

'''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     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase=None ,)     ->     Any:
	if attention_mask is None:
		UpperCAmelCase_		:   Optional[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_		:   Optional[Any]													=       np.ones((config.encoder_layers, config.encoder_attention_heads)	)
	if decoder_head_mask is None:
		UpperCAmelCase_		:   Dict													=       np.ones((config.decoder_layers, config.decoder_attention_heads)	)
	if cross_attn_head_mask is None:
		UpperCAmelCase_		:   Tuple													=       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			):


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


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

								UpperCAmelCase_		:   Optional[Any]													=       shift_tokens_right(_snake_case     ,      1)

								UpperCAmelCase_		:   Union[str, Any]													=       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_		:   List[Any]													=       prepare_mbart_inputs_dict(_snake_case     ,      _snake_case     ,      _snake_case)
								return config, inputs_dict


							def     _snake_case				(      self)				->					Any:
								UpperCAmelCase_						,     UpperCAmelCase_		:   List[Any]													=       self.prepare_config_and_inputs()
								return config, inputs_dict


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case)				->					Any:
								UpperCAmelCase_		:   str													=       20
								UpperCAmelCase_		:   str													=       model_class_name(_snake_case)

								UpperCAmelCase_		:   int													=       model.encode(inputs_dict['input_ids'])

								UpperCAmelCase_						,     UpperCAmelCase_		:   Union[str, Any]													=       (
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

								UpperCAmelCase_		:   Optional[int]													=       model.init_cache(decoder_input_ids.shape[0]     ,      _snake_case     ,      _snake_case)
								UpperCAmelCase_		:   str													=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,      dtype='i4')

								UpperCAmelCase_		:   List[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_		:   int													=       model.decode(
								    decoder_input_ids[:, :-1]     ,      _snake_case     ,      decoder_attention_mask=_snake_case     ,      past_key_values=_snake_case     ,      decoder_position_ids=_snake_case     ,      )

								UpperCAmelCase_		:   List[str]													=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,      dtype='i4')
								UpperCAmelCase_		:   str													=       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_		:   str													=       model.decode(_snake_case     ,      _snake_case)

								UpperCAmelCase_		:   Tuple													=       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)				->					Dict:
								UpperCAmelCase_		:   int													=       20
								UpperCAmelCase_		:   str													=       model_class_name(_snake_case)

								UpperCAmelCase_		:   List[Any]													=       model.encode(inputs_dict['input_ids'])

								UpperCAmelCase_						,     UpperCAmelCase_		:   Union[str, Any]													=       (
								    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_		:   Union[str, Any]													=       model.init_cache(decoder_input_ids.shape[0]     ,      _snake_case     ,      _snake_case)
								UpperCAmelCase_		:   Tuple													=       jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]     ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,      )

								UpperCAmelCase_		:   Any													=       model.decode(
								    decoder_input_ids[:, :-1]     ,      _snake_case     ,      decoder_attention_mask=_snake_case     ,      past_key_values=_snake_case     ,      decoder_position_ids=_snake_case     ,      )
								UpperCAmelCase_		:   Any													=       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,      dtype='i4')
								UpperCAmelCase_		:   Union[str, Any]													=       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_		:   Union[str, Any]													=       model.decode(_snake_case     ,      _snake_case     ,      decoder_attention_mask=_snake_case)

								UpperCAmelCase_		:   List[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}""")



@require_flax
class        lowercase    (	unittest.TestCase			):
							_lowerCamelCase		:   Union[str, Any]= 99


							def     _snake_case				(      self)				->					List[str]:
								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_		:   List[str]													=       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)				->					Any:
								UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_		:   Optional[int]													=       self._get_config_and_data()
								UpperCAmelCase_		:   int													=       FlaxMBartForSequenceClassification(_snake_case)
								UpperCAmelCase_		:   List[str]													=       model(input_ids=_snake_case     ,      decoder_input_ids=_snake_case)
								UpperCAmelCase_		:   Tuple													=       (batch_size, config.num_labels)
								self.assertEqual(outputs['logits'].shape     ,      _snake_case)


							def     _snake_case				(      self)				->					Union[str, Any]:
								UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_		:   List[str]													=       self._get_config_and_data()
								UpperCAmelCase_		:   int													=       FlaxMBartForQuestionAnswering(_snake_case)
								UpperCAmelCase_		:   Dict													=       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)				->					List[str]:
								UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_		:   Tuple													=       self._get_config_and_data()
								UpperCAmelCase_		:   Tuple													=       FlaxMBartForConditionalGeneration(_snake_case)
								UpperCAmelCase_		:   Tuple													=       lm_model(input_ids=_snake_case)
								UpperCAmelCase_		:   Dict													=       (batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['logits'].shape     ,      _snake_case)


							def     _snake_case				(      self)				->					List[str]:
								UpperCAmelCase_		:   int													=       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_		:   List[str]													=       FlaxMBartForConditionalGeneration(_snake_case)
								UpperCAmelCase_		:   Tuple													=       np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,      dtype=np.intaa)
								UpperCAmelCase_		:   Optional[Any]													=       np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,      dtype=np.intaa)
								UpperCAmelCase_		:   List[str]													=       lm_model(input_ids=_snake_case     ,      decoder_input_ids=_snake_case)
								UpperCAmelCase_		:   Any													=       (*summary.shape, config.vocab_size)
								self.assertEqual(outputs['logits'].shape     ,      _snake_case)





							def     _snake_case				(      self)				->					Any:
								UpperCAmelCase_		:   Dict													=       np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,      dtype=np.intaa)
								UpperCAmelCase_		:   List[Any]													=       shift_tokens_right(_snake_case     ,      1)
								UpperCAmelCase_		:   List[str]													=       np.equal(_snake_case     ,      1).astype(np.floataa).sum()
								UpperCAmelCase_		:   Optional[Any]													=       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        lowercase    (	a_,       unittest.TestCase,       a_			):
							_lowerCamelCase		:   Union[str, Any]= True
							_lowerCamelCase		:   int= (
							    (
							        FlaxMBartModel,
							        FlaxMBartForConditionalGeneration,
							        FlaxMBartForSequenceClassification,
							        FlaxMBartForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)
							_lowerCamelCase		:   Tuple= (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()


							def     _snake_case				(      self)				->					Tuple:
								UpperCAmelCase_		:   str													=       FlaxMBartModelTester(self)


							def     _snake_case				(      self)				->					int:
								UpperCAmelCase_						,     UpperCAmelCase_		:   Dict													=       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)				->					Optional[Any]:
								UpperCAmelCase_						,     UpperCAmelCase_		:   Union[str, Any]													=       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)				->					Any:
								UpperCAmelCase_						,     UpperCAmelCase_		:   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_		:   Optional[int]													=       self._prepare_for_class(_snake_case     ,      _snake_case)
										UpperCAmelCase_		:   Optional[Any]													=       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_		:   List[str]													=       encode_jitted(**_snake_case).to_tuple()

										with self.subTest('JIT Disabled'):
											with jax.disable_jit():
												UpperCAmelCase_		:   Any													=       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)				->					Optional[int]:
								UpperCAmelCase_						,     UpperCAmelCase_		:   Tuple													=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
										UpperCAmelCase_		:   int													=       model_class(_snake_case)
										UpperCAmelCase_		:   str													=       model.encode(inputs_dict['input_ids']     ,      inputs_dict['attention_mask'])

										UpperCAmelCase_		:   Dict													=       {
										    '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_		:   int													=       decode_jitted(**_snake_case).to_tuple()

										with self.subTest('JIT Disabled'):
											with jax.disable_jit():
												UpperCAmelCase_		:   List[Any]													=       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)				->					Any:
								for model_class_name in self.all_model_classes:
									UpperCAmelCase_		:   Optional[Any]													=       model_class_name.from_pretrained('facebook/mbart-large-cc25'     ,      from_pt=_snake_case)
									# FlaxMBartForSequenceClassification expects eos token in input_ids
									UpperCAmelCase_		:   str													=       np.ones((1, 1)) * model.config.eos_token_id
									UpperCAmelCase_		:   Tuple													=       model(_snake_case)
									self.assertIsNotNone(_snake_case)



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


							@cached_property
							def     _snake_case				(      self)				->					Union[str, Any]:
								return AutoTokenizer.from_pretrained(self.model_name)


							@cached_property
							def     _snake_case				(      self)				->					Optional[Any]:
								UpperCAmelCase_		:   List[Any]													=       FlaxMBartForConditionalGeneration.from_pretrained(self.model_name     ,      from_pt=_snake_case)
								return model


							def     _snake_case				(      self     ,      **_snake_case)				->					List[str]:
								UpperCAmelCase_		:   List[Any]													=       self.translate_src_text(**_snake_case)
								self.assertListEqual(self.expected_text     ,      _snake_case)


							def     _snake_case				(      self     ,      **_snake_case)				->					Any:
								UpperCAmelCase_		:   str													=       self.tokenizer(self.src_text     ,      **_snake_case     ,      return_tensors='np')
								UpperCAmelCase_		:   Optional[Any]													=       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_		:   Optional[int]													=       self.tokenizer.batch_decode(_snake_case     ,      skip_special_tokens=_snake_case)
								return generated_words





							@slow
							def     _snake_case				(      self)				->					Tuple:
								self._assert_generated_batch_equal_expected()









'''simple docstring'''


import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
						import torch



class        lowercase    (	unittest.TestCase,       a_			):


							def     _snake_case				(      self)				->					Optional[int]:
								UpperCAmelCase_		:   int													=       load_tool('speech-to-text')
								self.tool.setup()


							def     _snake_case				(      self)				->					Any:
								UpperCAmelCase_		:   str													=       self.tool(torch.ones(3000))
								self.assertEqual(_snake_case     ,      ' you')





							def     _snake_case				(      self)				->					str:
								UpperCAmelCase_		:   List[Any]													=       self.tool(audio=torch.ones(3000))
								self.assertEqual(_snake_case     ,      ' you')






