






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          = '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    __A    (   _lowercase    ,	_lowercase    ,	_lowercase=None    ,	_lowercase=None    ,	_lowercase=None    ,	_lowercase=None    ,	_lowercase=None    ,	_lowercase=None    ,	):

					'''simple docstring'''







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





							"""simple docstring"""

							def __init__(     self:      str						,   __A:      List[Any]						,   __A:      Dict=13						,   __A:      Tuple=7						,   __A:      Optional[int]=True						,   __A:      List[Any]=False						,   __A:      List[str]=99						,   __A:      Union[str, Any]=16						,   __A:      Any=2						,   __A:      Optional[Any]=4						,   __A:      Any=4						,   __A:      Dict="gelu"						,   __A:      Any=0.1						,   __A:      Union[str, Any]=0.1						,   __A:      str=32						,   __A:      int=2						,   __A:      Optional[Any]=1						,   __A:      str=0						,   __A:      int=2						,   __A:      Union[str, Any]=0.02						,   )			-> Optional[int]:
												_A									=       parent
												_A									=       batch_size
												_A									=       seq_length
												_A									=       is_training
												_A									=       use_labels
												_A									=       vocab_size
												_A									=       hidden_size
												_A									=       num_hidden_layers
												_A									=       num_attention_heads
												_A									=       intermediate_size
												_A									=       hidden_act
												_A									=       hidden_dropout_prob
												_A									=       attention_probs_dropout_prob
												_A									=       max_position_embeddings
												_A									=       eos_token_id
												_A									=       pad_token_id
												_A									=       bos_token_id
												_A									=       decoder_start_token_id
												_A									=       initializer_range

							def 					__A       (     self:      str      )			-> List[Any]:
												_A									=       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						,   self.vocab_size      )						,   3						,   self.vocab_size      )
												_A									=       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						,   dtype=np.intaa      ))						,   -1      )

												_A									=       shift_tokens_right(_lowercase						,   1      )

												_A									=       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=_lowercase						,   )
												_A									=       prepare_mbart_inputs_dict(_lowercase						,   _lowercase						,   _lowercase      )
												return config, inputs_dict

							def 					__A       (     self:      List[Any]      )			-> int:
												_A							,_A									=       self.prepare_config_and_inputs()
												return config, inputs_dict

							def 					__A       (     self:      int						,   __A:      str						,   __A:      int						,   __A:      List[Any]      )			-> Optional[int]:
												_A									=       20
												_A									=       model_class_name(_lowercase      )

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

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

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

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

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

												_A									=       model.decode(_lowercase						,   _lowercase      )

												_A									=       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:      Dict						,   __A:      str						,   __A:      List[Any]						,   __A:      Optional[Any]      )			-> int:
												_A									=       20
												_A									=       model_class_name(_lowercase      )

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

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

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

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

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

												_A									=       model.decode(_lowercase						,   _lowercase						,   decoder_attention_mask=_lowercase      )

												_A									=       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   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

							A_       =    99

							def 					__A       (     self:      Optional[Any]      )			-> int:
												_A									=       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						,   )

												_A									=       input_ids.shape[0]
												_A									=       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:      Any      )			-> str:
												_A							,_A							,_A									=       self._get_config_and_data()
												_A									=       FlaxMBartForSequenceClassification(_lowercase      )
												_A									=       model(input_ids=_lowercase						,   decoder_input_ids=_lowercase      )
												_A									=       (batch_size, config.num_labels)
												self.assertEqual(outputs['''logits'''].shape						,   _lowercase      )

							def 					__A       (     self:      Dict      )			-> int:
												_A							,_A							,_A									=       self._get_config_and_data()
												_A									=       FlaxMBartForQuestionAnswering(_lowercase      )
												_A									=       model(input_ids=_lowercase      )

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

							def 					__A       (     self:      List[Any]      )			-> List[Any]:
												_A							,_A							,_A									=       self._get_config_and_data()
												_A									=       FlaxMBartForConditionalGeneration(_lowercase      )
												_A									=       lm_model(input_ids=_lowercase      )
												_A									=       (batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs['''logits'''].shape						,   _lowercase      )

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



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





@require_flax
class   SCREAMING_SNAKE_CASE (       lowercase_     ,		unittest.TestCase     ,		lowercase_    ):





							"""simple docstring"""

							A_       =    True
							A_       =    (
							    (
							        FlaxMBartModel,
							        FlaxMBartForConditionalGeneration,
							        FlaxMBartForSequenceClassification,
							        FlaxMBartForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)
							A_       =    (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()

							def 					__A       (     self:      str      )			-> str:
												_A									=       FlaxMBartModelTester(self      )

							def 					__A       (     self:      Any      )			-> List[str]:
												_A							,_A									=       self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																	self.model_tester.check_use_cache_forward(_lowercase						,   _lowercase						,   _lowercase      )

							def 					__A       (     self:      Optional[Any]      )			-> str:
												_A							,_A									=       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(_lowercase						,   _lowercase						,   _lowercase      )

							def 					__A       (     self:      Optional[Any]      )			-> List[str]:
												_A							,_A									=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__      ):
																						_A									=       self._prepare_for_class(_lowercase						,   _lowercase      )
																						_A									=       model_class(_lowercase      )

																						@jax.jit
																						def encode_jitted(__A:      Optional[Any]						,   __A:      Tuple=None						,   **__A:      Dict      ):
																											return model.encode(input_ids=_lowercase						,   attention_mask=_lowercase      )

																						with self.subTest('''JIT Enabled'''      ):
																											_A									=       encode_jitted(**_lowercase      ).to_tuple()

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

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

							def 					__A       (     self:      Optional[int]      )			-> Tuple:
												_A							,_A									=       self.model_tester.prepare_config_and_inputs_for_common()

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

																						_A									=       {
																						    '''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:      Optional[int]						,   __A:      Any						,   __A:      List[str]      ):
																											return model.decode(
																											    decoder_input_ids=_lowercase						,   decoder_attention_mask=_lowercase						,   encoder_outputs=_lowercase						,   )

																						with self.subTest('''JIT Enabled'''      ):
																											_A									=       decode_jitted(**_lowercase      ).to_tuple()

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

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



							@slow
							def 					__A       (     self:      Tuple      )			-> str:





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





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





							"""simple docstring"""

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

							@cached_property
							def 					__A       (     self:      Optional[Any]      )			-> List[str]:
												return AutoTokenizer.from_pretrained(self.model_name      )

							@cached_property
							def 					__A       (     self:      Optional[int]      )			-> Dict:
												_A									=       FlaxMBartForConditionalGeneration.from_pretrained(self.model_name						,   from_pt=_lowercase      )
												return model

							def 					__A       (     self:      str						,   **__A:      List[str]      )			-> int:
												_A									=       self.translate_src_text(**_lowercase      )
												self.assertListEqual(self.expected_text						,   _lowercase      )

							def 					__A       (     self:      List[str]						,   **__A:      Union[str, Any]      )			-> List[str]:
												_A									=       self.tokenizer(self.src_text						,   **_lowercase						,   return_tensors='''np'''      )
												_A									=       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=_lowercase						,   num_beams=2						,   ).sequences
												_A									=       self.tokenizer.batch_decode(_lowercase						,   skip_special_tokens=_lowercase      )
												return generated_words



							@slow
							def 					__A       (     self:      Union[str, Any]      )			-> str:
												self._assert_generated_batch_equal_expected()










from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			42
  __lowerCamelCase		=			42







  def __init__(			self  :List[Any]    ,							_lowercase  :UNetaDModel    ,							_lowercase  :PNDMScheduler					):






     '''simple docstring'''





     super().__init__()

     lowercase__    =  PNDMScheduler.from_config(scheduler.config					)

     self.register_modules(unet=_lowercase    ,							scheduler=_lowercase					)







  @torch.no_grad()
  def __call__(			self  :Union[str, Any]    ,							_lowercase  :int = 1    ,							_lowercase  :int = 50    ,							_lowercase  :Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,							_lowercase  :Optional[str] = "pil"    ,							_lowercase  :bool = True    ,							**_lowercase  :List[Any]    ,							):






     '''simple docstring'''





     lowercase__    =  randn_tensor(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,							generator=_lowercase    ,							device=self.device    ,							)

     self.scheduler.set_timesteps(_lowercase					)
     for t in self.progress_bar(self.scheduler.timesteps					):
        lowercase__    =  self.unet(_lowercase    ,							_lowercase					).sample

        lowercase__    =  self.scheduler.step(_lowercase    ,							_lowercase    ,							_lowercase					).prev_sample

     lowercase__    =  (image / 2 + 0.5).clamp(0    ,							1					)
     lowercase__    =  image.cpu().permute(0    ,							2    ,							3    ,							1					).numpy()
     if output_type == "pil":
        lowercase__    =  self.numpy_to_pil(_lowercase					)

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowercase					)






