






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
	lowercase    				=   "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 		__UpperCAmelCase     (	a_				,	a_				,	a_=None				,	a_=None				,	a_=None				,	a_=None				,	a_=None				,	a_=None				,	):
							if attention_mask is None:
														snake_case_           =				np.where(input_ids != config.pad_token_id				,	1				,	0)
							if decoder_attention_mask is None:
														snake_case_           =				np.where(decoder_input_ids != config.pad_token_id				,	1				,	0)
							if head_mask is None:
														snake_case_           =				np.ones((config.encoder_layers, config.encoder_attention_heads))
							if decoder_head_mask is None:
														snake_case_           =				np.ones((config.decoder_layers, config.decoder_attention_heads))
							if cross_attn_head_mask is None:
														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": attention_mask,
							}




class 					UpperCamelCase_ :



	'''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=0.02			,     )			->       Optional[int]:
								snake_case_           =				parent
								snake_case_           =				batch_size
								snake_case_           =				seq_length
								snake_case_           =				is_training
								snake_case_           =				use_labels
								snake_case_           =				vocab_size
								snake_case_           =				hidden_size
								snake_case_           =				num_hidden_layers
								snake_case_           =				num_attention_heads
								snake_case_           =				intermediate_size
								snake_case_           =				hidden_act
								snake_case_           =				hidden_dropout_prob
								snake_case_           =				attention_probs_dropout_prob
								snake_case_           =				max_position_embeddings
								snake_case_           =				eos_token_id
								snake_case_           =				pad_token_id
								snake_case_           =				bos_token_id
								snake_case_           =				initializer_range




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

								snake_case_           =				shift_tokens_right(a			,     1			,     2   )

								snake_case_           =				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=a			,     )
								snake_case_           =				prepare_blenderbot_inputs_dict(a			,     a			,     a   )
								return config, inputs_dict




	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								snake_case_     ,						snake_case_           =				self.prepare_config_and_inputs()
								return config, inputs_dict




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a   )			->       List[Any]:
								snake_case_           =				20
								snake_case_           =				model_class_name(a   )

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

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

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

								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)			,     )
								snake_case_           =				model.decode(
								    decoder_input_ids[:, :-1]			,     a			,     decoder_attention_mask=a			,     past_key_values=a			,     decoder_position_ids=a			,     )

								snake_case_           =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,     dtype='i4'   )
								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			,     )

								snake_case_           =				model.decode(a			,     a   )

								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 				_UpperCamelCase     (			self			,     a			,     a			,     a   )			->       int:
								snake_case_           =				20
								snake_case_           =				model_class_name(a   )

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

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

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

								snake_case_           =				model.init_cache(decoder_input_ids.shape[0]			,     a			,     a   )
								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)			,     )

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

								snake_case_           =				model.decode(a			,     a			,     decoder_attention_mask=a   )

								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 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	lowerCAmelCase						=					9_9




	def 				_UpperCamelCase     (			self   )			->       List[str]:
								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			,     )

								snake_case_           =				input_ids.shape[0]
								snake_case_           =				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 				_UpperCamelCase     (			self   )			->       Any:
								snake_case_     ,						snake_case_     ,						snake_case_           =				self._get_config_and_data()
								snake_case_           =				FlaxBlenderbotForConditionalGeneration(a   )
								snake_case_           =				lm_model(input_ids=a   )
								snake_case_           =				(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['logits'].shape			,     a   )




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






	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_           =				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,     dtype=np.intaa   )
								snake_case_           =				shift_tokens_right(a			,     1			,     2   )
								snake_case_           =				np.equal(a			,     1   ).astype(np.floataa   ).sum()
								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 					UpperCamelCase_ (     snake_case_			,   unittest.TestCase			,   snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					True
	lowerCAmelCase						=					(
	    (
	        FlaxBlenderbotModel,
	        FlaxBlenderbotForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	lowerCAmelCase						=					(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




	def 				_UpperCamelCase     (			self   )			->       Tuple:
								snake_case_           =				FlaxBlenderbotModelTester(self   )




	def 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_     ,						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 				_UpperCamelCase     (			self   )			->       Any:
								snake_case_     ,						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 				_UpperCamelCase     (			self   )			->       Any:
								snake_case_     ,						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__   ):
																						snake_case_           =				self._prepare_for_class(a			,     a   )
																						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'   ):
																													snake_case_           =				encode_jitted(**a   ).to_tuple()

																						with self.subTest('JIT Disabled'   ):
																													with jax.disable_jit():
																																				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 				_UpperCamelCase     (			self   )			->       List[Any]:
								snake_case_     ,						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__   ):
																						snake_case_           =				model_class(a   )
																						snake_case_           =				model.encode(inputs_dict['input_ids']			,     inputs_dict['attention_mask']   )

																						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'   ):
																													snake_case_           =				decode_jitted(**a   ).to_tuple()

																						with self.subTest('JIT Disabled'   ):
																													with jax.disable_jit():
																																				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 				_UpperCamelCase     (			self   )			->       Tuple:
								for model_class_name in self.all_model_classes:
															snake_case_           =				model_class_name.from_pretrained('facebook/blenderbot-400M-distill'   )
															# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
															snake_case_           =				np.ones((1, 1)   ) * model.config.eos_token_id
															snake_case_           =				model(a   )
															self.assertIsNotNone(a   )






	@unittest.skipUnless(jax_device != 'cpu'			,     '3B test too slow on CPU.'   )
	@slow
	def 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				{'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
								snake_case_           =				{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

								snake_case_           =				FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'			,     from_pt=a   )
								snake_case_           =				BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'   )

								snake_case_           =				['Sam']
								snake_case_           =				tokenizer(a			,     return_tensors='jax'   )

								snake_case_           =				model.generate(**a			,     **a   )
								snake_case_           =				'Sam is a great name. It means "sun" in Gaelic.'

								snake_case_           =				tokenizer.batch_decode(a			,     **a   )
								assert generated_txt[0].strip() == tgt_text













from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

lowercase    				=   TypeVar("T")
lowercase    				=   TypeVar("U")




class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       List[Any]:
								snake_case_           =				key
								snake_case_           =				val
								snake_case_           =				None
								snake_case_           =				None






	def __repr__(			self   )			->       str:
								return (
								    F'''Node: key: {self.key}, val: {self.val}, '''
								    F'''has next: {bool(self.next   )}, has prev: {bool(self.prev   )}'''
								)






class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	def __init__(			self   )			->       None:
								snake_case_           =				DoubleLinkedListNode(a			,     a   )
								snake_case_           =				DoubleLinkedListNode(a			,     a   )
								snake_case_     ,						snake_case_           =				self.rear, self.head




	def __repr__(			self   )			->       str:
								snake_case_           =				['DoubleLinkedList']
								snake_case_           =				self.head
								while node.next is not None:
															rep.append(str(a   )   )
															snake_case_           =				node.next
								rep.append(str(self.rear   )   )
								return ",\n    ".join(a   )




	def 				_UpperCamelCase     (			self			,     a   )			->       None:
								snake_case_           =				self.rear.prev

								# All nodes other than self.head are guaranteed to have non-None previous
								assert previous is not None

								snake_case_           =				node
								snake_case_           =				previous
								snake_case_           =				node
								snake_case_           =				self.rear






	def 				_UpperCamelCase     (			self			,     a   )			->       DoubleLinkedListNode[T, U] | None:

								if node.prev is None or node.next is None:
															return None

								snake_case_           =				node.next
								snake_case_           =				node.prev
								snake_case_           =				None
								snake_case_           =				None
								return node






class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	lowerCAmelCase						=					{}




	def __init__(			self			,     a   )			->       str:
								snake_case_           =				DoubleLinkedList()
								snake_case_           =				capacity
								snake_case_           =				0
								snake_case_           =				0
								snake_case_           =				0
								snake_case_           =				{}




	def __repr__(			self   )			->       str:
								return (
								    F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
								    F'''capacity={self.capacity}, current size={self.num_keys})'''
								)




	def __contains__(			self			,     a   )			->       bool:
								return key in self.cache




	def 				_UpperCamelCase     (			self			,     a   )			->       U | None:
								# Note: pythonic interface would throw KeyError rather than return None

								if key in self.cache:
															self.hits += 1
															snake_case_           =				self.cache[key]
															snake_case_           =				self.list.remove(self.cache[key]   )
															assert node == value_node

															# node is guaranteed not None because it is in self.cache
															assert node is not None
															self.list.add(a   )
															return node.val
								self.miss += 1
								return None




	def 				_UpperCamelCase     (			self			,     a			,     a   )			->       None:

								if key not in self.cache:
															if self.num_keys >= self.capacity:
																						# delete first node (oldest) when over capacity
																						snake_case_           =				self.list.head.next

																						# guaranteed to have a non-None first node when num_keys > 0
																						# explain to type checker via assertions
																						assert first_node is not None
																						assert first_node.key is not None
																						assert (
																						    self.list.remove(a   ) is not None
																						)  # node guaranteed to be in list assert node.key is not None

																						del self.cache[first_node.key]
																						self.num_keys -= 1
															snake_case_           =				DoubleLinkedListNode(a			,     a   )
															self.list.add(self.cache[key]   )
															self.num_keys += 1

								else:
															# bump node to the end of the list, update value
															snake_case_           =				self.list.remove(self.cache[key]   )
															assert node is not None  # node guaranteed to be in list
															snake_case_           =				value
															self.list.add(a   )






	@classmethod
	def 				_UpperCamelCase     (			cls			,     a = 1_28   )			->       Callable[[Callable[[T], U]], Callable[..., U]]:

								def cache_decorator_inner(a   ) -> Callable[..., U]:
															def cache_decorator_wrapper(*a   ) -> U:
																						if func not in cls.decorator_function_to_instance_map:
																													snake_case_           =				LRUCache(a   )

																						snake_case_           =				cls.decorator_function_to_instance_map[func].get(args[0]   )
																						if result is None:
																													snake_case_           =				func(*a   )
																													cls.decorator_function_to_instance_map[func].put(args[0]			,     a   )
																						return result

															def cache_info() -> LRUCache[T, U]:
																						return cls.decorator_function_to_instance_map[func]

															setattr(a			,     'cache_info'			,     a   )  # noqa: B010

															return cache_decorator_wrapper

								return cache_decorator_inner


if __name__ == "__main__":
	import doctest

	doctest.testmod()





