

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 						__magic_name__    (       UpperCamelCase__    ):


 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      int:
   UpperCAmelCase					      =  self.config_class(**self.inputs_dict    )
   self.parent.assertTrue(hasattr(__A			,						"tf_padding"    )    )
   self.parent.assertTrue(hasattr(__A			,						"depth_multiplier"    )    )






class 						__magic_name__    :


 def __init__(						self		: List[str]			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: int=1_3			,						lowerCAmelCase__		: Union[str, Any]=3			,						lowerCAmelCase__		: Tuple=3_2			,						lowerCAmelCase__		: Union[str, Any]=0.25			,						lowerCAmelCase__		: List[Any]=8			,						lowerCAmelCase__		: Dict=True			,						lowerCAmelCase__		: List[Any]=1_0_2_4			,						lowerCAmelCase__		: Dict=3_2			,						lowerCAmelCase__		: Optional[int]="relu6"			,						lowerCAmelCase__		: Optional[Any]=0.1			,						lowerCAmelCase__		: Any=0.02			,						lowerCAmelCase__		: Tuple=True			,						lowerCAmelCase__		: int=True			,						lowerCAmelCase__		: Optional[int]=1_0			,						lowerCAmelCase__		: List[Any]=None			,						)    ->      Optional[Any]:
   UpperCAmelCase					      =  parent
   UpperCAmelCase					      =  batch_size
   UpperCAmelCase					      =  num_channels
   UpperCAmelCase					      =  image_size
   UpperCAmelCase					      =  depth_multiplier
   UpperCAmelCase					      =  min_depth
   UpperCAmelCase					      =  tf_padding
   UpperCAmelCase					      =  int(last_hidden_size * depth_multiplier    )
   UpperCAmelCase					      =  output_stride
   UpperCAmelCase					      =  hidden_act
   UpperCAmelCase					      =  classifier_dropout_prob
   UpperCAmelCase					      =  use_labels
   UpperCAmelCase					      =  is_training
   UpperCAmelCase					      =  num_labels
   UpperCAmelCase					      =  initializer_range
   UpperCAmelCase					      =  scope


 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Union[str, Any]:
   UpperCAmelCase					      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

   UpperCAmelCase					      =  self.get_config()

   return config, pixel_values, labels, pixel_labels


 def _UpperCamelCase	(						self		: Dict    )    ->      int:
   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		: Optional[int]			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: Dict    )    ->      int:
   UpperCAmelCase					      =  MobileNetVaModel(config=__A    )
   model.to(__A    )
   model.eval()
   UpperCAmelCase					      =  model(__A    )
   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[str]			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: str    )    ->      str:
   UpperCAmelCase					      =  self.num_labels
   UpperCAmelCase					      =  MobileNetVaForImageClassification(__A    )
   model.to(__A    )
   model.eval()
   UpperCAmelCase					      =  model(__A			,						labels=__A    )
   self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)    )


 def _UpperCamelCase	(						self		: int    )    ->      Tuple:
   UpperCAmelCase					      =  self.prepare_config_and_inputs()
   UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  config_and_inputs
   UpperCAmelCase					      =  {"pixel_values": pixel_values}
   return config, inputs_dict






@require_torch
class 						__magic_name__    (       UpperCamelCase__	,						UpperCamelCase__	,						unittest.TestCase    ):
 UpperCAmelCase							    =						(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
 UpperCAmelCase							    =						(
     {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
     if is_torch_available()
     else {}
 )

 UpperCAmelCase							    =						False
 UpperCAmelCase							    =						False
 UpperCAmelCase							    =						False
 UpperCAmelCase							    =						False


 def _UpperCamelCase	(						self		: Dict    )    ->      List[Any]:
   UpperCAmelCase					      =  MobileNetVaModelTester(self    )
   UpperCAmelCase					      =  MobileNetVaConfigTester(self			,						config_class=__A			,						has_text_modality=__A    )


 def _UpperCamelCase	(						self		: List[str]    )    ->      List[Any]:
   self.config_tester.run_common_tests()


 @unittest.skip(reason="MobileNetV1 does not use inputs_embeds"    )
 def _UpperCamelCase	(						self		: Dict    )    ->      Optional[int]:
   pass


 @unittest.skip(reason="MobileNetV1 does not support input and output embeddings"    )
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      Optional[Any]:
   pass


 @unittest.skip(reason="MobileNetV1 does not output attentions"    )
 def _UpperCamelCase	(						self		: Any    )    ->      int:
   pass


 def _UpperCamelCase	(						self		: List[Any]    )    ->      Dict:
   UpperCAmelCase      ,	UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs_for_common()

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

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


 def _UpperCamelCase	(						self		: int    )    ->      Union[str, Any]:
   UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__A    )


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Tuple:



   def check_hidden_states_output(lowerCAmelCase__		: Optional[Any]			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: List[str]    ):
     UpperCAmelCase					      =  model_class(__A    )
     model.to(__A    )
     model.eval()

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

     UpperCAmelCase					      =  outputs.hidden_states

     UpperCAmelCase					      =  2_6
     self.assertEqual(len(__A    )			,						__A    )

   UpperCAmelCase      ,	UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     UpperCAmelCase					      =  True
     check_hidden_states_output(__A			,						__A			,						__A    )

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

     check_hidden_states_output(__A			,						__A			,						__A    )


 def _UpperCamelCase	(						self		: Any    )    ->      int:
   UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*__A    )


 @slow
 def _UpperCamelCase	(						self		: Any    )    ->      Union[str, Any]:



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

def      _lowerCAmelCase(	):
  UpperCAmelCase					      =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
  return image






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


 @cached_property
 def _UpperCamelCase	(						self		: Any    )    ->      Union[str, Any]:
   return (
       MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"    ) if is_vision_available() else None
   )


 @slow
 def _UpperCamelCase	(						self		: Dict    )    ->      Optional[int]:
   UpperCAmelCase					      =  MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"    ).to(__A    )

   UpperCAmelCase					      =  self.default_image_processor
   UpperCAmelCase					      =  prepare_img()
   UpperCAmelCase					      =  image_processor(images=__A			,						return_tensors="pt"    ).to(__A    )

   # forward pass
   with torch.no_grad():
     UpperCAmelCase					      =  model(**__A    )

   # verify the logits
   UpperCAmelCase					      =  torch.Size((1, 1_0_0_1)    )
   self.assertEqual(outputs.logits.shape			,						__A    )

   UpperCAmelCase					      =  torch.tensor([-4.1_739, -1.1_233, 3.1_205]    ).to(__A    )

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






import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


lowerCAmelCase__       = logging.getLogger(__name__)

lowerCAmelCase__       = 50  # max width of layer names
lowerCAmelCase__       = 70  # max width of quantizer names
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  parser.add_argument_group("quant_trainer arguments"			)
  group.add_argument("--wprec"   ,       type=__A   ,       default=8   ,       help="weight precision"			)
  group.add_argument("--aprec"   ,       type=__A   ,       default=8   ,       help="activation precision"			)
  group.add_argument("--quant-per-tensor"   ,       action="store_true"   ,       help="per tensor weight scaling"			)
  group.add_argument("--quant-disable"   ,       action="store_true"   ,       help="disable all quantizers"			)
  group.add_argument("--quant-disable-embeddings"   ,       action="store_true"   ,       help="disable all embeddings quantizers"			)
  group.add_argument("--quant-disable-keyword"   ,       type=__A   ,       nargs="+"   ,       help="disable quantizers by keyword"			)
  group.add_argument("--quant-disable-layer-module"   ,       type=__A   ,       help="disable quantizers by keyword under layer."			)
  group.add_argument("--quant-enable-layer-module"   ,       type=__A   ,       help="enable quantizers by keyword under layer"			)
  group.add_argument("--calibrator"   ,       default="max"   ,       help="which quantization range calibrator to use"			)
  group.add_argument("--percentile"   ,       default=__A   ,       type=__A   ,       help="percentile for PercentileCalibrator"			)
  group.add_argument("--fuse-qkv"   ,       action="store_true"   ,       help="use the same scale factor for qkv"			)
  group.add_argument("--clip-gelu"   ,       metavar="N"   ,       type=__A   ,       help="clip gelu output maximum value to N"			)
  group.add_argument(
      "--recalibrate-weights"   ,       action="store_true"   ,       help=(
          "recalibrate weight amaxes by taking the max of the weights."
          " amaxes will be computed with the current quantization granularity (axis)."
      )   ,       )
def      _lowerCAmelCase(	__A			):

  if args.calibrator == "max":
    UpperCAmelCase					      =  "max"
  elif args.calibrator == "percentile":
    if args.percentile is None:
      raise ValueError("Specify --percentile when using percentile calibrator"			)
    UpperCAmelCase					      =  "histogram"
  elif args.calibrator == "mse":
    UpperCAmelCase					      =  "histogram"
  else:
    raise ValueError(F"Invalid calibrator {args.calibrator}"			)

  UpperCAmelCase					      =  QuantDescriptor(num_bits=args.aprec   ,       calib_method=__A			)
  UpperCAmelCase					      =  QuantDescriptor(num_bits=args.wprec   ,       axis=(None if args.quant_per_tensor else (0,))			)
  quant_nn.QuantLinear.set_default_quant_desc_input(__A			)
  quant_nn.QuantLinear.set_default_quant_desc_weight(__A			)
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=False   ,       __A=False			):
  logger.info("Configuring Model for Quantization"			)
  logger.info(F"using quantization package {pytorch_quantization.__file__}"			)

  if not calib:
    if args.quant_disable_embeddings:
      set_quantizer_by_name(__A   ,       ["embeddings"]   ,       which="weight"   ,       _disabled=__A			)

    if args.quant_disable:
      set_quantizer_by_name(__A   ,       [""]   ,       _disabled=__A			)

    if args.quant_disable_keyword:
      set_quantizer_by_name(__A   ,       args.quant_disable_keyword   ,       _disabled=__A			)

    if args.quant_disable_layer_module:
      set_quantizer_by_name(__A   ,       [r"layer.\d+." + args.quant_disable_layer_module]   ,       _disabled=__A			)

    if args.quant_enable_layer_module:
      set_quantizer_by_name(__A   ,       [r"layer.\d+." + args.quant_enable_layer_module]   ,       _disabled=__A			)

    if args.recalibrate_weights:
      recalibrate_weights(__A			)

    if args.fuse_qkv:
      fuse_qkv(__A   ,       __A			)

  if args.clip_gelu:
    clip_gelu(__A   ,       args.clip_gelu			)

  # if args.local_rank in [-1, 0] and not calib:
  print_quant_summary(__A			)
def      _lowerCAmelCase(	__A			):
  logger.info("Enabling Calibration"			)
  for name, module in model.named_modules():
    if name.endswith("_quantizer"			):
      if module._calibrator is not None:
        module.disable_quant()
        module.enable_calib()
      else:
        module.disable()
      logger.info(F"{name:80}: {module}"			)
def      _lowerCAmelCase(	__A   ,       __A			):
  logger.info("Loading calibrated amax"			)
  for name, module in model.named_modules():
    if name.endswith("_quantizer"			):
      if module._calibrator is not None:
        if isinstance(module._calibrator   ,       calib.MaxCalibrator			):
          module.load_calib_amax()
        else:
          module.load_calib_amax("percentile"   ,       percentile=args.percentile			)
        module.enable_quant()
        module.disable_calib()
      else:
        module.enable()
  model.cuda()
  print_quant_summary(__A			)
def      _lowerCAmelCase(	__A   ,       __A			):

  def fusea(__A   ,       __A   ,       __A			):
    for mod in [qq, qk, qv]:
      if not hasattr(__A   ,       "_amax"			):
        print("          WARNING: NO AMAX BUFFER"			)
        return
    UpperCAmelCase					      =  qq._amax.detach().item()
    UpperCAmelCase					      =  qk._amax.detach().item()
    UpperCAmelCase					      =  qv._amax.detach().item()

    UpperCAmelCase					      =  max(__A   ,       __A   ,       __A			)
    qq._amax.fill_(__A			)
    qk._amax.fill_(__A			)
    qv._amax.fill_(__A			)
    logger.info(F"          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}"			)

  for name, mod in model.named_modules():
    if name.endswith(".attention.self"			):
      logger.info(F"FUSE_QKV: {name:{name_width}}"			)
      fusea(mod.matmul_q_input_quantizer   ,       mod.matmul_k_input_quantizer   ,       mod.matmul_v_input_quantizer			)
      if args.quant_per_tensor:
        fusea(mod.query._weight_quantizer   ,       mod.key._weight_quantizer   ,       mod.value._weight_quantizer			)
def      _lowerCAmelCase(	__A   ,       __A			):

  for name, mod in model.named_modules():
    if name.endswith(".output.dense"			) and not name.endswith("attention.output.dense"			):
      UpperCAmelCase					      =  mod._input_quantizer._amax.data.detach().item()
      mod._input_quantizer._amax.data.detach().clamp_(max=__A			)
      UpperCAmelCase					      =  mod._input_quantizer._amax.data.detach().item()
      logger.info(F"CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}"			)
def      _lowerCAmelCase(	__A			):

  for name, mod in model.named_modules():
    if hasattr(__A   ,       "_weight_quantizer"			) and mod._weight_quantizer.axis is not None:
      UpperCAmelCase					      =  mod.weight.shape[0]
      UpperCAmelCase					      =  mod._weight_quantizer._amax.detach()
      UpperCAmelCase					      =  torch.ones(__A   ,       dtype=amax.dtype   ,       device=amax.device			) * amax
      print(F"expanding {name} {amax} -> {mod._weight_quantizer._amax}"			)
def      _lowerCAmelCase(	__A			):

  for name, mod in model.named_modules():
    if hasattr(__A   ,       "_weight_quantizer"			):
      if not hasattr(mod.weight_quantizer   ,       "_amax"			):
        print("RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER"			)
        continue

      # determine which axes to reduce across
      # e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
      UpperCAmelCase					      =  set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis			)
      UpperCAmelCase					      =  set(range(len(mod.weight.size()			)			)			) - axis_set
      UpperCAmelCase					      =  pytorch_quantization.utils.reduce_amax(mod.weight   ,       axis=__A   ,       keepdims=__A			).detach()
      logger.info(F"RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"			)
      UpperCAmelCase					      =  amax
def      _lowerCAmelCase(	__A   ,       __A=25   ,       __A=180   ,       __A=None			):

  if ignore is None:
    UpperCAmelCase					      =  []
  elif not isinstance(__A   ,       __A			):
    UpperCAmelCase					      =  [ignore]

  UpperCAmelCase					      =  0
  for name, mod in model.named_modules():
    if not hasattr(__A   ,       "weight"			):
      continue
    UpperCAmelCase					      =  max(__A   ,       len(__A			)			)

  for name, mod in model.named_modules():
    UpperCAmelCase					      =  getattr(__A   ,       "_input_quantizer"   ,       __A			)
    UpperCAmelCase					      =  getattr(__A   ,       "_weight_quantizer"   ,       __A			)
    if not hasattr(__A   ,       "weight"			):
      continue
    if type(__A			) in ignore:
      continue
    if [True for s in ignore if type(__A			) is str and s in name]:
      continue
    UpperCAmelCase					      =  F"Act:{input_q.extra_repr()}"
    UpperCAmelCase					      =  F"Wgt:{weight_q.extra_repr()}"
    UpperCAmelCase					      =  F"{name:{name_width}} {act_str} {wgt_str}"
    if len(__A			) <= line_width:
      logger.info(__A			)
    else:
      logger.info(F"{name:{name_width}} {act_str}"			)
      logger.info(F"{'  ':{name_width}} {wgt_str}"			)
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  0
  for name, mod in model.named_modules():
    if isinstance(__A   ,       pytorch_quantization.nn.TensorQuantizer			):
      print(F"{name:80} {mod}"			)
      count += 1
  print(F"{count} TensorQuantizers found in model"			)
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A   ,       __A			):
  UpperCAmelCase					      =  getattr(__A   ,       __A   ,       __A			)
  if quantizer_mod is not None:
    assert hasattr(__A   ,       __A			)
    setattr(__A   ,       __A   ,       __A			)
  else:
    logger.warning(F"{name} has no {quantizer}"			)
def      _lowerCAmelCase(	__A   ,       __A   ,       __A="both"   ,       **__A			):
  UpperCAmelCase					      =  F"Warning: changing {which} quantizers of {name:{qname_width}}"
  for k, v in kwargs.items():
    s += F" {k}={v}"
    if which in ["input", "both"]:
      set_quantizer(__A   ,       __A   ,       "_input_quantizer"   ,       __A   ,       __A			)
    if which in ["weight", "both"]:
      set_quantizer(__A   ,       __A   ,       "_weight_quantizer"   ,       __A   ,       __A			)
  logger.info(__A			)





def      _lowerCAmelCase(	__A   ,       __A   ,       **__A			):

  for name, mod in model.named_modules():
    if hasattr(__A   ,       "_input_quantizer"			) or hasattr(__A   ,       "_weight_quantizer"			):
      for n in names:
        if re.search(__A   ,       __A			):
          set_quantizers(__A   ,       __A   ,       **__A			)
    elif name.endswith("_quantizer"			):
      for n in names:
        if re.search(__A   ,       __A			):
          UpperCAmelCase					      =  F"Warning: changing {name:{name_width}}"
          for k, v in kwargs.items():
            s += F" {k}={v}"
            setattr(__A   ,       __A   ,       __A			)
          logger.info(__A			)



