


import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


snake_case							:       str										=	get_tests_dir('''fixtures''')







class 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			mock.Mock()
       a		:Dict									=			500
       a		:Optional[int]									=			{}
       a		:List[str]									=			HTTPError
       a		:Tuple									=			{}

       # Download this model to make sure it's in the cache.
       a		:Any									=			ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''       )
       # Under the mock environment we get a 500 error when trying to reach the model.
       with mock.patch('''requests.Session.request'''   ,    return_value=lowerCamelCase_       ) as mock_head:
             a		:Tuple									=			ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''       )
             # This check we did call the fake head request
             mock_head.assert_called()




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			ViTImageProcessor.from_pretrained(
           '''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'''       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):



       with self.assertRaises(lowerCamelCase_       ):
             # config is in subfolder, the following should not work without specifying the subfolder
             a		:int									=			AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants'''       )

       a		:Optional[Any]									=			AutoImageProcessor.from_pretrained(
           '''hf-internal-testing/stable-diffusion-all-variants'''   ,    subfolder='''feature_extractor'''       )

       self.assertIsNotNone(lowerCamelCase_       )







@is_staging_test
class 						_snake_case    ( unittest.TestCase				):




 @classmethod
 def 			SCREAMING_SNAKE_CASE__							(						cls       ):
       a		:Optional[int]									=			TOKEN
       HfFolder.save_token(lowerCamelCase_       )




 @classmethod
 def 			SCREAMING_SNAKE_CASE__							(						cls       ):



       try:
             delete_repo(token=cls._token   ,    repo_id='''test-image-processor'''       )
       except HTTPError:
             pass

       try:
             delete_repo(token=cls._token   ,    repo_id='''valid_org/test-image-processor-org'''       )
       except HTTPError:
             pass

       try:
             delete_repo(token=cls._token   ,    repo_id='''test-dynamic-image-processor'''       )
       except HTTPError:
             pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[Any]									=			ViTImageProcessor.from_pretrained(lowerCamelCase_       )
       image_processor.push_to_hub('''test-image-processor'''   ,    use_auth_token=self._token       )

       a		:Any									=			ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor'''       )
       for k, v in image_processor.__dict__.items():
             self.assertEqual(lowerCamelCase_   ,    getattr(lowerCamelCase_   ,    lowerCamelCase_       )       )

       # Reset repo
       delete_repo(token=self._token   ,    repo_id='''test-image-processor'''       )

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(
                 lowerCamelCase_   ,    repo_id='''test-image-processor'''   ,    push_to_hub=lowerCamelCase_   ,    use_auth_token=self._token       )

       a		:List[Any]									=			ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor'''       )
       for k, v in image_processor.__dict__.items():
             self.assertEqual(lowerCamelCase_   ,    getattr(lowerCamelCase_   ,    lowerCamelCase_       )       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			ViTImageProcessor.from_pretrained(lowerCamelCase_       )
       image_processor.push_to_hub('''valid_org/test-image-processor'''   ,    use_auth_token=self._token       )

       a		:Any									=			ViTImageProcessor.from_pretrained('''valid_org/test-image-processor'''       )
       for k, v in image_processor.__dict__.items():
             self.assertEqual(lowerCamelCase_   ,    getattr(lowerCamelCase_   ,    lowerCamelCase_       )       )

       # Reset repo
       delete_repo(token=self._token   ,    repo_id='''valid_org/test-image-processor'''       )

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(
                 lowerCamelCase_   ,    repo_id='''valid_org/test-image-processor-org'''   ,    push_to_hub=lowerCamelCase_   ,    use_auth_token=self._token       )

       a		:Any									=			ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org'''       )
       for k, v in image_processor.__dict__.items():
             self.assertEqual(lowerCamelCase_   ,    getattr(lowerCamelCase_   ,    lowerCamelCase_       )       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       CustomImageProcessor.register_for_auto_class()
       a		:List[str]									=			CustomImageProcessor.from_pretrained(lowerCamelCase_       )

       image_processor.push_to_hub('''test-dynamic-image-processor'''   ,    use_auth_token=self._token       )

       # This has added the proper auto_map field to the config
       self.assertDictEqual(
           image_processor.auto_map   ,    {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''}   ,    )

       a		:Any									=			AutoImageProcessor.from_pretrained(
           F'''{USER}/test-dynamic-image-processor'''   ,    trust_remote_code=lowerCamelCase_       )
       # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
       self.assertEqual(new_image_processor.__class__.__name__   ,    '''CustomImageProcessor'''       )






'''simple docstring'''

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


UpperCamelCase__  :						Optional[Any]					=      logging.getLogger(__name__)

UpperCamelCase__  :						Dict					=      50  # max width of layer names
UpperCamelCase__  :						Any					=      70  # max width of quantizer names







def __UpperCamelCase( _A       :					Union[str, Any]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[str] 					=	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 __UpperCamelCase( _A       :					Tuple     ):
   '''simple docstring'''

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

   UpperCAmelCase__				:					Dict 					=	QuantDescriptor(num_bits=args.aprec	,      calib_method=_A     )
   UpperCAmelCase__				:					str 					=	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 __UpperCamelCase( _A       :					Any	,      _A       :					Any	,      _A       :					Any=False	,      _A       :					Optional[Any]=False     ):
   '''simple docstring'''
   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 __UpperCamelCase( _A       :					str     ):
   '''simple docstring'''
   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 __UpperCamelCase( _A       :					Tuple	,      _A       :					Any     ):
   '''simple docstring'''
   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 __UpperCamelCase( _A       :					Dict	,      _A       :					Optional[int]     ):
   '''simple docstring'''

   def fusea(_A       :					Optional[Any]	,      _A       :					Optional[Any]	,      _A       :					Dict     ):
      for mod in [qq, qk, qv]:
         if not hasattr(_A	,      '''_amax'''     ):
            print('''          WARNING: NO AMAX BUFFER'''     )
            return
      UpperCAmelCase__				:					Dict 					=	qq._amax.detach().item()
      UpperCAmelCase__				:					List[Any] 					=	qk._amax.detach().item()
      UpperCAmelCase__				:					Optional[int] 					=	qv._amax.detach().item()

      UpperCAmelCase__				:					Dict 					=	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 __UpperCamelCase( _A       :					Dict	,      _A       :					Any     ):
   '''simple docstring'''

   for name, mod in model.named_modules():
      if name.endswith('''.output.dense'''     ) and not name.endswith('''attention.output.dense'''     ):
         UpperCAmelCase__				:					Union[str, Any] 					=	mod._input_quantizer._amax.data.detach().item()
         mod._input_quantizer._amax.data.detach().clamp_(max=_A     )
         UpperCAmelCase__				:					Tuple 					=	mod._input_quantizer._amax.data.detach().item()
         logger.info(F'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}'''     )







def __UpperCamelCase( _A       :					str     ):
   '''simple docstring'''

   for name, mod in model.named_modules():
      if hasattr(_A	,      '''_weight_quantizer'''     ) and mod._weight_quantizer.axis is not None:
         UpperCAmelCase__				:					int 					=	mod.weight.shape[0]
         UpperCAmelCase__				:					Tuple 					=	mod._weight_quantizer._amax.detach()
         UpperCAmelCase__				:					Optional[int] 					=	torch.ones(_A	,      dtype=amax.dtype	,      device=amax.device     ) * amax
         print(F'''expanding {name} {amax} -> {mod._weight_quantizer._amax}'''     )







def __UpperCamelCase( _A       :					List[str]     ):
   '''simple docstring'''

   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__				:					Any 					=	set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis     )
         UpperCAmelCase__				:					Optional[Any] 					=	set(range(len(mod.weight.size()     )     )     ) - axis_set
         UpperCAmelCase__				:					int 					=	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__				:					str 					=	amax







def __UpperCamelCase( _A       :					Dict	,      _A       :					Tuple=25	,      _A       :					Any=1_80	,      _A       :					Optional[int]=None     ):
   '''simple docstring'''

   if ignore is None:
      UpperCAmelCase__				:					Dict 					=	[]
   elif not isinstance(_A	,      _A     ):
      UpperCAmelCase__				:					int 					=	[ignore]

   UpperCAmelCase__				:					Optional[int] 					=	0
   for name, mod in model.named_modules():
      if not hasattr(_A	,      '''weight'''     ):
         continue
      UpperCAmelCase__				:					Dict 					=	max(_A	,      len(_A     )     )

   for name, mod in model.named_modules():
      UpperCAmelCase__				:					str 					=	getattr(_A	,      '''_input_quantizer'''	,      _A     )
      UpperCAmelCase__				:					int 					=	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__				:					Dict 					=	F'''Act:{input_q.extra_repr()}'''
      UpperCAmelCase__				:					int 					=	F'''Wgt:{weight_q.extra_repr()}'''
      UpperCAmelCase__				:					Dict 					=	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 __UpperCamelCase( _A       :					Optional[Any]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	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 __UpperCamelCase( _A       :					Dict	,      _A       :					Optional[Any]	,      _A       :					Union[str, Any]	,      _A       :					Union[str, Any]	,      _A       :					Dict     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Any 					=	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 __UpperCamelCase( _A       :					str	,      _A       :					Any	,      _A       :					Optional[int]="both"	,      **_A       :					List[str]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Tuple 					=	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 __UpperCamelCase( _A       :					Tuple	,      _A       :					List[str]	,      **_A       :					Optional[Any]     ):
   '''simple docstring'''

   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__				:					str 					=	F'''Warning: changing {name:{name_width}}'''
               for k, v in kwargs.items():
                  s += F''' {k}={v}'''
                  setattr(_A	,      _A	,      _A     )
               logger.info(_A     )




