue(
					    torch.all(module.weight == 1							)					,      msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					,      )
					self.assertTrue(
					    torch.all(module.bias == 0							)					,      msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					,      )




	def        lowerCAmelCase_       (     self						:     Dict							):
		def check_hidden_states_output(__A						:     Union[str, Any]					,      __A						:     List[Any]					,      __A						:     List[Any]							):
			__A :			Any		    =			model_class(__A							)
			model.to(__A							)
			model.eval()

			with torch.no_grad():
				__A :			Dict		    =			model(**self._prepare_for_class(__A					,      __A							)							)

			__A :			Dict		    =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

			__A :			Optional[Any]		    =			self.model_tester.num_stages
			self.assertEqual(len(__A							)					,      expected_num_stages + 1							)

			# Bit'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]					,      )

		__A    ,				__A :			Tuple		    =			self.model_tester.prepare_config_and_inputs_for_common()
		__A :			int		    =			["""preactivation""", """bottleneck"""]
		for model_class in self.all_model_classes:
			for layer_type in layers_type:
				__A :			List[Any]		    =			layer_type
				__A :			List[Any]		    =			True
				check_hidden_states_output(__A					,      __A					,      __A							)

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

				check_hidden_states_output(__A					,      __A					,      __A							)




	@unittest.skip(reason="""Bit does not use feedforward chunking"""							)
	def        lowerCAmelCase_       (     self						:     Any							):
		pass




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			List[str]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*__A							)




	@slow
	def        lowerCAmelCase_       (     self						:     List[Any]							):
		for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			__A :			List[Any]		    =			BitModel.from_pretrained(__A							)
			self.assertIsNotNone(__A							)






def 							__SCREAMING_SNAKE_CASE     (   )					->				int:
	__A :			Union[str, Any]		    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
	return image






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




	@cached_property
	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		return (
		    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							) if is_vision_available() else None
		)




	@slow
	def        lowerCAmelCase_       (     self						:     List[str]							):
		__A :			Optional[Any]		    =			BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(__A							)

		__A :			Optional[int]		    =			self.default_image_processor
		__A :			str		    =			prepare_img()
		__A :			int		    =			image_processor(images=__A					,      return_tensors="""pt"""							).to(__A							)

		# forward pass
		with torch.no_grad():
			__A :			Union[str, Any]		    =			model(**__A							)

		# verify the logits
		__A :			Optional[int]		    =			torch.Size((1, 1000)							)
		self.assertEqual(outputs.logits.shape					,      __A							)

		__A :			Tuple		    =			torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]							).to(__A							)

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






@require_torch
class      lowerCamelCase_      (	_lowercase						,      unittest.TestCase	):
	_lowercase		:   Tuple     					=				(BitBackbone,) if is_torch_available() else ()
	_lowercase		:   Dict     					=				BitConfig

	_lowercase		:   Tuple     					=				False




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Dict		    =			BitModelTester(self							)






'''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_      (					__snake_case  :							Tuple=None	,   __snake_case  :							int=None)			->					int:
   return field(default_factory=lambda: default	,   metadata=__snake_case)


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






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



def snake_case_      (					__snake_case  :							Dict)			->					int:
   try:
      float(__snake_case)
      return True
   except ValueError:
      return False


class     __UpperCAmelCase       :





  def __init__(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      args
     lowerCAmelCase_			=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )

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







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_	,lowerCAmelCase_			=      plt.subplots()
     lowerCAmelCase_			=      '''Time usage''' if self.args.is_time else '''Memory usage'''
     lowerCAmelCase_			=      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_			=      sorted(set(self.result_dict[model_name]['''bsz'''] ) )
        lowerCAmelCase_			=      sorted(set(self.result_dict[model_name]['''seq_len'''] ) )
        lowerCAmelCase_			=      self.result_dict[model_name]['''result''']

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

        lowerCAmelCase_			=      (
            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_			=      np.asarray(
                  [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]    ,     dtype=_lowerCamelCase    ,     )
           else:
              lowerCAmelCase_			=      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_))			=      (
               ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
           )

           lowerCAmelCase_			=      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_			=      title_str[:-4]
     lowerCAmelCase_			=      '''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 snake_case_      (					)			->					Tuple:
   lowerCAmelCase_			=      HfArgumentParser(__snake_case)
   lowerCAmelCase_			=      parser.parse_args_into_dataclasses()[0]
   lowerCAmelCase_			=      Plot(args=__snake_case)
   plot.plot()


if __name__ == "__main__":
 main()





