



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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.models.blenderbot_small.modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    shift_tokens_right,
				)






def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     str					, __lowerCamelCase						:     int=None					, __lowerCamelCase						:     Optional[Any]=None					, __lowerCamelCase						:     Union[str, Any]=None					, __lowerCamelCase						:     Optional[Any]=None					, __lowerCamelCase						:     Union[str, Any]=None					, __lowerCamelCase						:     Dict=None					, )	->    Union[str, Any]:
					if attention_mask is None:
										__lowerCAmelCase						 =np.where(input_ids != config.pad_token_id					, 1					, 0						)
					if decoder_attention_mask is None:
										__lowerCAmelCase						 =np.where(decoder_input_ids != config.pad_token_id					, 1					, 0						)
					if head_mask is None:
										__lowerCAmelCase						 =np.ones((config.encoder_layers, config.encoder_attention_heads)						)
					if decoder_head_mask is None:
										__lowerCAmelCase						 =np.ones((config.decoder_layers, config.decoder_attention_heads)						)
					if cross_attn_head_mask is None:
										__lowerCAmelCase						 =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     __a			:



	def __init__(    self				:     Union[str, Any]					,						snake_case_				:     List[str]					,						snake_case_				:     str=13					,						snake_case_				:     List[Any]=7					,						snake_case_				:     str=True					,						snake_case_				:     str=False					,						snake_case_				:     List[str]=99					,						snake_case_				:     Tuple=16					,						snake_case_				:     Dict=2					,						snake_case_				:     str=4					,						snake_case_				:     Tuple=4					,						snake_case_				:     str="gelu"					,						snake_case_				:     Tuple=0.1					,						snake_case_				:     Optional[int]=0.1					,						snake_case_				:     int=32					,						snake_case_				:     List[str]=2					,						snake_case_				:     List[str]=1					,						snake_case_				:     List[str]=0					,						snake_case_				:     Union[str, Any]=0.0_2					,						)->				Optional[int]:
						__lowerCAmelCase						 =parent
						__lowerCAmelCase						 =batch_size
						__lowerCAmelCase						 =seq_length
						__lowerCAmelCase						 =is_training
						__lowerCAmelCase						 =use_labels
						__lowerCAmelCase						 =vocab_size
						__lowerCAmelCase						 =hidden_size
						__lowerCAmelCase						 =num_hidden_layers
						__lowerCAmelCase						 =num_attention_heads
						__lowerCAmelCase						 =intermediate_size
						__lowerCAmelCase						 =hidden_act
						__lowerCAmelCase						 =hidden_dropout_prob
						__lowerCAmelCase						 =attention_probs_dropout_prob
						__lowerCAmelCase						 =max_position_embeddings
						__lowerCAmelCase						 =eos_token_id
						__lowerCAmelCase						 =pad_token_id
						__lowerCAmelCase						 =bos_token_id
						__lowerCAmelCase						 =initializer_range



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

						__lowerCAmelCase						 =shift_tokens_right(snake_case_					,						1					,						2)

						__lowerCAmelCase						 =BlenderbotSmallConfig(
						    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=snake_case_					,						)
						__lowerCAmelCase						 =prepare_blenderbot_inputs_dict(snake_case_					,						snake_case_					,						snake_case_)
						return config, inputs_dict



	def 		UpperCamelCase				(    self				:     str)->				List[str]:
						__lowerCAmelCase ,						__lowerCAmelCase						 =self.prepare_config_and_inputs()
						return config, inputs_dict



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						snake_case_				:     int					,						snake_case_				:     Dict					,						snake_case_				:     Union[str, Any])->				str:
						__lowerCAmelCase						 =20
						__lowerCAmelCase						 =model_class_name(snake_case_)

						__lowerCAmelCase						 =model.encode(inputs_dict["""input_ids"""])

						__lowerCAmelCase ,						__lowerCAmelCase						 =(
						    inputs_dict["""decoder_input_ids"""],
						    inputs_dict["""decoder_attention_mask"""],
						)

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

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

						__lowerCAmelCase						 =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,						dtype="""i4""")
						__lowerCAmelCase						 =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_					,						)

						__lowerCAmelCase						 =model.decode(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =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				:     List[str]					,						snake_case_				:     Tuple					,						snake_case_				:     int					,						snake_case_				:     Optional[int])->				List[Any]:
						__lowerCAmelCase						 =20
						__lowerCAmelCase						 =model_class_name(snake_case_)

						__lowerCAmelCase						 =model.encode(inputs_dict["""input_ids"""])

						__lowerCAmelCase ,						__lowerCAmelCase						 =(
						    inputs_dict["""decoder_input_ids"""],
						    inputs_dict["""decoder_attention_mask"""],
						)

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

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

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

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

						__lowerCAmelCase						 =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     __a			(  unittest.TestCase					):
	SCREAMING_SNAKE_CASE  	=							99



	def 		UpperCamelCase				(    self				:     List[Any])->				List[Any]:
						__lowerCAmelCase						 =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					,						)

						__lowerCAmelCase						 =input_ids.shape[0]
						__lowerCAmelCase						 =BlenderbotSmallConfig(
						    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				:     List[str])->				int:
						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =self._get_config_and_data()
						__lowerCAmelCase						 =FlaxBlenderbotSmallForConditionalGeneration(snake_case_)
						__lowerCAmelCase						 =lm_model(input_ids=snake_case_)
						__lowerCAmelCase						 =(batch_size, input_ids.shape[1], config.vocab_size)
						self.assertEqual(outputs["""logits"""].shape					,						snake_case_)



	def 		UpperCamelCase				(    self				:     str)->				List[str]:
						__lowerCAmelCase						 =BlenderbotSmallConfig(
						    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					,						)
						__lowerCAmelCase						 =FlaxBlenderbotSmallForConditionalGeneration(snake_case_)
						__lowerCAmelCase						 =np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					,						dtype=np.intaa)
						__lowerCAmelCase						 =np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					,						dtype=np.intaa)
						__lowerCAmelCase						 =lm_model(input_ids=snake_case_					,						decoder_input_ids=snake_case_)
						__lowerCAmelCase						 =(*summary.shape, config.vocab_size)
						self.assertEqual(outputs["""logits"""].shape					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str])->				Tuple:
						__lowerCAmelCase						 =np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					,						dtype=np.intaa)
						__lowerCAmelCase						 =shift_tokens_right(snake_case_					,						1					,						2)
						__lowerCAmelCase						 =np.equal(snake_case_					,						1).astype(np.floataa).sum()
						__lowerCAmelCase						 =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     __a			(  SCREAMING_SNAKE_CASE						,     unittest.TestCase						,     SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							True
	SCREAMING_SNAKE_CASE  	=							(
	    (
	        FlaxBlenderbotSmallModel,
	        FlaxBlenderbotSmallForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE  	=							(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



	def 		UpperCamelCase				(    self				:     str)->				Optional[Any]:
						__lowerCAmelCase						 =FlaxBlenderbotSmallModelTester(self)



	def 		UpperCamelCase				(    self				:     Optional[Any])->				int:
						__lowerCAmelCase ,						__lowerCAmelCase						 =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 		UpperCamelCase				(    self				:     int)->				Union[str, Any]:
						__lowerCAmelCase ,						__lowerCAmelCase						 =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 		UpperCamelCase				(    self				:     str)->				str:
						__lowerCAmelCase ,						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__):
																__lowerCAmelCase						 =self._prepare_for_class(snake_case_					,						snake_case_)
																__lowerCAmelCase						 =model_class(snake_case_)

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

																with self.subTest("""JIT Enabled"""):
																					__lowerCAmelCase						 =encode_jitted(**snake_case_).to_tuple()

																with self.subTest("""JIT Disabled"""):
																					with jax.disable_jit():
																										__lowerCAmelCase						 =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 		UpperCamelCase				(    self				:     Any)->				List[Any]:
						__lowerCAmelCase ,						__lowerCAmelCase						 =self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__):
																__lowerCAmelCase						 =model_class(snake_case_)
																__lowerCAmelCase						 =model.encode(inputs_dict["""input_ids"""]					,						inputs_dict["""attention_mask"""])

																__lowerCAmelCase						 ={
																    """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_				:     str					,						snake_case_				:     int					,						snake_case_				:     str):
																					return model.decode(
																					    decoder_input_ids=snake_case_					,						decoder_attention_mask=snake_case_					,						encoder_outputs=snake_case_					,						)

																with self.subTest("""JIT Enabled"""):
																					__lowerCAmelCase						 =decode_jitted(**snake_case_).to_tuple()

																with self.subTest("""JIT Disabled"""):
																					with jax.disable_jit():
																										__lowerCAmelCase						 =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 		UpperCamelCase				(    self				:     Optional[int])->				Tuple:
						for model_class_name in self.all_model_classes:
											__lowerCAmelCase						 =model_class_name.from_pretrained("""facebook/blenderbot_small-90M""")
											# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
											__lowerCAmelCase						 =np.ones((1, 1)) * model.config.eos_token_id
											__lowerCAmelCase						 =model(snake_case_)
											self.assertIsNotNone(snake_case_)









from collections.abc import Sequence
from queue import Queue






class     __a			:



	def __init__(    self				:     str					,						snake_case_				:     List[str]					,						snake_case_				:     Tuple					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[Any]=None					,						snake_case_				:     Dict=None)->				Optional[int]:
						__lowerCAmelCase						 =start
						__lowerCAmelCase						 =end
						__lowerCAmelCase						 =val
						__lowerCAmelCase						 =(start + end) // 2
						__lowerCAmelCase						 =left
						__lowerCAmelCase						 =right



	def __repr__(    self				:     List[Any])->				Dict:
						return F"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""






class     __a			:



	def __init__(    self				:     List[str]					,						snake_case_				:     Sequence					,						snake_case_				:     int)->				str:
						__lowerCAmelCase						 =collection
						__lowerCAmelCase						 =function
						if self.collection:
											__lowerCAmelCase						 =self._build_tree(0					,						len(snake_case_) - 1)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[int])->				Optional[int]:
						self._update_tree(self.root					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						snake_case_				:     Any					,						snake_case_				:     Union[str, Any])->				Union[str, Any]:
						return self._query_range(self.root					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str]					,						snake_case_				:     List[str]					,						snake_case_				:     List[str])->				List[Any]:
						if start == end:
											return SegmentTreeNode(snake_case_					,						snake_case_					,						self.collection[start])
						__lowerCAmelCase						 =(start + end) // 2
						__lowerCAmelCase						 =self._build_tree(snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self._build_tree(mid + 1					,						snake_case_)
						return SegmentTreeNode(snake_case_					,						snake_case_					,						self.fn(left.val					,						right.val)					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Tuple					,						snake_case_				:     int					,						snake_case_				:     int)->				List[Any]:
						if node.start == i and node.end == i:
											__lowerCAmelCase						 =val
											return
						if i <= node.mid:
											self._update_tree(node.left					,						snake_case_					,						snake_case_)
						else:
											self._update_tree(node.right					,						snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self.fn(node.left.val					,						node.right.val)



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     str					,						snake_case_				:     List[Any])->				Optional[int]:
						if node.start == i and node.end == j:
											return node.val

						if i <= node.mid:
											if j <= node.mid:
																# range in left child tree
																return self._query_range(node.left					,						snake_case_					,						snake_case_)
											else:
																# range in left child tree and right child tree
																return self.fn(
																    self._query_range(node.left					,						snake_case_					,						node.mid)					,						self._query_range(node.right					,						node.mid + 1					,						snake_case_)					,						)
						else:
											# range in right child tree
											return self._query_range(node.right					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str])->				Tuple:
						if self.root is not None:
											__lowerCAmelCase						 =Queue()
											queue.put(self.root)
											while not queue.empty():
																__lowerCAmelCase						 =queue.get()
																yield node

																if node.left is not None:
																					queue.put(node.left)

																if node.right is not None:
																					queue.put(node.right)


if __name__ == "__main__":
				import operator

				for fn in [operator.add, max, min]:
								print('''*''' * 50)
								lowercase_													=      SegmentTree([2, 1, 5, 3, 4], fn)
								for node in arr.traverse():
												print(node)
								print()

								arr.update(1, 5)
								for node in arr.traverse():
												print(node)
								print()

								print(arr.query_range(3, 4))  # 7
								print(arr.query_range(2, 2))  # 5
								print(arr.query_range(1, 3))  # 13
								print()




