


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

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
		SCREAMING_SNAKE_CASE__     					=			"platform"

		import jax
		import jax.numpy as jnp

		from transformers import BlenderbotTokenizer
		from transformers.models.blenderbot.modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    shift_tokens_right,
		)


def     lowercase	(    a     , a     , a=None     , a=None     , a=None     , a=None     , a=None     , a=None     , ):



		'''simple docstring'''




		if attention_mask is None:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						np.where(input_ids != config.pad_token_id     , 1     , 0 )
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						np.where(decoder_input_ids != config.pad_token_id     , 1     , 0 )
		if head_mask is None:
				SCREAMING_SNAKE_CASE_				:int				     =						np.ones((config.encoder_layers, config.encoder_attention_heads) )
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						np.ones((config.decoder_layers, config.decoder_attention_heads) )
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE_				:Any				     =						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": attention_mask,
		}



class 						_UpperCAmelCase      :






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






		def _snake_case		(						self				:     Tuple):
				SCREAMING_SNAKE_CASE_				:int				     =						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,  self.vocab_size)   ,  3   ,  self.vocab_size)
				SCREAMING_SNAKE_CASE_				:int				     =						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,  dtype=np.intaa))   ,  -1)

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						shift_tokens_right(_lowercase   ,  1   ,  2)

				SCREAMING_SNAKE_CASE_				:Any				     =						BlenderbotConfig(
				    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   ,  initializer_range=self.initializer_range   ,  use_cache=_lowercase   ,  )
				SCREAMING_SNAKE_CASE_				:Dict				     =						prepare_blenderbot_inputs_dict(_lowercase   ,  _lowercase   ,  _lowercase)
				return config, inputs_dict






		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Dict				     =						self.prepare_config_and_inputs()
				return config, inputs_dict






		def _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     Tuple):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						20
				SCREAMING_SNAKE_CASE_				:List[str]				     =						model_class_name(_lowercase)

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model.encode(inputs_dict["input_ids"])

				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						(
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

				SCREAMING_SNAKE_CASE_				:int				     =						model.init_cache(decoder_input_ids.shape[0]   ,  _lowercase   ,  _lowercase)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,  dtype="i4")

				SCREAMING_SNAKE_CASE_				:str				     =						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_				:int				     =						model.decode(
				    decoder_input_ids[:, :-1]   ,  _lowercase   ,  decoder_attention_mask=_lowercase   ,  past_key_values=_lowercase   ,  decoder_position_ids=_lowercase   ,  )

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,  dtype="i4")
				SCREAMING_SNAKE_CASE_				:int				     =						model.decode(
				    decoder_input_ids[:, -1:]   ,  _lowercase   ,  decoder_attention_mask=_lowercase   ,  past_key_values=outputs_cache.past_key_values   ,  decoder_position_ids=_lowercase   ,  )

				SCREAMING_SNAKE_CASE_				:str				     =						model.decode(_lowercase   ,  _lowercase)

				SCREAMING_SNAKE_CASE_				: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				:     Dict   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Any				     =						20
				SCREAMING_SNAKE_CASE_				:List[str]				     =						model_class_name(_lowercase)

				SCREAMING_SNAKE_CASE_				:int				     =						model.encode(inputs_dict["input_ids"])

				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Dict				     =						(
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

				SCREAMING_SNAKE_CASE_				:Tuple				     =						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_				:Union[str, Any]				     =						model.init_cache(decoder_input_ids.shape[0]   ,  _lowercase   ,  _lowercase)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						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_				:Optional[Any]				     =						model.decode(
				    decoder_input_ids[:, :-1]   ,  _lowercase   ,  decoder_attention_mask=_lowercase   ,  past_key_values=_lowercase   ,  decoder_position_ids=_lowercase   ,  )
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,  dtype="i4")
				SCREAMING_SNAKE_CASE_				:Tuple				     =						model.decode(
				    decoder_input_ids[:, -1:]   ,  _lowercase   ,  past_key_values=outputs_cache.past_key_values   ,  decoder_attention_mask=_lowercase   ,  decoder_position_ids=_lowercase   ,  )

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						model.decode(_lowercase   ,  _lowercase   ,  decoder_attention_mask=_lowercase)

				SCREAMING_SNAKE_CASE_				: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 						_UpperCAmelCase      (     unittest.TestCase							):
		lowerCamelCase_	:			str       			=  9_9






		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:str				     =						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_				:Any				     =						input_ids.shape[0]
				SCREAMING_SNAKE_CASE_				:Tuple				     =						BlenderbotConfig(
				    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				:     Tuple):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:str				     =						self._get_config_and_data()
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						FlaxBlenderbotForConditionalGeneration(_lowercase)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						lm_model(input_ids=_lowercase)
				SCREAMING_SNAKE_CASE_				:Tuple				     =						(batch_size, input_ids.shape[1], config.vocab_size)
				self.assertEqual(outputs["logits"].shape   ,  _lowercase)






		def _snake_case		(						self				:     Optional[Any]):
				SCREAMING_SNAKE_CASE_				:Dict				     =						BlenderbotConfig(
				    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_				:str				     =						FlaxBlenderbotForConditionalGeneration(_lowercase)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]   ,  dtype=np.intaa)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]   ,  dtype=np.intaa)
				SCREAMING_SNAKE_CASE_				:Any				     =						lm_model(input_ids=_lowercase   ,  decoder_input_ids=_lowercase)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						(*summary.shape, config.vocab_size)
				self.assertEqual(outputs["logits"].shape   ,  _lowercase)







		def _snake_case		(						self				:     Any):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]   ,  dtype=np.intaa)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						shift_tokens_right(_lowercase   ,  1   ,  2)
				SCREAMING_SNAKE_CASE_				:int				     =						np.equal(_lowercase   ,  1).astype(np.floataa).sum()
				SCREAMING_SNAKE_CASE_				:Tuple				     =						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 						_UpperCAmelCase      (     _SCREAMING_SNAKE_CASE		,		unittest.TestCase		,		_SCREAMING_SNAKE_CASE							):
		lowerCamelCase_	:			List[str]       			=  True
		lowerCamelCase_	:			Tuple       			=  (
		    (
		        FlaxBlenderbotModel,
		        FlaxBlenderbotForConditionalGeneration,
		    )
		    if is_flax_available()
		    else ()
		)
		lowerCamelCase_	:			Tuple       			=  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






		def _snake_case		(						self				:     Tuple):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						FlaxBlenderbotModelTester(self)






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Any				     =						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 _snake_case		(						self				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[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(_lowercase   ,  _lowercase   ,  _lowercase)






		def _snake_case		(						self				:     int):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[str]				     =						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_				:List[str]				     =						self._prepare_for_class(_lowercase   ,  _lowercase)
								SCREAMING_SNAKE_CASE_				:int				     =						model_class(_lowercase)

								@jax.jit
								def encode_jitted(UpperCAmelCase				:     str   ,  UpperCAmelCase				:     Optional[int]=None   ,  **UpperCAmelCase				:     Dict):
										return model.encode(input_ids=_lowercase   ,  attention_mask=_lowercase)

								with self.subTest("JIT Enabled"):
										SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						encode_jitted(**_lowercase).to_tuple()

								with self.subTest("JIT Disabled"):
										with jax.disable_jit():
												SCREAMING_SNAKE_CASE_				:List[Any]				     =						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 _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						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_				:List[Any]				     =						model_class(_lowercase)
								SCREAMING_SNAKE_CASE_				:List[Any]				     =						model.encode(inputs_dict["input_ids"]   ,  inputs_dict["attention_mask"])

								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						{
								    "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(UpperCAmelCase				:     Optional[Any]   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     Any):
										return model.decode(
										    decoder_input_ids=_lowercase   ,  decoder_attention_mask=_lowercase   ,  encoder_outputs=_lowercase   ,  )

								with self.subTest("JIT Enabled"):
										SCREAMING_SNAKE_CASE_				:Optional[int]				     =						decode_jitted(**_lowercase).to_tuple()

								with self.subTest("JIT Disabled"):
										with jax.disable_jit():
												SCREAMING_SNAKE_CASE_				:str				     =						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 _snake_case		(						self				:     Optional[Any]):



				for model_class_name in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model_class_name.from_pretrained("facebook/blenderbot-400M-distill")
						# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
						SCREAMING_SNAKE_CASE_				:List[str]				     =						np.ones((1, 1)) * model.config.eos_token_id
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						model(_lowercase)
						self.assertIsNotNone(_lowercase)







		@unittest.skipUnless(jax_device != "cpu"   ,  "3B test too slow on CPU.")
		@slow
		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						{"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						{"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

				SCREAMING_SNAKE_CASE_				:int				     =						FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"   ,  from_pt=_lowercase)
				SCREAMING_SNAKE_CASE_				:str				     =						BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B")

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						["Sam"]
				SCREAMING_SNAKE_CASE_				:str				     =						tokenizer(_lowercase   ,  return_tensors="jax")

				SCREAMING_SNAKE_CASE_				:Tuple				     =						model.generate(**_lowercase   ,  **_lowercase)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						"Sam is a great name. It means \"sun\" in Gaelic."

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						tokenizer.batch_decode(_lowercase   ,  **_lowercase)
				assert generated_txt[0].strip() == tgt_text








'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def __init__(   self       ,  *_lowercase       ,  **_lowercase ):

												"""simple docstring"""



												warnings.warn(
												    """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
												    """ use DonutImageProcessor instead."""       ,  _lowercase       ,  )
												super().__init__(*_lowercase       ,  **_lowercase )
