

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
				_a:      int								=     """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 (  A    ,       A    ,       A=None    ,       A=None    ,       A=None    ,       A=None    ,       A=None    ,       A=None    ,       ):
	if attention_mask is None:
		UpperCAmelCase_    			=     np.where(input_ids != config.pad_token_id    ,       1    ,       0      )
	if decoder_attention_mask is None:
		UpperCAmelCase_    			=     np.where(decoder_input_ids != config.pad_token_id    ,       1    ,       0      )
	if head_mask is None:
		UpperCAmelCase_    			=     np.ones((config.encoder_layers, config.encoder_attention_heads)      )
	if decoder_head_mask is None:
		UpperCAmelCase_    			=     np.ones((config.decoder_layers, config.decoder_attention_heads)      )
	if cross_attn_head_mask is None:
		UpperCAmelCase_    			=     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   __UpperCamelCase      (   unittest.TestCase  ):




	def __init__(						self		:  Union[str, Any]						,		lowerCAmelCase		:  Any						,		lowerCAmelCase		:  Optional[int]=13						,		lowerCAmelCase		:  Dict=7						,		lowerCAmelCase		:  Any=True						,		lowerCAmelCase		:  Optional[Any]=False						,		lowerCAmelCase		:  List[Any]=99						,		lowerCAmelCase		:  Tuple=16						,		lowerCAmelCase		:  List[Any]=2						,		lowerCAmelCase		:  List[Any]=4						,		lowerCAmelCase		:  Any=4						,		lowerCAmelCase		:  str="gelu"						,		lowerCAmelCase		:  List[Any]=0.1						,		lowerCAmelCase		:  int=0.1						,		lowerCAmelCase		:  Dict=32						,		lowerCAmelCase		:  str=2						,		lowerCAmelCase		:  Tuple=1						,		lowerCAmelCase		:  str=0						,		lowerCAmelCase		:  List[Any]=2						,		lowerCAmelCase		:  str=0.02						,		):


		'''simple docstring'''







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




	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







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

		UpperCAmelCase_    			=     shift_tokens_right(lowerCAmelCase						,		1							)

		UpperCAmelCase_    			=     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_    			=     prepare_mbart_inputs_dict(lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase							)
		return config, inputs_dict




	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_    			=     self.prepare_config_and_inputs()
		return config, inputs_dict




	def  __A   (						self		:  Optional[Any]						,		lowerCAmelCase		:  List[Any]						,		lowerCAmelCase		:  List[str]						,		lowerCAmelCase		:  str							):


		'''simple docstring'''







		UpperCAmelCase_    			=     20
		UpperCAmelCase_    			=     model_class_name(lowerCAmelCase							)

		UpperCAmelCase_    			=     model.encode(inputs_dict["input_ids"]							)

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

		UpperCAmelCase_    			=     model.init_cache(decoder_input_ids.shape[0]						,		lowerCAmelCase						,		lowerCAmelCase							)
		UpperCAmelCase_    			=     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,		dtype="i4"							)

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

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

		UpperCAmelCase_    			=     model.decode(lowerCAmelCase						,		lowerCAmelCase							)

		UpperCAmelCase_    			=     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  __A   (						self		:  int						,		lowerCAmelCase		:  Optional[Any]						,		lowerCAmelCase		:  Union[str, Any]						,		lowerCAmelCase		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     20
		UpperCAmelCase_    			=     model_class_name(lowerCAmelCase							)

		UpperCAmelCase_    			=     model.encode(inputs_dict["input_ids"]							)

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

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

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

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

		UpperCAmelCase_    			=     model.decode(lowerCAmelCase						,		lowerCAmelCase						,		decoder_attention_mask=lowerCAmelCase							)

		UpperCAmelCase_    			=     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   __UpperCamelCase      (   unittest.TestCase  ):
	SCREAMING_SNAKE_CASE__      			=       99




	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     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_    			=     input_ids.shape[0]
		UpperCAmelCase_    			=     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  __A   (						self		:  str							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_    			=     self._get_config_and_data()
		UpperCAmelCase_    			=     FlaxMBartForSequenceClassification(lowerCAmelCase							)
		UpperCAmelCase_    			=     model(input_ids=lowerCAmelCase						,		decoder_input_ids=lowerCAmelCase							)
		UpperCAmelCase_    			=     (batch_size, config.num_labels)
		self.assertEqual(outputs["logits"].shape						,		lowerCAmelCase							)




	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_    			=     self._get_config_and_data()
		UpperCAmelCase_    			=     FlaxMBartForQuestionAnswering(lowerCAmelCase							)
		UpperCAmelCase_    			=     model(input_ids=lowerCAmelCase							)

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




	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_    			=     self._get_config_and_data()
		UpperCAmelCase_    			=     FlaxMBartForConditionalGeneration(lowerCAmelCase							)
		UpperCAmelCase_    			=     lm_model(input_ids=lowerCAmelCase							)
		UpperCAmelCase_    			=     (batch_size, input_ids.shape[1], config.vocab_size)
		self.assertEqual(outputs["logits"].shape						,		lowerCAmelCase							)




	def  __A   (						self		:  Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     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_    			=     FlaxMBartForConditionalGeneration(lowerCAmelCase							)
		UpperCAmelCase_    			=     np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						,		dtype=np.intaa							)
		UpperCAmelCase_    			=     np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						,		dtype=np.intaa							)
		UpperCAmelCase_    			=     lm_model(input_ids=lowerCAmelCase						,		decoder_input_ids=lowerCAmelCase							)
		UpperCAmelCase_    			=     (*summary.shape, config.vocab_size)
		self.assertEqual(outputs["logits"].shape						,		lowerCAmelCase							)




	def  __A   (						self		:  Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						,		dtype=np.intaa							)
		UpperCAmelCase_    			=     shift_tokens_right(lowerCAmelCase						,		1							)
		UpperCAmelCase_    			=     np.equal(lowerCAmelCase						,		1							).astype(np.floataa							).sum()
		UpperCAmelCase_    			=     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   __UpperCamelCase      (   lowercase       ,       unittest.TestCase       ,       lowercase  ):
	SCREAMING_SNAKE_CASE__      			=       True
	SCREAMING_SNAKE_CASE__      			=       (
	    (
	        FlaxMBartModel,
	        FlaxMBartForConditionalGeneration,
	        FlaxMBartForSequenceClassification,
	        FlaxMBartForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE__      			=       (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




	def  __A   (						self		:  Dict							):


		'''simple docstring'''







		UpperCAmelCase_    			=     FlaxMBartModelTester(self							)




	def  __A   (						self		:  Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_    			=     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  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_    			=     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  __A   (						self		:  List[str]							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_    			=     self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__							):
				UpperCAmelCase_    			=     self._prepare_for_class(lowerCAmelCase						,		lowerCAmelCase							)
				UpperCAmelCase_    			=     model_class(lowerCAmelCase							)

				@jax.jit
				def encode_jitted(lowerCAmelCase		:  Optional[Any]						,		lowerCAmelCase		:  Union[str, Any]=None						,		**lowerCAmelCase		:  Optional[int]							):
					return model.encode(input_ids=lowerCAmelCase						,		attention_mask=lowerCAmelCase							)

				with self.subTest("JIT Enabled"							):
					UpperCAmelCase_    			=     encode_jitted(**lowerCAmelCase							).to_tuple()

				with self.subTest("JIT Disabled"							):
					with jax.disable_jit():
						UpperCAmelCase_    			=     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  __A   (						self		:  Tuple							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_    			=     self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__							):
				UpperCAmelCase_    			=     model_class(lowerCAmelCase							)
				UpperCAmelCase_    			=     model.encode(inputs_dict["input_ids"]						,		inputs_dict["attention_mask"]							)

				UpperCAmelCase_    			=     {
				    "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		:  Any						,		lowerCAmelCase		:  List[Any]						,		lowerCAmelCase		:  Optional[int]							):
					return model.decode(
					    decoder_input_ids=lowerCAmelCase						,		decoder_attention_mask=lowerCAmelCase						,		encoder_outputs=lowerCAmelCase						,		)

				with self.subTest("JIT Enabled"							):
					UpperCAmelCase_    			=     decode_jitted(**lowerCAmelCase							).to_tuple()

				with self.subTest("JIT Disabled"							):
					with jax.disable_jit():
						UpperCAmelCase_    			=     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  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		for model_class_name in self.all_model_classes:
			UpperCAmelCase_    			=     model_class_name.from_pretrained("facebook/mbart-large-cc25"						,		from_pt=lowerCAmelCase							)
			# FlaxMBartForSequenceClassification expects eos token in input_ids
			UpperCAmelCase_    			=     np.ones((1, 1)							) * model.config.eos_token_id
			UpperCAmelCase_    			=     model(lowerCAmelCase							)
			self.assertIsNotNone(lowerCAmelCase							)


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




	@cached_property
	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







		return AutoTokenizer.from_pretrained(self.model_name							)




	@cached_property
	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_    			=     FlaxMBartForConditionalGeneration.from_pretrained(self.model_name						,		from_pt=lowerCAmelCase							)
		return model




	def  __A   (						self		:  Union[str, Any]						,		**lowerCAmelCase		:  int							):


		'''simple docstring'''







		UpperCAmelCase_    			=     self.translate_src_text(**lowerCAmelCase							)
		self.assertListEqual(self.expected_text						,		lowerCAmelCase							)




	def  __A   (						self		:  List[str]						,		**lowerCAmelCase		:  Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     self.tokenizer(self.src_text						,		**lowerCAmelCase						,		return_tensors="np"							)
		UpperCAmelCase_    			=     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_    			=     self.tokenizer.batch_decode(lowerCAmelCase						,		skip_special_tokens=lowerCAmelCase							)
		return generated_words




	@slow
	def  __A   (						self		:  Union[str, Any]							):


		'''simple docstring'''







		self._assert_generated_batch_equal_expected()


def  __lowerCAmelCase (  A      ):
	UpperCAmelCase_    			=     len(A      )

	# Set the gap value to be decreased by a factor of 1.3
	# after each iteration
	UpperCAmelCase_    			=     1.3

	# Continue sorting until the gap is 1
	while gap > 1:
		# Decrease the gap value
		UpperCAmelCase_    			=     int(gap / shrink      )

		# Sort the elements using insertion sort
		for i in range(A    ,       len(A      )      ):
			UpperCAmelCase_    			=     collection[i]
			UpperCAmelCase_    			=     i
			while j >= gap and collection[j - gap] > temp:
				UpperCAmelCase_    			=     collection[j - gap]
				j -= gap
			UpperCAmelCase_    			=     temp

	return collection


if __name__ == "__main__":
				import doctest

				doctest.testmod()