






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		lowerCamelCase_						     =  'platform'

		import jax
		import jax.numpy as jnp

		from transformers import MarianTokenizer
		from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right
def      lowerCamelCase					(						a_ ,						a_ ,						a_=None ,						a_=None ,						a_=None ,						a_=None ,						a_=None ,						a_=None ,						)	->			Dict:



							if attention_mask is None:
														lowerCAmelCase_										=     np.where(input_ids != config.pad_token_id ,						1 ,						0	)
							if decoder_attention_mask is None:
														lowerCAmelCase_										=     np.where(decoder_input_ids != config.pad_token_id ,						1 ,						0	)
							if head_mask is None:
														lowerCAmelCase_										=     np.ones((config.encoder_layers, config.encoder_attention_heads)	)
							if decoder_head_mask is None:
														lowerCAmelCase_										=     np.ones((config.decoder_layers, config.decoder_attention_heads)	)
							if cross_attn_head_mask is None:
														lowerCAmelCase_										=     np.ones((config.decoder_layers, config.decoder_attention_heads)	)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							}




class 							a_ :


							'''simple docstring'''






							def __init__(		self			,			lowercase_			,			lowercase_=1_3			,			lowercase_=7			,			lowercase_=True			,			lowercase_=False			,			lowercase_=9_9			,			lowercase_=1_6			,			lowercase_=2			,			lowercase_=4			,			lowercase_=4			,			lowercase_="gelu"			,			lowercase_=0.1			,			lowercase_=0.1			,			lowercase_=3_2			,			lowercase_=2			,			lowercase_=1			,			lowercase_=0			,			lowercase_=0.02			,			)					->					List[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     parent
														lowerCAmelCase_										=     batch_size
														lowerCAmelCase_										=     seq_length
														lowerCAmelCase_										=     is_training
														lowerCAmelCase_										=     use_labels
														lowerCAmelCase_										=     vocab_size
														lowerCAmelCase_										=     hidden_size
														lowerCAmelCase_										=     num_hidden_layers
														lowerCAmelCase_										=     num_attention_heads
														lowerCAmelCase_										=     intermediate_size
														lowerCAmelCase_										=     hidden_act
														lowerCAmelCase_										=     hidden_dropout_prob
														lowerCAmelCase_										=     attention_probs_dropout_prob
														lowerCAmelCase_										=     max_position_embeddings
														lowerCAmelCase_										=     eos_token_id
														lowerCAmelCase_										=     pad_token_id
														lowerCAmelCase_										=     bos_token_id
														lowerCAmelCase_										=     initializer_range

							def      _lowercase       (		self   )					->					Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_										=     np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,			self.vocab_size   )			,			3			,			self.vocab_size   )
														lowerCAmelCase_										=     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,			dtype=np.intaa   ))			,			-1   )

														lowerCAmelCase_										=     shift_tokens_right(lowercase_			,			1			,			2   )

														lowerCAmelCase_										=     MarianConfig(
														    vocab_size=self.vocab_size			,			d_model=self.hidden_size			,			encoder_layers=self.num_hidden_layers			,			decoder_layers=self.num_hidden_layers			,			encoder_attention_heads=self.num_attention_heads			,			decoder_attention_heads=self.num_attention_heads			,			encoder_ffn_dim=self.intermediate_size			,			decoder_ffn_dim=self.intermediate_size			,			dropout=self.hidden_dropout_prob			,			attention_dropout=self.attention_probs_dropout_prob			,			max_position_embeddings=self.max_position_embeddings			,			eos_token_id=self.eos_token_id			,			bos_token_id=self.bos_token_id			,			pad_token_id=self.pad_token_id			,			initializer_range=self.initializer_range			,			use_cache=lowercase_			,			)
														lowerCAmelCase_										=     prepare_marian_inputs_dict(lowercase_			,			lowercase_			,			lowercase_   )
														return config, inputs_dict

							def      _lowercase       (		self   )					->					List[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     self.prepare_config_and_inputs()
														return config, inputs_dict

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_   )					->					List[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     2_0
														lowerCAmelCase_										=     model_class_name(lowercase_   )

														lowerCAmelCase_										=     model.encode(inputs_dict['input_ids']   )

														lowerCAmelCase_										=     (
														    inputs_dict["decoder_input_ids"],
														    inputs_dict["decoder_attention_mask"],
														)

														lowerCAmelCase_										=     model.init_cache(decoder_input_ids.shape[0]			,			lowercase_			,			lowercase_   )
														lowerCAmelCase_										=     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,			dtype='i4'   )

														lowerCAmelCase_										=     jnp.broadcast_to(
														    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]			,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,			)
														lowerCAmelCase_										=     model.decode(
														    decoder_input_ids[:, :-1]			,			lowercase_			,			decoder_attention_mask=lowercase_			,			past_key_values=lowercase_			,			decoder_position_ids=lowercase_			,			)

														lowerCAmelCase_										=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,			dtype='i4'   )
														lowerCAmelCase_										=     model.decode(
														    decoder_input_ids[:, -1:]			,			lowercase_			,			decoder_attention_mask=lowercase_			,			past_key_values=outputs_cache.past_key_values			,			decoder_position_ids=lowercase_			,			)

														lowerCAmelCase_										=     model.decode(lowercase_			,			lowercase_   )

														lowerCAmelCase_										=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
														self.parent.assertTrue(diff < 1e-3			,			msg=f'''Max diff is {diff}'''   )






							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_   )					->					List[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     2_0
														lowerCAmelCase_										=     model_class_name(lowercase_   )

														lowerCAmelCase_										=     model.encode(inputs_dict['input_ids']   )

														lowerCAmelCase_										=     (
														    inputs_dict["decoder_input_ids"],
														    inputs_dict["decoder_attention_mask"],
														)

														lowerCAmelCase_										=     jnp.concatenate(
														    [
														        decoder_attention_mask,
														        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
														    ]			,			axis=-1			,			)

														lowerCAmelCase_										=     model.init_cache(decoder_input_ids.shape[0]			,			lowercase_			,			lowercase_   )
														lowerCAmelCase_										=     jnp.broadcast_to(
														    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]			,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,			)

														lowerCAmelCase_										=     model.decode(
														    decoder_input_ids[:, :-1]			,			lowercase_			,			decoder_attention_mask=lowercase_			,			past_key_values=lowercase_			,			decoder_position_ids=lowercase_			,			)
														lowerCAmelCase_										=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,			dtype='i4'   )
														lowerCAmelCase_										=     model.decode(
														    decoder_input_ids[:, -1:]			,			lowercase_			,			past_key_values=outputs_cache.past_key_values			,			decoder_attention_mask=lowercase_			,			decoder_position_ids=lowercase_			,			)

														lowerCAmelCase_										=     model.decode(lowercase_			,			lowercase_			,			decoder_attention_mask=lowercase_   )

														lowerCAmelCase_										=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
														self.parent.assertTrue(diff < 1e-3			,			msg=f'''Max diff is {diff}'''   )




@require_flax
class 							a_ (						__snake_case	,	unittest.TestCase	,	__snake_case		):


							'''simple docstring'''






							__a:	Any     =    True
							__a:	Dict     =    (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
							__a:	Optional[int]     =    (FlaxMarianMTModel,) if is_flax_available() else ()

							def      _lowercase       (		self   )					->					int:
														'''simple docstring'''

														lowerCAmelCase_										=     FlaxMarianModelTester(self   )

							def      _lowercase       (		self   )					->					List[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     self.model_tester.prepare_config_and_inputs()
														for model_class in self.all_model_classes:
																					self.model_tester.check_use_cache_forward(lowercase_			,			lowercase_			,			lowercase_   )

							def      _lowercase       (		self   )					->					str:
														'''simple docstring'''

														lowerCAmelCase_										=     self.model_tester.prepare_config_and_inputs()
														for model_class in self.all_model_classes:
																					self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_			,			lowercase_			,			lowercase_   )

							def      _lowercase       (		self   )					->					str:
														'''simple docstring'''

														lowerCAmelCase_										=     self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__   ):
																												lowerCAmelCase_										=     self._prepare_for_class(lowercase_			,			lowercase_   )
																												lowerCAmelCase_										=     model_class(lowercase_   )

																												@jax.jit
																												def encode_jitted(lowercase_			,			lowercase_=None			,			**lowercase_   ):
																																			return model.encode(input_ids=lowercase_			,			attention_mask=lowercase_   )

																												with self.subTest('JIT Enabled'   ):
																																			lowerCAmelCase_										=     encode_jitted(**lowercase_   ).to_tuple()

																												with self.subTest('JIT Disabled'   ):
																																			with jax.disable_jit():
																																										lowerCAmelCase_										=     encode_jitted(**lowercase_   ).to_tuple()

																												self.assertEqual(len(lowercase_   )			,			len(lowercase_   )   )
																												for jitted_output, output in zip(lowercase_			,			lowercase_   ):
																																			self.assertEqual(jitted_output.shape			,			output.shape   )

							def      _lowercase       (		self   )					->					Any:
														'''simple docstring'''

														lowerCAmelCase_										=     self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__   ):
																												lowerCAmelCase_										=     model_class(lowercase_   )
																												lowerCAmelCase_										=     model.encode(inputs_dict['input_ids']			,			inputs_dict['attention_mask']   )

																												lowerCAmelCase_										=     {
																												    "decoder_input_ids": inputs_dict["decoder_input_ids"],
																												    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
																												    "encoder_outputs": encoder_outputs,
																												}

																												@jax.jit
																												def decode_jitted(lowercase_			,			lowercase_			,			lowercase_   ):
																																			return model.decode(
																																			    decoder_input_ids=lowercase_			,			decoder_attention_mask=lowercase_			,			encoder_outputs=lowercase_			,			)

																												with self.subTest('JIT Enabled'   ):
																																			lowerCAmelCase_										=     decode_jitted(**lowercase_   ).to_tuple()

																												with self.subTest('JIT Disabled'   ):
																																			with jax.disable_jit():
																																										lowerCAmelCase_										=     decode_jitted(**lowercase_   ).to_tuple()

																												self.assertEqual(len(lowercase_   )			,			len(lowercase_   )   )
																												for jitted_output, output in zip(lowercase_			,			lowercase_   ):
																																			self.assertEqual(jitted_output.shape			,			output.shape   )






							@slow
							def      _lowercase       (		self   )					->					Union[str, Any]:
														'''simple docstring'''

														for model_class_name in self.all_model_classes:
																					lowerCAmelCase_										=     model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'   )
																					# FlaxMarianForSequenceClassification expects eos token in input_ids
																					lowerCAmelCase_										=     np.ones((1, 1)   ) * model.config.eos_token_id
																					lowerCAmelCase_										=     model(lowercase_   )
																					self.assertIsNotNone(lowercase_   )




@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_ (						unittest.TestCase		):


							'''simple docstring'''






							__a:	Union[str, Any]     =    None
							__a:	List[str]     =    None

							@classmethod
							def      _lowercase       (		cls   )					->					str:
														'''simple docstring'''

														lowerCAmelCase_										=     f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
														return cls

							@cached_property
							def      _lowercase       (		self   )					->					Optional[int]:
														'''simple docstring'''

														return MarianTokenizer.from_pretrained(self.model_name   )

							@property
							def      _lowercase       (		self   )					->					Any:
														'''simple docstring'''

														return self.tokenizer.eos_token_id

							@cached_property
							def      _lowercase       (		self   )					->					List[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     FlaxMarianMTModel.from_pretrained(self.model_name   )
														self.assertEqual(model.config.decoder_start_token_id			,			model.config.pad_token_id   )
														return model

							def      _lowercase       (		self			,			**lowercase_   )					->					Optional[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     self.translate_src_text(**lowercase_   )
														self.assertListEqual(self.expected_text			,			lowercase_   )






							def      _lowercase       (		self			,			**lowercase_   )					->					str:
														'''simple docstring'''

														lowerCAmelCase_										=     self.tokenizer(self.src_text			,			padding=lowercase_			,			return_tensors='np'			,			**lowercase_   )
														lowerCAmelCase_										=     self.model.generate(
														    model_inputs.input_ids			,			attention_mask=model_inputs.attention_mask			,			num_beams=2			,			max_length=1_2_8			,			).sequences
														lowerCAmelCase_										=     self.tokenizer.batch_decode(lowercase_			,			skip_special_tokens=lowercase_   )
														return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_ (						__snake_case		):


							'''simple docstring'''






							__a:	str     =    '''en'''
							__a:	Optional[int]     =    '''fr'''
							__a:	Tuple     =    [
							    '''I am a small frog.''',
							    '''Now I can forget the 100 words of german that I know.''',
							]
							__a:	Any     =    [
							    '''Je suis une petite grenouille.''',
							    '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
							]

							@slow
							def      _lowercase       (		self   )					->					int:
														'''simple docstring'''

														self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_ (						__snake_case		):


							'''simple docstring'''






							__a:	Optional[int]     =    '''fr'''
							__a:	Tuple     =    '''en'''
							__a:	Dict     =    [
							    '''Donnez moi le micro.''',
							    '''Tom et Mary étaient assis à une table.''',  # Accents
							]
							__a:	List[str]     =    [
							    '''Give me the microphone.''',
							    '''Tom and Mary were sitting at a table.''',
							]

							@slow
							def      _lowercase       (		self   )					->					List[Any]:
														'''simple docstring'''

														self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_ (						__snake_case		):


							'''simple docstring'''






							__a:	Tuple     =    '''mt'''
							__a:	str     =    '''en'''
							__a:	Dict     =    ['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
							__a:	str     =    ['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']

							@slow
							def      _lowercase       (		self   )					->					Any:
														'''simple docstring'''

														self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_ (						__snake_case		):


							'''simple docstring'''






							__a:	Dict     =    '''en'''
							__a:	Optional[Any]     =    '''de'''
							__a:	Dict     =    [
							    '''I am a small frog.''',
							    '''Now I can forget the 100 words of german that I know.''',
							    '''Tom asked his teacher for advice.''',
							    '''That\'s how I would do it.''',
							    '''Tom really admired Mary\'s courage.''',
							    '''Turn around and close your eyes.''',
							]
							__a:	List[Any]     =    [
							    '''Ich bin ein kleiner Frosch.''',
							    '''Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.''',
							    '''Tom bat seinen Lehrer um Rat.''',
							    '''So würde ich das machen.''',
							    '''Tom bewunderte Marias Mut wirklich.''',
							    '''Drehen Sie sich um und schließen Sie die Augen.''',
							]

							@slow
							def      _lowercase       (		self   )					->					Any:
														'''simple docstring'''

														self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_ (						__snake_case		):


							'''simple docstring'''






							__a:	Optional[int]     =    '''en'''
							__a:	Any     =    '''zh'''
							__a:	Any     =    ['''My name is Wolfgang and I live in Berlin''']
							__a:	str     =    ['''我叫沃尔夫冈 我住在柏林''']

							@slow
							def      _lowercase       (		self   )					->					List[Any]:
														'''simple docstring'''

														self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_ (						__snake_case		):


							'''simple docstring'''






							__a:	List[Any]     =    '''ru'''
							__a:	Optional[int]     =    '''fr'''
							__a:	List[str]     =    ['''Он показал мне рукопись своей новой пьесы.''']
							__a:	Optional[int]     =    ['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']

							@slow
							def      _lowercase       (		self   )					->					Dict:
														'''simple docstring'''

														self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class 							a_ (						__snake_case		):


							'''simple docstring'''






							__a:	Any     =    '''en'''
							__a:	List[str]     =    '''ROMANCE'''
							__a:	int     =    [
							    '''>>fr<< Don\'t spend so much time watching TV.''',
							    '''>>pt<< Your message has been sent.''',
							    '''>>es<< He\'s two years older than me.''',
							]
							__a:	Optional[Any]     =    [
							    '''Ne passez pas autant de temps à regarder la télé.''',
							    '''A sua mensagem foi enviada.''',
							    '''Es dos años más viejo que yo.''',
							]

							@slow
							def      _lowercase       (		self   )					->					int:
														'''simple docstring'''

														self._assert_generated_batch_equal_expected()




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 					snake_case						(__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						_snake_case						:     List[str]				=    args.serialization_dir
						_snake_case						:     Union[str, Any]				=    args.pruning_method
						_snake_case						:     Any				=    args.threshold

						_snake_case						:     Union[str, Any]				=    torch.load(os.path.join(__lowercase					,			"pytorch_model.bin"						)					,			map_location="cpu"						)

						_snake_case						:     Dict				=    0  # Number of remaining (not pruned) params in the encoder
						_snake_case						:     int				=    0  # Number of params in the encoder

						print("name".ljust(60					,			" "						)					,			"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						:     Union[str, Any]				=    TopKBinarizer.apply(__lowercase					,			__lowercase						).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								_snake_case						:     str				=    ThresholdBinarizer.apply(__lowercase					,			__lowercase					,			__lowercase						).sum().item()
																		elif pruning_method == "l0":
																								_snake_case				,_snake_case						:     List[str]				=    -0.1, 1.1
																								_snake_case						:     Any				=    torch.sigmoid(__lowercase						)
																								_snake_case						:     str				=    s * (r - l) + l
																								_snake_case						:     Dict				=    s_bar.clamp(min=0.0					,			max=1.0						)
																								_snake_case						:     Tuple				=    (mask > 0.0).sum().item()
																		else:
																								raise ValueError("Unknown pruning method"						)
																		remaining_count += mask_ones
																		print(name.ljust(60					,			" "						)					,			str(round(100 * mask_ones / param.numel()					,			3						)						).ljust(20					,			" "						)					,			str(__lowercase						)						)
												else:
																		encoder_count += param.numel()
																		if "bias" in name or "LayerNorm" in name:
																								remaining_count += param.numel()

						print(""						)
						print("Remaining Weights (global) %: "					,			100 * remaining_count / encoder_count						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    str     = 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',
				)

				__SCREAMING_SNAKE_CASE						:    Any     = parser.parse_args()

				main(args)