

'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.ctrl.modeling_tf_ctrl import (
						    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCTRLForSequenceClassification,
						    TFCTRLLMHeadModel,
						    TFCTRLModel,
						)







class lowerCamelCase   (    __lowerCAmelCase				):

				def __init__(       self,						lowercase_,						)    ->							Any:
					snake_case							=					parent
					snake_case							=					13
					snake_case							=					7
					snake_case							=					True
					snake_case							=					True
					snake_case							=					True
					snake_case							=					True
					snake_case							=					True
					snake_case							=					99
					snake_case							=					32
					snake_case							=					2
					snake_case							=					4
					snake_case							=					37
					snake_case							=					'gelu'
					snake_case							=					0.1
					snake_case							=					0.1
					snake_case							=					512
					snake_case							=					16
					snake_case							=					2
					snake_case							=					0.02
					snake_case							=					3
					snake_case							=					4
					snake_case							=					None
					snake_case							=					self.vocab_size - 1

				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					ids_tensor([self.batch_size, self.seq_length],						self.vocab_size )

					snake_case							=					None
					if self.use_input_mask:
						snake_case							=					random_attention_mask([self.batch_size, self.seq_length] )

					snake_case							=					None
					if self.use_token_type_ids:
						snake_case							=					ids_tensor([self.batch_size, self.seq_length],						self.type_vocab_size )

					snake_case							=					None
					if self.use_mc_token_ids:
						snake_case							=					ids_tensor([self.batch_size, self.num_choices],						self.seq_length )

					snake_case							=					None
					snake_case							=					None
					snake_case							=					None
					if self.use_labels:
						snake_case							=					ids_tensor([self.batch_size],						self.type_sequence_label_size )
						snake_case							=					ids_tensor([self.batch_size, self.seq_length],						self.num_labels )
						snake_case							=					ids_tensor([self.batch_size],						self.num_choices )

					snake_case							=					CTRLConfig(
					    vocab_size=self.vocab_size,						n_embd=self.hidden_size,						n_layer=self.num_hidden_layers,						n_head=self.num_attention_heads,						n_positions=self.max_position_embeddings,						pad_token_id=self.pad_token_id,						)

					snake_case							=					ids_tensor([self.num_hidden_layers, self.num_attention_heads],						2 )

					return (
					    config,
					    input_ids,
					    input_mask,
					    head_mask,
					    token_type_ids,
					    mc_token_ids,
					    sequence_labels,
					    token_labels,
					    choice_labels,
					)

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						*lowercase_ )    ->							Dict:
					snake_case							=					TFCTRLModel(config=lowercase_ )
					snake_case							=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
					snake_case							=					model(lowercase_ )

					snake_case							=					[input_ids, None, input_mask]  # None is the input for 'past'
					snake_case							=					model(lowercase_ )

					snake_case							=					model(lowercase_ )

					self.parent.assertEqual(result.last_hidden_state.shape,						(self.batch_size, self.seq_length, self.hidden_size) )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						*lowercase_ )    ->							Tuple:
					snake_case							=					TFCTRLLMHeadModel(config=lowercase_ )
					snake_case							=					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
					snake_case							=					model(lowercase_ )
					self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.seq_length, self.vocab_size) )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						*lowercase_ )    ->							Optional[Any]:
					snake_case							=					self.num_labels
					snake_case							=					ids_tensor([self.batch_size],						self.type_sequence_label_size )
					snake_case							=					{
					    'input_ids': input_ids,
					    'token_type_ids': token_type_ids,
					    'labels': sequence_labels,
					}
					snake_case							=					TFCTRLForSequenceClassification(lowercase_ )
					snake_case							=					model(lowercase_ )
					self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.num_labels) )






				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					self.prepare_config_and_inputs()

					(
					    (
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			(
					    snake_case
					)			,			
					)							=					config_and_inputs

					snake_case							=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
					return config, inputs_dict







@require_tf
class lowerCamelCase   (    __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
				snake_case_							     = (TFCTRLLMHeadModel,) if is_tf_available() else ()
				snake_case_							     = (
				    {
				        '''feature-extraction''': TFCTRLModel,
				        '''text-classification''': TFCTRLForSequenceClassification,
				        '''text-generation''': TFCTRLLMHeadModel,
				        '''zero-shot''': TFCTRLForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				snake_case_							     = False
				snake_case_							     = False

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							Tuple:
					if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
						# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
						# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
						# config could not be created.
						return True

					return False

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					TFCTRLModelTester(self )
					snake_case							=					ConfigTester(self,						config_class=lowercase_,						n_embd=37 )

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					self.config_tester.run_common_tests()

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_ctrl_model(*lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_ctrl_lm_head(*lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							List[str]:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_ctrl_for_sequence_classification(*lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()
					snake_case							=					[TFCTRLLMHeadModel]
					snake_case							=					[TFCTRLForSequenceClassification]

					for model_class in self.all_model_classes:
						snake_case							=					model_class(lowercase_ )
						model.build()  # may be needed for the get_bias() call below
						assert isinstance(model.get_input_embeddings(),						tf.keras.layers.Layer )

						if model_class in list_lm_models:
							snake_case							=					model.get_output_embeddings()
							assert isinstance(lowercase_,						tf.keras.layers.Layer )
							snake_case							=					model.get_bias()
							assert isinstance(lowercase_,						lowercase_ )
							for k, v in name.items():
								assert isinstance(lowercase_,						tf.Variable )
						elif model_class in list_other_models_with_output_ebd:
							snake_case							=					model.get_output_embeddings()
							assert isinstance(lowercase_,						tf.keras.layers.Layer )
							snake_case							=					model.get_bias()
							assert name is None
						else:
							snake_case							=					model.get_output_embeddings()
							assert x is None
							snake_case							=					model.get_bias()
							assert name is None






				@slow
				def 					_lowerCamelCase    (       self )    ->							Tuple:
					for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						snake_case							=					TFCTRLModel.from_pretrained(lowercase_ )
						self.assertIsNotNone(lowercase_ )







@require_tf
class lowerCamelCase   (    unittest.TestCase				):

				@slow
				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					TFCTRLLMHeadModel.from_pretrained('ctrl' )
					snake_case							=					tf.convert_to_tensor([[11859, 0, 1611, 8]],						dtype=tf.intaa )  # Legal the president is
					snake_case							=					[
					    11859,
					    0,
					    1611,
					    8,
					    5,
					    150,
					    26449,
					    2,
					    19,
					    348,
					    469,
					    3,
					    2595,
					    48,
					    20740,
					    246533,
					    246533,
					    19,
					    30,
					    5,
					]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

					snake_case							=					model.generate(lowercase_,						do_sample=lowercase_ )
					self.assertListEqual(output_ids[0].numpy().tolist(),						lowercase_ )








'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def      __magic_name__							(			A				)      ->		str:
	snake_case							=					args.serialization_dir
	snake_case							=					args.pruning_method
	snake_case							=					args.threshold

	snake_case							=					torch.load(os.path.join(A       , 'pytorch_model.bin'				)       , map_location='cpu'				)

	snake_case							=					0  # Number of remaining (not pruned) params in the encoder
	snake_case							=					0  # Number of params in the encoder

	print('name'.ljust(6_0       , ' '				)       , 'Remaining Weights %'       , 'Remaining Weight'				)
	for name, param in st.items():
		if "encoder" not in name:
			continue

		if "mask_scores" in name:
			if pruning_method == "topK":
				snake_case							=					TopKBinarizer.apply(A       , A				).sum().item()
			elif pruning_method == "sigmoied_threshold":
				snake_case							=					ThresholdBinarizer.apply(A       , A       , A				).sum().item()
			elif pruning_method == "l0":
				snake_case			,			snake_case							=					-0.1, 1.1
				snake_case							=					torch.sigmoid(A				)
				snake_case							=					s * (r - l) + l
				snake_case							=					s_bar.clamp(min=0.0       , max=1.0				)
				snake_case							=					(mask > 0.0).sum().item()
			else:
				raise ValueError('Unknown pruning method'				)
			remaining_count += mask_ones
			print(name.ljust(6_0       , ' '				)       , str(round(1_0_0 * mask_ones / param.numel()       , 3				)				).ljust(2_0       , ' '				)       , str(A				)				)
		else:
			encoder_count += param.numel()
			if "bias" in name or "LayerNorm" in name:
				remaining_count += param.numel()

	print(''				)
	print('Remaining Weights (global) %: '       , 1_0_0 * remaining_count / encoder_count				)


if __name__ == "__main__":
						lowerCAmelCase_     =      argparse.ArgumentParser()

						parser.add_argument(
						    "--pruning_method",
						    choices=["l0", "topK", "sigmoied_threshold"],
						    type=str,
						    required=True,
						    help=(
						        "Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"
						        " pruning)"
						    ),
						)
						parser.add_argument(
						    "--threshold",
						    type=float,
						    required=False,
						    help=(
						        "For `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(
						    "--serialization_dir",
						    type=str,
						    required=True,
						    help="Folder containing the model that was previously fine-pruned",
						)

						lowerCAmelCase_     =      parser.parse_args()

						main(args)





