



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
	snake_case__					:     Union[str, Any]    		=   """platform"""

	import jax
	import jax.numpy as jnp

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



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE=None  ,	):
	if attention_mask is None:
		__lowercase		       =			np.where(input_ids != config.pad_token_id  ,	1  ,	0   )
	if decoder_attention_mask is None:
		__lowercase		       =			np.where(decoder_input_ids != config.pad_token_id  ,	1  ,	0   )
	if head_mask is None:
		__lowercase		       =			np.ones((config.encoder_layers, config.encoder_attention_heads)   )
	if decoder_head_mask is None:
		__lowercase		       =			np.ones((config.decoder_layers, config.decoder_attention_heads)   )
	if cross_attn_head_mask is None:
		__lowercase		       =			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   :


						'''simple docstring'''





						def __init__(	self	:							Dict							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							List[Any]=13							,      lowerCamelCase	:							Tuple=7							,      lowerCamelCase	:							List[str]=True							,      lowerCamelCase	:							Optional[Any]=False							,      lowerCamelCase	:							Optional[int]=99							,      lowerCamelCase	:							Any=16							,      lowerCamelCase	:							Optional[Any]=2							,      lowerCamelCase	:							Optional[int]=4							,      lowerCamelCase	:							Any=4							,      lowerCamelCase	:							Optional[Any]="gelu"							,      lowerCamelCase	:							Tuple=0.1							,      lowerCamelCase	:							Dict=0.1							,      lowerCamelCase	:							Optional[int]=32							,      lowerCamelCase	:							Tuple=2							,      lowerCamelCase	:							List[Any]=1							,      lowerCamelCase	:							Tuple=0							,      lowerCamelCase	:							Optional[Any]=0.02							,      ):






							'''simple docstring'''



							__lowercase		       =			parent
							__lowercase		       =			batch_size
							__lowercase		       =			seq_length
							__lowercase		       =			is_training
							__lowercase		       =			use_labels
							__lowercase		       =			vocab_size
							__lowercase		       =			hidden_size
							__lowercase		       =			num_hidden_layers
							__lowercase		       =			num_attention_heads
							__lowercase		       =			intermediate_size
							__lowercase		       =			hidden_act
							__lowercase		       =			hidden_dropout_prob
							__lowercase		       =			attention_probs_dropout_prob
							__lowercase		       =			max_position_embeddings
							__lowercase		       =			eos_token_id
							__lowercase		       =			pad_token_id
							__lowercase		       =			bos_token_id
							__lowercase		       =			initializer_range



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,      self.vocab_size	)							,      3							,      self.vocab_size	)
							__lowercase		       =			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,      dtype=np.intaa	))							,      -1	)

							__lowercase		       =			shift_tokens_right(lowerCamelCase							,      1							,      2	)

							__lowercase		       =			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=lowerCamelCase							,      )
							__lowercase		       =			prepare_blenderbot_inputs_dict(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
							return config, inputs_dict



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			self.prepare_config_and_inputs()
							return config, inputs_dict



						def     _snake_case	(	self	:							int							,      lowerCamelCase	:							str							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Dict	):






							'''simple docstring'''



							__lowercase		       =			20
							__lowercase		       =			model_class_name(lowerCamelCase	)

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

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

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

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

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

							__lowercase		       =			model.decode(lowerCamelCase							,      lowerCamelCase	)

							__lowercase		       =			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	:							Any							,      lowerCamelCase	:							List[Any]							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							__lowercase		       =			20
							__lowercase		       =			model_class_name(lowerCamelCase	)

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

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

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

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

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

							__lowercase		       =			model.decode(lowerCamelCase							,      lowerCamelCase							,      decoder_attention_mask=lowerCamelCase	)

							__lowercase		       =			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	):


						'''simple docstring'''





						_snake_case			:     Optional[Any]      							=      99



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			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							,      )

							__lowercase		       =			input_ids.shape[0]
							__lowercase		       =			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     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase       ,    __lowercase       ,    __lowercase		       =			self._get_config_and_data()
							__lowercase		       =			FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase	)
							__lowercase		       =			lm_model(input_ids=lowerCamelCase	)
							__lowercase		       =			(batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["logits"].shape							,      lowerCamelCase	)



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							__lowercase		       =			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							,      )
							__lowercase		       =			FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase	)
							__lowercase		       =			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]							,      dtype=np.intaa	)
							__lowercase		       =			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]							,      dtype=np.intaa	)
							__lowercase		       =			lm_model(input_ids=lowerCamelCase							,      decoder_input_ids=lowerCamelCase	)
							__lowercase		       =			(*summary.shape, config.vocab_size)
							self.assertEqual(outputs["logits"].shape							,      lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



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

@require_flax
class   _A   ( _lowercase  ,						unittest.TestCase  ,						_lowercase	):


						'''simple docstring'''





						_snake_case			:     Union[str, Any]      							=      True
						_snake_case			:     Tuple      							=      (
						    (
						        FlaxBlenderbotSmallModel,
						        FlaxBlenderbotSmallForConditionalGeneration,
						    )
						    if is_flax_available()
						    else ()
						)
						_snake_case			:     Tuple      							=      (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			FlaxBlenderbotSmallModelTester(self	)



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
								self.model_tester.check_use_cache_forward(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			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(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__	):
									__lowercase		       =			self._prepare_for_class(lowerCamelCase							,      lowerCamelCase	)
									__lowercase		       =			model_class(lowerCamelCase	)

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

									with self.subTest("JIT Enabled"	):
										__lowercase		       =			encode_jitted(**lowerCamelCase	).to_tuple()

									with self.subTest("JIT Disabled"	):
										with jax.disable_jit():
											__lowercase		       =			encode_jitted(**lowerCamelCase	).to_tuple()

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



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()

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

									__lowercase		       =			{
									    "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(lowerCamelCase	:							int							,      lowerCamelCase	:							Union[str, Any]							,      lowerCamelCase	:							Tuple	):
										return model.decode(
										    decoder_input_ids=lowerCamelCase							,      decoder_attention_mask=lowerCamelCase							,      encoder_outputs=lowerCamelCase							,      )

									with self.subTest("JIT Enabled"	):
										__lowercase		       =			decode_jitted(**lowerCamelCase	).to_tuple()

									with self.subTest("JIT Disabled"	):
										with jax.disable_jit():
											__lowercase		       =			decode_jitted(**lowerCamelCase	).to_tuple()

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



						@slow
						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							for model_class_name in self.all_model_classes:
								__lowercase		       =			model_class_name.from_pretrained("facebook/blenderbot_small-90M"	)
								# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
								__lowercase		       =			np.ones((1, 1)	) * model.config.eos_token_id
								__lowercase		       =			model(lowerCamelCase	)
								self.assertIsNotNone(lowerCamelCase	)








from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class   _A   :


						'''simple docstring'''





						_snake_case			:     int
						_snake_case			:     TreeNode | None      							=      None
						_snake_case			:     TreeNode | None      							=      None

snake_case__					:     Dict    		=   namedtuple("""CoinsDistribResult""", """moves excess""")



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):

	if root is None:
		return 0

	# Validation
	def count_nodes(_SCREAMING_SNAKE_CASE   ) -> int:
		if node is None:
			return 0

		return count_nodes(node.left   ) + count_nodes(node.right   ) + 1

	def count_coins(_SCREAMING_SNAKE_CASE   ) -> int:
		if node is None:
			return 0

		return count_coins(node.left   ) + count_coins(node.right   ) + node.data

	if count_nodes(_SCREAMING_SNAKE_CASE   ) != count_coins(_SCREAMING_SNAKE_CASE   ):
		raise ValueError("The nodes number should be same as the number of coins"   )

	# Main calculation
	def get_distrib(_SCREAMING_SNAKE_CASE   ) -> CoinsDistribResult:

		if node is None:
			return CoinsDistribResult(0  ,	1   )

		__lowercase       ,    __lowercase		       =			get_distrib(node.left   )
		__lowercase       ,    __lowercase		       =			get_distrib(node.right   )

		__lowercase		       =			1 - left_distrib_excess
		__lowercase		       =			1 - right_distrib_excess

		__lowercase		       =			(
		    left_distrib_moves
		    + right_distrib_moves
		    + abs(_SCREAMING_SNAKE_CASE   )
		    + abs(_SCREAMING_SNAKE_CASE   )
		)
		__lowercase		       =			node.data - coins_to_left - coins_to_right

		return CoinsDistribResult(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

	return get_distrib(_SCREAMING_SNAKE_CASE   )[0]


if __name__ == "__main__":
	import doctest

	doctest.testmod()



