


'''simple docstring'''


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, 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 AutoTokenizer
						from transformers.models.mbart.modeling_flax_mbart import (
						    FlaxMBartForConditionalGeneration,
						    FlaxMBartForQuestionAnswering,
						    FlaxMBartForSequenceClassification,
						    FlaxMBartModel,
						    shift_tokens_right,
						)






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase=None   ,			__lowerCAmelCase=None   ,			__lowerCAmelCase=None   ,			__lowerCAmelCase=None   ,			__lowerCAmelCase=None   ,			__lowerCAmelCase=None   ,			):
		if attention_mask is None:
				_UpperCAmelCase		:							List[Any]             =   np.where(input_ids != config.pad_token_id   ,			1   ,			0 )
		if decoder_attention_mask is None:
				_UpperCAmelCase		:							Dict             =   np.where(decoder_input_ids != config.pad_token_id   ,			1   ,			0 )
		if head_mask is None:
				_UpperCAmelCase		:							str             =   np.ones((config.encoder_layers, config.encoder_attention_heads) )
		if decoder_head_mask is None:
				_UpperCAmelCase		:							Optional[int]             =   np.ones((config.decoder_layers, config.decoder_attention_heads) )
		if cross_attn_head_mask is None:
				_UpperCAmelCase		:							str             =   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": decoder_attention_mask,
		}






class 						lowerCAmelCase__      (      unittest.TestCase						):




			def __init__(		self	:  Any						,	lowerCamelCase__	:  Union[str, Any]						,	lowerCamelCase__	:  List[str]=13						,	lowerCamelCase__	:  int=7						,	lowerCamelCase__	:  Dict=True						,	lowerCamelCase__	:  Dict=False						,	lowerCamelCase__	:  Optional[int]=99						,	lowerCamelCase__	:  Optional[int]=16						,	lowerCamelCase__	:  Tuple=2						,	lowerCamelCase__	:  Dict=4						,	lowerCamelCase__	:  List[Any]=4						,	lowerCamelCase__	:  Optional[int]="gelu"						,	lowerCamelCase__	:  str=0.1						,	lowerCamelCase__	:  Dict=0.1						,	lowerCamelCase__	:  Optional[Any]=32						,	lowerCamelCase__	:  Tuple=2						,	lowerCamelCase__	:  Optional[Any]=1						,	lowerCamelCase__	:  Tuple=0						,	lowerCamelCase__	:  Optional[int]=2						,	lowerCamelCase__	:  str=0.0_2						,	) ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:							str             =   parent
					_UpperCAmelCase		:							Optional[Any]             =   batch_size
					_UpperCAmelCase		:							Any             =   seq_length
					_UpperCAmelCase		:							List[Any]             =   is_training
					_UpperCAmelCase		:							Union[str, Any]             =   use_labels
					_UpperCAmelCase		:							Tuple             =   vocab_size
					_UpperCAmelCase		:							Optional[int]             =   hidden_size
					_UpperCAmelCase		:							Dict             =   num_hidden_layers
					_UpperCAmelCase		:							Optional[int]             =   num_attention_heads
					_UpperCAmelCase		:							Tuple             =   intermediate_size
					_UpperCAmelCase		:							List[str]             =   hidden_act
					_UpperCAmelCase		:							List[Any]             =   hidden_dropout_prob
					_UpperCAmelCase		:							Any             =   attention_probs_dropout_prob
					_UpperCAmelCase		:							int             =   max_position_embeddings
					_UpperCAmelCase		:							Optional[int]             =   eos_token_id
					_UpperCAmelCase		:							str             =   pad_token_id
					_UpperCAmelCase		:							Any             =   bos_token_id
					_UpperCAmelCase		:							int             =   decoder_start_token_id
					_UpperCAmelCase		:							int             =   initializer_range




			def 				lowerCAmelCase__     (		self	:  Tuple			) ->List[Any]:



					'''simple docstring'''







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

					_UpperCAmelCase		:							int             =   shift_tokens_right(lowerCamelCase__						,	1			)

					_UpperCAmelCase		:							int             =   MBartConfig(
					    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						,	decoder_start_token_id=self.decoder_start_token_id						,	initializer_range=self.initializer_range						,	use_cache=lowerCamelCase__						,	)
					_UpperCAmelCase		:							Optional[int]             =   prepare_mbart_inputs_dict(lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__			)
					return config, inputs_dict




			def 				lowerCAmelCase__     (		self	:  Tuple			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							str             =   self.prepare_config_and_inputs()
					return config, inputs_dict




			def 				lowerCAmelCase__     (		self	:  int						,	lowerCamelCase__	:  int						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  Any			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Any             =   20
					_UpperCAmelCase		:							Optional[Any]             =   model_class_name(lowerCamelCase__			)

					_UpperCAmelCase		:							Union[str, Any]             =   model.encode(inputs_dict["input_ids"]			)

					_UpperCAmelCase       ,     _UpperCAmelCase		:							Union[str, Any]             =   (
					    inputs_dict["decoder_input_ids"],
					    inputs_dict["decoder_attention_mask"],
					)

					_UpperCAmelCase		:							Any             =   model.init_cache(decoder_input_ids.shape[0]						,	lowerCamelCase__						,	lowerCamelCase__			)
					_UpperCAmelCase		:							Union[str, Any]             =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,	dtype="i4"			)

					_UpperCAmelCase		:							str             =   jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]						,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,	)
					_UpperCAmelCase		:							Dict             =   model.decode(
					    decoder_input_ids[:, :-1]						,	lowerCamelCase__						,	decoder_attention_mask=lowerCamelCase__						,	past_key_values=lowerCamelCase__						,	decoder_position_ids=lowerCamelCase__						,	)

					_UpperCAmelCase		:							Optional[int]             =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,	dtype="i4"			)
					_UpperCAmelCase		:							Tuple             =   model.decode(
					    decoder_input_ids[:, -1:]						,	lowerCamelCase__						,	decoder_attention_mask=lowerCamelCase__						,	past_key_values=outputs_cache.past_key_values						,	decoder_position_ids=lowerCamelCase__						,	)

					_UpperCAmelCase		:							Tuple             =   model.decode(lowerCamelCase__						,	lowerCamelCase__			)

					_UpperCAmelCase		:							Optional[Any]             =   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 				lowerCAmelCase__     (		self	:  List[str]						,	lowerCamelCase__	:  Optional[Any]						,	lowerCamelCase__	:  Tuple						,	lowerCamelCase__	:  int			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[Any]             =   20
					_UpperCAmelCase		:							List[str]             =   model_class_name(lowerCamelCase__			)

					_UpperCAmelCase		:							int             =   model.encode(inputs_dict["input_ids"]			)

					_UpperCAmelCase       ,     _UpperCAmelCase		:							str             =   (
					    inputs_dict["decoder_input_ids"],
					    inputs_dict["decoder_attention_mask"],
					)

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

					_UpperCAmelCase		:							Any             =   model.init_cache(decoder_input_ids.shape[0]						,	lowerCamelCase__						,	lowerCamelCase__			)
					_UpperCAmelCase		:							Any             =   jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]						,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,	)

					_UpperCAmelCase		:							List[str]             =   model.decode(
					    decoder_input_ids[:, :-1]						,	lowerCamelCase__						,	decoder_attention_mask=lowerCamelCase__						,	past_key_values=lowerCamelCase__						,	decoder_position_ids=lowerCamelCase__						,	)
					_UpperCAmelCase		:							int             =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,	dtype="i4"			)
					_UpperCAmelCase		:							str             =   model.decode(
					    decoder_input_ids[:, -1:]						,	lowerCamelCase__						,	past_key_values=outputs_cache.past_key_values						,	decoder_attention_mask=lowerCamelCase__						,	decoder_position_ids=lowerCamelCase__						,	)

					_UpperCAmelCase		:							Optional[Any]             =   model.decode(lowerCamelCase__						,	lowerCamelCase__						,	decoder_attention_mask=lowerCamelCase__			)

					_UpperCAmelCase		:							List[str]             =   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 						lowerCAmelCase__      (      unittest.TestCase						):
			lowerCAmelCase			:						Union[str, Any]							 =  99




			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->str:



					'''simple docstring'''







					_UpperCAmelCase		:							Dict             =   np.array(
					    [
					        [71, 82, 18, 33, 46, 91, 2],
					        [68, 34, 26, 58, 30, 82, 2],
					        [5, 97, 17, 39, 94, 40, 2],
					        [76, 83, 94, 25, 70, 78, 2],
					        [87, 59, 41, 35, 48, 66, 2],
					        [55, 13, 16, 58, 5, 2, 1],  # note padding
					        [64, 27, 31, 51, 12, 75, 2],
					        [52, 64, 86, 17, 83, 39, 2],
					        [48, 61, 9, 24, 71, 82, 2],
					        [26, 1, 60, 48, 22, 13, 2],
					        [21, 5, 62, 28, 14, 76, 2],
					        [45, 98, 37, 86, 59, 48, 2],
					        [70, 70, 50, 9, 28, 0, 2],
					    ]						,	dtype=np.intaa						,	)

					_UpperCAmelCase		:							Optional[int]             =   input_ids.shape[0]
					_UpperCAmelCase		:							Union[str, Any]             =   MBartConfig(
					    vocab_size=self.vocab_size						,	d_model=24						,	encoder_layers=2						,	decoder_layers=2						,	encoder_attention_heads=2						,	decoder_attention_heads=2						,	encoder_ffn_dim=32						,	decoder_ffn_dim=32						,	max_position_embeddings=48						,	eos_token_id=2						,	pad_token_id=1						,	bos_token_id=0						,	)
					return config, input_ids, batch_size




			def 				lowerCAmelCase__     (		self	:  Any			) ->str:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase		:							Union[str, Any]             =   self._get_config_and_data()
					_UpperCAmelCase		:							Tuple             =   FlaxMBartForSequenceClassification(lowerCamelCase__			)
					_UpperCAmelCase		:							Dict             =   model(input_ids=lowerCamelCase__						,	decoder_input_ids=lowerCamelCase__			)
					_UpperCAmelCase		:							Dict             =   (batch_size, config.num_labels)
					self.assertEqual(outputs["logits"].shape						,	lowerCamelCase__			)




			def 				lowerCAmelCase__     (		self	:  Tuple			) ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase		:							Optional[int]             =   self._get_config_and_data()
					_UpperCAmelCase		:							str             =   FlaxMBartForQuestionAnswering(lowerCamelCase__			)
					_UpperCAmelCase		:							Any             =   model(input_ids=lowerCamelCase__			)

					self.assertEqual(outputs["start_logits"].shape						,	input_ids.shape			)
					self.assertEqual(outputs["end_logits"].shape						,	input_ids.shape			)




			def 				lowerCAmelCase__     (		self	:  List[Any]			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase		:							Dict             =   self._get_config_and_data()
					_UpperCAmelCase		:							Optional[Any]             =   FlaxMBartForConditionalGeneration(lowerCamelCase__			)
					_UpperCAmelCase		:							Optional[int]             =   lm_model(input_ids=lowerCamelCase__			)
					_UpperCAmelCase		:							Optional[int]             =   (batch_size, input_ids.shape[1], config.vocab_size)
					self.assertEqual(outputs["logits"].shape						,	lowerCamelCase__			)




			def 				lowerCAmelCase__     (		self	:  Dict			) ->str:



					'''simple docstring'''







					_UpperCAmelCase		:							Union[str, Any]             =   MBartConfig(
					    vocab_size=self.vocab_size						,	d_model=14						,	encoder_layers=2						,	decoder_layers=2						,	encoder_attention_heads=2						,	decoder_attention_heads=2						,	encoder_ffn_dim=8						,	decoder_ffn_dim=8						,	max_position_embeddings=48						,	)
					_UpperCAmelCase		:							Optional[int]             =   FlaxMBartForConditionalGeneration(lowerCamelCase__			)
					_UpperCAmelCase		:							Union[str, Any]             =   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						,	dtype=np.intaa			)
					_UpperCAmelCase		:							int             =   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						,	dtype=np.intaa			)
					_UpperCAmelCase		:							str             =   lm_model(input_ids=lowerCamelCase__						,	decoder_input_ids=lowerCamelCase__			)
					_UpperCAmelCase		:							Any             =   (*summary.shape, config.vocab_size)
					self.assertEqual(outputs["logits"].shape						,	lowerCamelCase__			)






			def 				lowerCAmelCase__     (		self	:  Optional[int]			) ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:							List[str]             =   np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						,	dtype=np.intaa			)
					_UpperCAmelCase		:							int             =   shift_tokens_right(lowerCamelCase__						,	1			)
					_UpperCAmelCase		:							Union[str, Any]             =   np.equal(lowerCamelCase__						,	1			).astype(np.floataa			).sum()
					_UpperCAmelCase		:							str             =   np.equal(lowerCamelCase__						,	1			).astype(np.floataa			).sum()
					self.assertEqual(shifted.shape						,	input_ids.shape			)
					self.assertEqual(lowerCamelCase__						,	n_pad_before - 1			)
					self.assertTrue(np.equal(shifted[:, 0]						,	2			).all()			)






@require_flax
class 						lowerCAmelCase__      (      UpperCAmelCase__     ,							unittest.TestCase     ,							UpperCAmelCase__						):
			lowerCAmelCase			:						Optional[Any]							 =  True
			lowerCAmelCase			:						Optional[Any]							 =  (
			    (
			        FlaxMBartModel,
			        FlaxMBartForConditionalGeneration,
			        FlaxMBartForSequenceClassification,
			        FlaxMBartForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)
			lowerCAmelCase			:						List[str]							 =  (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




			def 				lowerCAmelCase__     (		self	:  int			) ->int:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   FlaxMBartModelTester(self			)




			def 				lowerCAmelCase__     (		self	:  Optional[Any]			) ->str:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							Any             =   self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward(lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__			)




			def 				lowerCAmelCase__     (		self	:  Any			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							Tuple             =   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(lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__			)




			def 				lowerCAmelCase__     (		self	:  Dict			) ->Any:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							int             =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__			):
									_UpperCAmelCase		:							str             =   self._prepare_for_class(lowerCamelCase__						,	lowerCamelCase__			)
									_UpperCAmelCase		:							int             =   model_class(lowerCamelCase__			)

									@jax.jit
									def encode_jitted(lowerCamelCase__	:  List[Any]						,	lowerCamelCase__	:  int=None						,	**lowerCamelCase__	:  int			):
											return model.encode(input_ids=lowerCamelCase__						,	attention_mask=lowerCamelCase__			)

									with self.subTest("JIT Enabled"			):
											_UpperCAmelCase		:							List[Any]             =   encode_jitted(**lowerCamelCase__			).to_tuple()

									with self.subTest("JIT Disabled"			):
											with jax.disable_jit():
													_UpperCAmelCase		:							int             =   encode_jitted(**lowerCamelCase__			).to_tuple()

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




			def 				lowerCAmelCase__     (		self	:  Dict			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							List[Any]             =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__			):
									_UpperCAmelCase		:							Union[str, Any]             =   model_class(lowerCamelCase__			)
									_UpperCAmelCase		:							Dict             =   model.encode(inputs_dict["input_ids"]						,	inputs_dict["attention_mask"]			)

									_UpperCAmelCase		:							Any             =   {
									    "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(lowerCamelCase__	:  int						,	lowerCamelCase__	:  Optional[Any]						,	lowerCamelCase__	:  Optional[Any]			):
											return model.decode(
											    decoder_input_ids=lowerCamelCase__						,	decoder_attention_mask=lowerCamelCase__						,	encoder_outputs=lowerCamelCase__						,	)

									with self.subTest("JIT Enabled"			):
											_UpperCAmelCase		:							Tuple             =   decode_jitted(**lowerCamelCase__			).to_tuple()

									with self.subTest("JIT Disabled"			):
											with jax.disable_jit():
													_UpperCAmelCase		:							Union[str, Any]             =   decode_jitted(**lowerCamelCase__			).to_tuple()

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






			@slow
			def 				lowerCAmelCase__     (		self	:  Any			) ->str:



					'''simple docstring'''







					for model_class_name in self.all_model_classes:
							_UpperCAmelCase		:							Dict             =   model_class_name.from_pretrained("facebook/mbart-large-cc25"						,	from_pt=lowerCamelCase__			)
							# FlaxMBartForSequenceClassification expects eos token in input_ids
							_UpperCAmelCase		:							List[str]             =   np.ones((1, 1)			) * model.config.eos_token_id
							_UpperCAmelCase		:							Tuple             =   model(lowerCamelCase__			)
							self.assertIsNotNone(lowerCamelCase__			)






@require_flax
@require_sentencepiece
@require_tokenizers
class 						lowerCAmelCase__      (      unittest.TestCase						):
			lowerCAmelCase			:						Any							 =  [
			    " UN Chief Says There Is No Military Solution in Syria",
			]
			lowerCAmelCase			:						Dict							 =  [
			    "Şeful ONU declară că nu există o soluţie militară în Siria",
			]
			lowerCAmelCase			:						Optional[int]							 =  "facebook/mbart-large-en-ro"




			@cached_property
			def 				lowerCAmelCase__     (		self	:  Any			) ->Optional[Any]:



					'''simple docstring'''







					return AutoTokenizer.from_pretrained(self.model_name			)




			@cached_property
			def 				lowerCAmelCase__     (		self	:  Optional[int]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   FlaxMBartForConditionalGeneration.from_pretrained(self.model_name						,	from_pt=lowerCamelCase__			)
					return model




			def 				lowerCAmelCase__     (		self	:  Optional[int]						,	**lowerCamelCase__	:  Dict			) ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Tuple             =   self.translate_src_text(**lowerCamelCase__			)
					self.assertListEqual(self.expected_text						,	lowerCamelCase__			)




			def 				lowerCAmelCase__     (		self	:  List[Any]						,	**lowerCamelCase__	:  Tuple			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							Union[str, Any]             =   self.tokenizer(self.src_text						,	**lowerCamelCase__						,	return_tensors="np"			)
					_UpperCAmelCase		:							str             =   self.model.generate(
					    model_inputs.input_ids						,	attention_mask=model_inputs.attention_mask						,	decoder_start_token_id=self.tokenizer.lang_code_to_id["ro_RO"]						,	early_stopping=lowerCamelCase__						,	num_beams=2						,	).sequences
					_UpperCAmelCase		:							Optional[Any]             =   self.tokenizer.batch_decode(lowerCamelCase__						,	skip_special_tokens=lowerCamelCase__			)
					return generated_words






			@slow
			def 				lowerCAmelCase__     (		self	:  Tuple			) ->Tuple:



					'''simple docstring'''







					self._assert_generated_batch_equal_expected()







'''simple docstring'''


import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json






def 			__lowerCAmelCase			(__lowerCAmelCase ):
		_UpperCAmelCase		:							Dict             =   filter(lambda __lowerCAmelCase : p.requires_grad   ,			model.parameters() )
		_UpperCAmelCase		:							List[Any]             =   sum([np.prod(p.size() ) for p in model_parameters] )
		return params


lowerCamelCase__     						=  logging.getLogger(__name__)






class 						lowerCAmelCase__      (      pl.Callback						):




			def 				lowerCAmelCase__     (		self	:  Optional[Any]						,	lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Union[str, Any]			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   {F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
					pl_module.logger.log_metrics(lowerCamelCase__			)




			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  Dict						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  pl.LightningModule						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  Any=True			) ->None:



					'''simple docstring'''







					logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""			)
					_UpperCAmelCase		:							Tuple             =   trainer.callback_metrics
					trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}			)
					# Log results
					_UpperCAmelCase		:							Optional[int]             =   Path(pl_module.hparams.output_dir			)
					if type_path == "test":
							_UpperCAmelCase		:							Optional[Any]             =   od / "test_results.txt"
							_UpperCAmelCase		:							str             =   od / "test_generations.txt"
					else:
							# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
							# If people want this it will be easy enough to add back.
							_UpperCAmelCase		:							Dict             =   od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
							_UpperCAmelCase		:							List[str]             =   od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
							results_file.parent.mkdir(exist_ok=lowerCamelCase__			)
							generations_file.parent.mkdir(exist_ok=lowerCamelCase__			)
					with open(lowerCamelCase__						,	"a+"			) as writer:
							for key in sorted(lowerCamelCase__			):
									if key in ["log", "progress_bar", "preds"]:
											continue
									_UpperCAmelCase		:							Union[str, Any]             =   metrics[key]
									if isinstance(lowerCamelCase__						,	torch.Tensor			):
											_UpperCAmelCase		:							int             =   val.item()
									_UpperCAmelCase		:							List[str]             =   F"""{key}: {val:.6f}\n"""
									writer.write(lowerCamelCase__			)

					if not save_generations:
							return

					if "preds" in metrics:
							_UpperCAmelCase		:							Dict             =   "\n".join(metrics["preds"]			)
							generations_file.open("w+"			).write(lowerCamelCase__			)




			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  List[Any]						,	lowerCamelCase__	:  Dict						,	lowerCamelCase__	:  int			) ->Optional[int]:



					'''simple docstring'''







					try:
							_UpperCAmelCase		:							int             =   pl_module.model.model.num_parameters()
					except AttributeError:
							_UpperCAmelCase		:							Dict             =   pl_module.model.num_parameters()

					_UpperCAmelCase		:							Dict             =   count_trainable_parameters(lowerCamelCase__			)
					# mp stands for million parameters
					trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1E6, "grad_mp": n_trainable_pars / 1E6}			)




			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  List[str]						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  pl.LightningModule			) ->int:



					'''simple docstring'''







					save_json(pl_module.metrics						,	pl_module.metrics_save_path			)
					return self._write_logs(lowerCamelCase__						,	lowerCamelCase__						,	"test"			)






			@rank_zero_only
			def 				lowerCAmelCase__     (		self	:  List[str]						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  Tuple			) ->Dict:



					'''simple docstring'''







					save_json(pl_module.metrics						,	pl_module.metrics_save_path			)
					# Uncommenting this will save val generations
					# return self._write_logs(trainer, pl_module, "valid")






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase=1   ,			__lowerCAmelCase=False ):
		if metric == "rouge2":
				_UpperCAmelCase		:							Tuple             =   "{val_avg_rouge2:.4f}-{step_count}"
		elif metric == "bleu":
				_UpperCAmelCase		:							List[str]             =   "{val_avg_bleu:.4f}-{step_count}"
		elif metric == "loss":
				_UpperCAmelCase		:							Union[str, Any]             =   "{val_avg_loss:.4f}-{step_count}"
		else:
				raise NotImplementedError(
				    F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
				    " this function." )

		_UpperCAmelCase		:							Optional[int]             =   ModelCheckpoint(
		    dirpath=__lowerCAmelCase   ,			filename=__lowerCAmelCase   ,			monitor=F"""val_{metric}"""   ,			mode="min" if "loss" in metric else "max"   ,			save_top_k=__lowerCAmelCase   ,			)
		return checkpoint_callback






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		return EarlyStopping(
		    monitor=F"""val_{metric}"""   ,			mode="min" if "loss" in metric else "max"   ,			patience=__lowerCAmelCase   ,			verbose=__lowerCAmelCase   ,			)



