
from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
					from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: str ):
					__A   			= self.config_class(**self.inputs_dict )
					self.parent.assertTrue(hasattr(A							,"embed_dim" ) )
					self.parent.assertTrue(hasattr(A							,"num_heads" ) )





class 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: str							,A					: Tuple							,A					: Dict=13							,A					: Optional[int]=64							,A					: Optional[Any]=3							,A					: List[Any]=[16, 48, 96]							,A					: Union[str, Any]=[1, 3, 6]							,A					: str=[1, 2, 10]							,A					: Optional[int]=[7, 3, 3]							,A					: List[Any]=[4, 2, 2]							,A					: int=[2, 1, 1]							,A					: Tuple=[2, 2, 2]							,A					: Any=[False, False, True]							,A					: Any=[0.0, 0.0, 0.0]							,A					: Optional[int]=0.02							,A					: Optional[Any]=1E-12							,A					: List[str]=True							,A					: Union[str, Any]=True							,A					: List[Any]=2							,):
					__A   			= parent
					__A   			= batch_size
					__A   			= image_size
					__A   			= patch_sizes
					__A   			= patch_stride
					__A   			= patch_padding
					__A   			= is_training
					__A   			= use_labels
					__A   			= num_labels
					__A   			= num_channels
					__A   			= embed_dim
					__A   			= num_heads
					__A   			= stride_kv
					__A   			= depth
					__A   			= cls_token
					__A   			= attention_drop_rate
					__A   			= initializer_range
					__A   			= layer_norm_eps






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

					__A   			= None
					if self.use_labels:
									# create a random int32 tensor of given shape
									__A   			= ids_tensor([self.batch_size]							,self.num_labels )

					__A   			= self.get_config()
					return config, pixel_values, labels






	def 				UpperCamelCase_  (       self					: str ):
					return CvtConfig(
					    image_size=self.image_size							,num_labels=self.num_labels							,num_channels=self.num_channels							,embed_dim=self.embed_dim							,num_heads=self.num_heads							,patch_sizes=self.patch_sizes							,patch_padding=self.patch_padding							,patch_stride=self.patch_stride							,stride_kv=self.stride_kv							,depth=self.depth							,cls_token=self.cls_token							,attention_drop_rate=self.attention_drop_rate							,initializer_range=self.initializer_range							,)






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Union[str, Any]							,A					: Tuple							,A					: Any ):
					__A   			= TFCvtModel(config=A )
					__A   			= model(A							,training=A )
					__A   			= (self.image_size, self.image_size)
					__A			,							__A   			= image_size[0], image_size[1]
					for i in range(len(self.depth ) ):
									__A   			= floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
									__A   			= floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
					self.parent.assertEqual(result.last_hidden_state.shape							,(self.batch_size, self.embed_dim[-1], height, width) )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: int							,A					: Tuple							,A					: List[Any] ):
					__A   			= self.num_labels
					__A   			= TFCvtForImageClassification(A )
					__A   			= model(A							,labels=A							,training=A )
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels) )







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





@require_tf
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
	snake_case_					    =     (
	    {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
	    if is_tf_available()
	    else {}
	)
	snake_case_					    =     False
	snake_case_					    =     False
	snake_case_					    =     False
	snake_case_					    =     False
	snake_case_					    =     False






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= TFCvtModelTester(self )
					__A   			= TFCvtConfigTester(self							,config_class=A							,has_text_modality=A							,hidden_size=37 )






	def 				UpperCamelCase_  (       self					: str ):
					self.config_tester.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()






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






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






	@unittest.skip(reason="Cvt does not support input and output embeddings" )
	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					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."							,)
	def 				UpperCamelCase_  (       self					: Any ):
					super().test_dataset_conversion()






	@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					: List[str] ):
					super().test_keras_fit()






	@unittest.skip(reason="Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8" )
	def 				UpperCamelCase_  (       self					: List[str] ):
					__A   			= tf.keras.mixed_precision.Policy("mixed_float16" )
					tf.keras.mixed_precision.set_global_policy(A )
					super().test_keras_fit()
					tf.keras.mixed_precision.set_global_policy("float32" )






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

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

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






	def 				UpperCamelCase_  (       self					: List[str] ):
					def check_hidden_states_output(A					: Union[str, Any]							,A					: int							,A					: List[Any] ):
									__A   			= model_class(A )

									__A   			= model(**self._prepare_for_class(A							,A ) )
									__A   			= outputs.hidden_states

									__A   			= len(self.model_tester.depth )
									self.assertEqual(len(A )							,A )

									# verify the first hidden states (first block)
									self.assertListEqual(
									    list(hidden_states[0].shape[-3:] )							,[
									        self.model_tester.embed_dim[0],
									        self.model_tester.image_size // 4,
									        self.model_tester.image_size // 4,
									    ]							,)

					__A			,							__A   			= self.model_tester.prepare_config_and_inputs_for_common()

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

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

									check_hidden_states_output(A							,A							,A )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A )






	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*A )







	@slow
	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A   			= TFCvtModel.from_pretrained(A )
									self.assertIsNotNone(A )





def 						UpperCAmelCase			(	)		->	Optional[Any]:







				"""simple docstring"""



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





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




	'''simple docstring'''





	@cached_property
	def 				UpperCamelCase_  (       self					: List[str] ):
					return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )







	@slow
	def 				UpperCamelCase_  (       self					: str ):
					__A   			= TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

					__A   			= self.default_image_processor
					__A   			= prepare_img()
					__A   			= image_processor(images=A							,return_tensors="tf" )

					# forward pass
					__A   			= model(**A )

					# verify the logits
					__A   			= tf.TensorShape((1, 10_00) )
					self.assertEqual(outputs.logits.shape							,A )

					__A   			= tf.constant([0.92_85, 0.90_15, -0.31_50] )
					self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()							,A							,atol=1E-4 ) )


import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


def 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				__A   			= args.pruning_method
				__A   			= args.threshold

				__A   			= args.model_name_or_path.rstrip("/"						)
				__A   			= args.target_model_path

				print(F'''Load fine-pruned model from {model_name_or_path}'''						)
				__A   			= torch.load(os.path.join(a_		,   "pytorch_model.bin"						)						)
				__A   			= {}

				for name, tensor in model.items():
								if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
												__A   			= tensor
												print(F'''Copied layer {name}'''						)
								elif "classifier" in name or "qa_output" in name:
												__A   			= tensor
												print(F'''Copied layer {name}'''						)
								elif "bias" in name:
												__A   			= tensor
												print(F'''Copied layer {name}'''						)
								else:
												if pruning_method == "magnitude":
																__A   			= MagnitudeBinarizer.apply(inputs=a_		,   threshold=a_						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												elif pruning_method == "topK":
																if "mask_scores" in name:
																				continue
																__A   			= name[:-6]
																__A   			= model[F'''{prefix_}mask_scores''']
																__A   			= TopKBinarizer.apply(a_		,   a_						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												elif pruning_method == "sigmoied_threshold":
																if "mask_scores" in name:
																				continue
																__A   			= name[:-6]
																__A   			= model[F'''{prefix_}mask_scores''']
																__A   			= ThresholdBinarizer.apply(a_		,   a_		,   a_						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												elif pruning_method == "l0":
																if "mask_scores" in name:
																				continue
																__A   			= name[:-6]
																__A   			= model[F'''{prefix_}mask_scores''']
																__A			,							__A   			= -0.1, 1.1
																__A   			= torch.sigmoid(a_						)
																__A   			= s * (r - l) + l
																__A   			= s_bar.clamp(min=0.0		,   max=1.0						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												else:
																raise ValueError("Unknown pruning method"						)

				if target_model_path is None:
								__A   			= os.path.join(
								    os.path.dirname(a_						)		,   F'''bertarized_{os.path.basename(a_						)}'''						)

				if not os.path.isdir(a_						):
								shutil.copytree(a_		,   a_						)
								print(F'''\nCreated folder {target_model_path}'''						)

				torch.save(a_		,   os.path.join(a_		,   "pytorch_model.bin"						)						)
				print("\nPruned model saved! See you later!"						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				argparse.ArgumentParser()

					parser.add_argument(
					    '--pruning_method',
					    choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
					    type=str,
					    required=True,
					    help=(
					        'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
					        ' sigmoied_threshold = Soft movement pruning)'
					    ),
					)
					parser.add_argument(
					    '--threshold',
					    type=float,
					    required=False,
					    help=(
					        'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
					        'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
					        'Not needed for `l0`'
					    ),
					)
					parser.add_argument(
					    '--model_name_or_path',
					    type=str,
					    required=True,
					    help='Folder containing the model that was previously fine-pruned',
					)
					parser.add_argument(
					    '--target_model_path',
					    default=None,
					    type=str,
					    required=False,
					    help='Folder containing the model that was previously fine-pruned',
					)

					SCREAMING_SNAKE_CASE							:str							 =				parser.parse_args()

					main(args)
