


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
							snake_case			:      str 							=   '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 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,):

				"""simple docstring"""






				if attention_mask is None:
								_SCREAMING_SNAKE_CASE      = np.where(input_ids != config.pad_token_id   ,1   ,0							)
				if decoder_attention_mask is None:
								_SCREAMING_SNAKE_CASE      = np.where(decoder_input_ids != config.pad_token_id   ,1   ,0							)
				if head_mask is None:
								_SCREAMING_SNAKE_CASE      = np.ones((config.encoder_layers, config.encoder_attention_heads)							)
				if decoder_head_mask is None:
								_SCREAMING_SNAKE_CASE      = np.ones((config.decoder_layers, config.decoder_attention_heads)							)
				if cross_attn_head_mask is None:
								_SCREAMING_SNAKE_CASE      = 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    __lowercase  (       unittest.TestCase							):

							"""simple docstring"""


							def __init__(    self				, A_				, A_=13				, A_=7				, A_=True				, A_=False				, A_=99				, A_=16				, A_=2				, A_=4				, A_=4				, A_="gelu"				, A_=0.1				, A_=0.1				, A_=32				, A_=2				, A_=1				, A_=0				, A_=2				, A_=0.02				, )->	Any:
											_SCREAMING_SNAKE_CASE      = parent
											_SCREAMING_SNAKE_CASE      = batch_size
											_SCREAMING_SNAKE_CASE      = seq_length
											_SCREAMING_SNAKE_CASE      = is_training
											_SCREAMING_SNAKE_CASE      = use_labels
											_SCREAMING_SNAKE_CASE      = vocab_size
											_SCREAMING_SNAKE_CASE      = hidden_size
											_SCREAMING_SNAKE_CASE      = num_hidden_layers
											_SCREAMING_SNAKE_CASE      = num_attention_heads
											_SCREAMING_SNAKE_CASE      = intermediate_size
											_SCREAMING_SNAKE_CASE      = hidden_act
											_SCREAMING_SNAKE_CASE      = hidden_dropout_prob
											_SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE      = max_position_embeddings
											_SCREAMING_SNAKE_CASE      = eos_token_id
											_SCREAMING_SNAKE_CASE      = pad_token_id
											_SCREAMING_SNAKE_CASE      = bos_token_id
											_SCREAMING_SNAKE_CASE      = decoder_start_token_id
											_SCREAMING_SNAKE_CASE      = initializer_range





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

											_SCREAMING_SNAKE_CASE      = shift_tokens_right(A_				, 1	)

											_SCREAMING_SNAKE_CASE      = 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=A_				, )
											_SCREAMING_SNAKE_CASE      = prepare_mbart_inputs_dict(A_				, A_				, A_	)
											return config, inputs_dict





							def 		__magic_name__			(    self	)->	Tuple:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.prepare_config_and_inputs()
											return config, inputs_dict





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	int:
											_SCREAMING_SNAKE_CASE      = 20
											_SCREAMING_SNAKE_CASE      = model_class_name(A_	)

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

											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = (
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

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

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

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

											_SCREAMING_SNAKE_CASE      = model.decode(A_				, A_	)

											_SCREAMING_SNAKE_CASE      = 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 		__magic_name__			(    self				, A_				, A_				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = 20
											_SCREAMING_SNAKE_CASE      = model_class_name(A_	)

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

											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = (
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

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

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

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

											_SCREAMING_SNAKE_CASE      = model.decode(A_				, A_				, decoder_attention_mask=A_	)

											_SCREAMING_SNAKE_CASE      = 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    __lowercase  (       unittest.TestCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Tuple        =						99





							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = 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				, )

											_SCREAMING_SNAKE_CASE      = input_ids.shape[0]
											_SCREAMING_SNAKE_CASE      = 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 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self._get_config_and_data()
											_SCREAMING_SNAKE_CASE      = FlaxMBartForSequenceClassification(A_	)
											_SCREAMING_SNAKE_CASE      = model(input_ids=A_				, decoder_input_ids=A_	)
											_SCREAMING_SNAKE_CASE      = (batch_size, config.num_labels)
											self.assertEqual(outputs['logits'].shape				, A_	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self._get_config_and_data()
											_SCREAMING_SNAKE_CASE      = FlaxMBartForQuestionAnswering(A_	)
											_SCREAMING_SNAKE_CASE      = model(input_ids=A_	)

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





							def 		__magic_name__			(    self	)->	List[str]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self._get_config_and_data()
											_SCREAMING_SNAKE_CASE      = FlaxMBartForConditionalGeneration(A_	)
											_SCREAMING_SNAKE_CASE      = lm_model(input_ids=A_	)
											_SCREAMING_SNAKE_CASE      = (batch_size, input_ids.shape[1], config.vocab_size)
											self.assertEqual(outputs['logits'].shape				, A_	)





							def 		__magic_name__			(    self	)->	int:
											_SCREAMING_SNAKE_CASE      = 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				, )
											_SCREAMING_SNAKE_CASE      = FlaxMBartForConditionalGeneration(A_	)
											_SCREAMING_SNAKE_CASE      = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				, dtype=np.intaa	)
											_SCREAMING_SNAKE_CASE      = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				, dtype=np.intaa	)
											_SCREAMING_SNAKE_CASE      = lm_model(input_ids=A_				, decoder_input_ids=A_	)
											_SCREAMING_SNAKE_CASE      = (*summary.shape, config.vocab_size)
											self.assertEqual(outputs['logits'].shape				, A_	)





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				, dtype=np.intaa	)
											_SCREAMING_SNAKE_CASE      = shift_tokens_right(A_				, 1	)
											_SCREAMING_SNAKE_CASE      = np.equal(A_				, 1	).astype(np.floataa	).sum()
											_SCREAMING_SNAKE_CASE      = np.equal(A_				, 1	).astype(np.floataa	).sum()
											self.assertEqual(shifted.shape				, input_ids.shape	)
											self.assertEqual(A_				, n_pad_before - 1	)
											self.assertTrue(np.equal(shifted[:, 0]				, 2	).all()	)







@require_flax
class    __lowercase  (       UpperCamelCase      ,     unittest.TestCase      ,     UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						True
							SCREAMING_SNAKE_CASE   :					int        =						(
							    (
							        FlaxMBartModel,
							        FlaxMBartForConditionalGeneration,
							        FlaxMBartForSequenceClassification,
							        FlaxMBartForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)
							SCREAMING_SNAKE_CASE   :					int        =						(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()





							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = FlaxMBartModelTester(self	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward(A_				, A_				, A_	)





							def 		__magic_name__			(    self	)->	List[str]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = 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(A_				, A_				, A_	)





							def 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__	):
																			_SCREAMING_SNAKE_CASE      = self._prepare_for_class(A_				, A_	)
																			_SCREAMING_SNAKE_CASE      = model_class(A_	)

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

																			with self.subTest('JIT Enabled'	):
																							_SCREAMING_SNAKE_CASE      = encode_jitted(**A_	).to_tuple()

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

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





							def 		__magic_name__			(    self	)->	Any:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

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

																			_SCREAMING_SNAKE_CASE      = {
																			    '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(A_				, A_				, A_	):
																							return model.decode(
																							    decoder_input_ids=A_				, decoder_attention_mask=A_				, encoder_outputs=A_				, )

																			with self.subTest('JIT Enabled'	):
																							_SCREAMING_SNAKE_CASE      = decode_jitted(**A_	).to_tuple()

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

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





							@slow
							def 		__magic_name__			(    self	)->	int:
											for model_class_name in self.all_model_classes:
															_SCREAMING_SNAKE_CASE      = model_class_name.from_pretrained('facebook/mbart-large-cc25'				, from_pt=A_	)
															# FlaxMBartForSequenceClassification expects eos token in input_ids
															_SCREAMING_SNAKE_CASE      = np.ones((1, 1)	) * model.config.eos_token_id
															_SCREAMING_SNAKE_CASE      = model(A_	)
															self.assertIsNotNone(A_	)







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

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					List[Any]        =						[
							    " UN Chief Says There Is No Military Solution in Syria",
							]
							SCREAMING_SNAKE_CASE   :					Tuple        =						[
							    "Şeful ONU declară că nu există o soluţie militară în Siria",
							]
							SCREAMING_SNAKE_CASE   :					List[str]        =						"facebook/mbart-large-en-ro"





							@cached_property
							def 		__magic_name__			(    self	)->	str:
											return AutoTokenizer.from_pretrained(self.model_name	)





							@cached_property
							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = FlaxMBartForConditionalGeneration.from_pretrained(self.model_name				, from_pt=A_	)
											return model





							def 		__magic_name__			(    self				, **A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = self.translate_src_text(**A_	)
											self.assertListEqual(self.expected_text				, A_	)





							def 		__magic_name__			(    self				, **A_	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = self.tokenizer(self.src_text				, **A_				, return_tensors='np'	)
											_SCREAMING_SNAKE_CASE      = 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=A_				, num_beams=2				, ).sequences
											_SCREAMING_SNAKE_CASE      = self.tokenizer.batch_decode(A_				, skip_special_tokens=A_	)
											return generated_words





							@slow
							def 		__magic_name__			(    self	)->	Optional[int]:
											self._assert_generated_batch_equal_expected()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case			:      Optional[int] 							=   {'configuration_vit_hybrid': ['VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTHybridConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case			:      int 							=   [
							    'VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ViTHybridForImageClassification',
							    'ViTHybridModel',
							    'ViTHybridPreTrainedModel',
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case			:      Any 							=   ['ViTHybridImageProcessor']


if TYPE_CHECKING:
							from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_hybrid import (
														    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTHybridForImageClassification,
														    ViTHybridModel,
														    ViTHybridPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
							import sys

							snake_case			:      Optional[int] 							=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
