


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
						__UpperCamelCase      						=    '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     UpperCamelCase_(			_A				:List[str]			,       _A				:str			,       _A				:Optional[int]=None			,       _A				:List[Any]=None			,       _A				:Optional[Any]=None			,       _A				:str=None			,       _A				:List[str]=None			,       _A				:Dict=None			,       )->						str:
			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     lowerCamelCase__		(							unittest.TestCase ):






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  snake_case=13 ,  snake_case=7 ,  snake_case=True ,  snake_case=False ,  snake_case=99 ,  snake_case=16 ,  snake_case=2 ,  snake_case=4 ,  snake_case=4 ,  snake_case="gelu" ,  snake_case=0.1 ,  snake_case=0.1 ,  snake_case=32 ,  snake_case=2 ,  snake_case=1 ,  snake_case=0 ,  snake_case=2 ,  snake_case=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 			snake_case__			(							self       ):

						'''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(snake_case ,  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=snake_case ,  )
						UpperCamelCase__   =    prepare_mbart_inputs_dict(snake_case ,  snake_case ,  snake_case       )
						return config, inputs_dict




			def 			snake_case__			(							self       ):

						'''simple docstring'''


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




			def 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    20
						UpperCamelCase__   =    model_class_name(snake_case       )

						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] ,  snake_case ,  snake_case       )
						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] ,  snake_case ,  decoder_attention_mask=snake_case ,  past_key_values=snake_case ,  decoder_position_ids=snake_case ,  )

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

						UpperCamelCase__   =    model.decode(snake_case ,  snake_case       )

						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 			snake_case__			(							self ,  snake_case ,  snake_case ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    20
						UpperCamelCase__   =    model_class_name(snake_case       )

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

						UpperCamelCase__   =    model.decode(snake_case ,  snake_case ,  decoder_attention_mask=snake_case       )

						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     lowerCamelCase__		(							unittest.TestCase ):






			"""simple docstring"""






			_UpperCamelCase						:   Union[str, Any]					=						99




			def 			snake_case__			(							self       ):

						'''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 			snake_case__			(							self       ):

						'''simple docstring'''


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




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__, UpperCamelCase__, UpperCamelCase__   =    self._get_config_and_data()
						UpperCamelCase__   =    FlaxMBartForQuestionAnswering(snake_case       )
						UpperCamelCase__   =    model(input_ids=snake_case       )

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




			def 			snake_case__			(							self       ):

						'''simple docstring'''


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




			def 			snake_case__			(							self       ):

						'''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(snake_case       )
						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=snake_case ,  decoder_input_ids=snake_case       )
						UpperCamelCase__   =    (*summary.shape, config.vocab_size)
						self.assertEqual(outputs["logits"].shape ,  snake_case       )







			def 			snake_case__			(							self       ):

						'''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(snake_case ,  1       )
						UpperCamelCase__   =    np.equal(snake_case ,  1       ).astype(np.floataa       ).sum()
						UpperCamelCase__   =    np.equal(snake_case ,  1       ).astype(np.floataa       ).sum()
						self.assertEqual(shifted.shape ,  input_ids.shape       )
						self.assertEqual(snake_case ,  n_pad_before - 1       )
						self.assertTrue(np.equal(shifted[:, 0] ,  2       ).all()       )







@require_flax
class     lowerCamelCase__		(							UpperCAmelCase   ,		unittest.TestCase   ,		UpperCAmelCase ):






			"""simple docstring"""






			_UpperCamelCase						:   Optional[int]					=						True
			_UpperCamelCase						:   int					=						(
			    (
			        FlaxMBartModel,
			        FlaxMBartForConditionalGeneration,
			        FlaxMBartForSequenceClassification,
			        FlaxMBartForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)
			_UpperCamelCase						:   List[str]					=						(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    FlaxMBartModelTester(self       )




			def 			snake_case__			(							self       ):

						'''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(snake_case ,  snake_case ,  snake_case       )




			def 			snake_case__			(							self       ):

						'''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(snake_case ,  snake_case ,  snake_case       )




			def 			snake_case__			(							self       ):

						'''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(snake_case ,  snake_case       )
												UpperCamelCase__   =    model_class(snake_case       )

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

												with self.subTest("JIT Enabled"       ):
															UpperCamelCase__   =    encode_jitted(**snake_case       ).to_tuple()

												with self.subTest("JIT Disabled"       ):
															with jax.disable_jit():
																		UpperCamelCase__   =    encode_jitted(**snake_case       ).to_tuple()

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




			def 			snake_case__			(							self       ):

						'''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(snake_case       )
												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(snake_case ,  snake_case ,  snake_case       ):
															return model.decode(
															    decoder_input_ids=snake_case ,  decoder_attention_mask=snake_case ,  encoder_outputs=snake_case ,  )

												with self.subTest("JIT Enabled"       ):
															UpperCamelCase__   =    decode_jitted(**snake_case       ).to_tuple()

												with self.subTest("JIT Disabled"       ):
															with jax.disable_jit():
																		UpperCamelCase__   =    decode_jitted(**snake_case       ).to_tuple()

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







			@slow
			def 			snake_case__			(							self       ):

						'''simple docstring'''


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







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






			"""simple docstring"""






			_UpperCamelCase						:   Union[str, Any]					=						[
			    ' UN Chief Says There Is No Military Solution in Syria',
			]
			_UpperCamelCase						:   Optional[Any]					=						[
			    'Şeful ONU declară că nu există o soluţie militară în Siria',
			]
			_UpperCamelCase						:   Optional[int]					=						'facebook/mbart-large-en-ro'




			@cached_property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return AutoTokenizer.from_pretrained(self.model_name       )




			@cached_property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						UpperCamelCase__   =    FlaxMBartForConditionalGeneration.from_pretrained(self.model_name ,  from_pt=snake_case       )
						return model




			def 			snake_case__			(							self ,  **snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.translate_src_text(**snake_case       )
						self.assertListEqual(self.expected_text ,  snake_case       )




			def 			snake_case__			(							self ,  **snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.tokenizer(self.src_text ,  **snake_case ,  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=snake_case ,  num_beams=2 ,  ).sequences
						UpperCamelCase__   =    self.tokenizer.batch_decode(snake_case ,  skip_special_tokens=snake_case       )
						return generated_words







			@slow
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						self._assert_generated_batch_equal_expected()






from __future__ import annotations
def     UpperCamelCase_(			_A				:list					)->						int | float:
			UpperCamelCase__   =    sorted(_A					)
			UpperCamelCase__   =    len(_A					)
			UpperCamelCase__   =    length >> 1
			return (
			    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
			    if length % 2 == 0
			    else sorted_list[mid_index]
			)
def     UpperCamelCase_(			)->						List[Any]:
			import doctest

			doctest.testmod()


if __name__ == "__main__":
						main()


