

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
				_a:    Dict 							=			"""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       __lowerCAmelCase      (						A       ,     A       ,     A=None       ,     A=None       ,     A=None       ,     A=None       ,     A=None       ,     A=None       ,     ):
	if attention_mask is None:
		UpperCAmelCase_       				=			np.where(input_ids != config.pad_token_id       ,     1       ,     0    )
	if decoder_attention_mask is None:
		UpperCAmelCase_       				=			np.where(decoder_input_ids != config.pad_token_id       ,     1       ,     0    )
	if head_mask is None:
		UpperCAmelCase_       				=			np.ones((config.encoder_layers, config.encoder_attention_heads)    )
	if decoder_head_mask is None:
		UpperCAmelCase_       				=			np.ones((config.decoder_layers, config.decoder_attention_heads)    )
	if cross_attn_head_mask is None:
		UpperCAmelCase_       				=			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		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						List[Any]=13		,	lowerCAmelCase	:						List[str]=7		,	lowerCAmelCase	:						Dict=True		,	lowerCAmelCase	:						Any=False		,	lowerCAmelCase	:						str=99		,	lowerCAmelCase	:						Dict=16		,	lowerCAmelCase	:						List[str]=2		,	lowerCAmelCase	:						Tuple=4		,	lowerCAmelCase	:						Any=4		,	lowerCAmelCase	:						str="gelu"		,	lowerCAmelCase	:						Optional[Any]=0.1		,	lowerCAmelCase	:						Dict=0.1		,	lowerCAmelCase	:						Optional[Any]=32		,	lowerCAmelCase	:						int=2		,	lowerCAmelCase	:						int=1		,	lowerCAmelCase	:						str=0		,	lowerCAmelCase	:						Optional[int]=0.02		,	):


		'''simple docstring'''







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




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			shift_tokens_right(lowerCAmelCase		,	1		,	2							)

		UpperCAmelCase_       				=			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=lowerCAmelCase		,	)
		UpperCAmelCase_       				=			prepare_blenderbot_inputs_dict(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
		return config, inputs_dict




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


		'''simple docstring'''







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




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			20
		UpperCAmelCase_       				=			model_class_name(lowerCAmelCase							)

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

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

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

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

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

		UpperCAmelCase_       				=			model.decode(lowerCAmelCase		,	lowerCAmelCase							)

		UpperCAmelCase_       				=			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	:						Union[str, Any]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			20
		UpperCAmelCase_       				=			model_class_name(lowerCAmelCase							)

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

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

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

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

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

		UpperCAmelCase_       				=			model.decode(lowerCAmelCase		,	lowerCAmelCase		,	decoder_attention_mask=lowerCAmelCase							)

		UpperCAmelCase_       				=			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						):
	SCREAMING_SNAKE_CASE__    				=     99




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


		'''simple docstring'''







		UpperCAmelCase_       				=			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		,	)

		UpperCAmelCase_       				=			input_ids.shape[0]
		UpperCAmelCase_       				=			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       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			self._get_config_and_data()
		UpperCAmelCase_       				=			FlaxBlenderbotForConditionalGeneration(lowerCAmelCase							)
		UpperCAmelCase_       				=			lm_model(input_ids=lowerCAmelCase							)
		UpperCAmelCase_       				=			(batch_size, input_ids.shape[1], config.vocab_size)
		self.assertEqual(outputs["logits"].shape		,	lowerCAmelCase							)




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







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




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,	dtype=np.intaa							)
		UpperCAmelCase_       				=			shift_tokens_right(lowerCAmelCase		,	1		,	2							)
		UpperCAmelCase_       				=			np.equal(lowerCAmelCase		,	1							).astype(np.floataa							).sum()
		UpperCAmelCase_       				=			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 						__UpperCamelCase    (					lowercase     ,     unittest.TestCase     ,     lowercase						):
	SCREAMING_SNAKE_CASE__    				=     True
	SCREAMING_SNAKE_CASE__    				=     (
	    (
	        FlaxBlenderbotModel,
	        FlaxBlenderbotForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE__    				=     (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




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


		'''simple docstring'''







		UpperCAmelCase_       				=			FlaxBlenderbotModelTester(self							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			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       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			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       __A					(		self	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__							):
				UpperCAmelCase_       				=			self._prepare_for_class(lowerCAmelCase		,	lowerCAmelCase							)
				UpperCAmelCase_       				=			model_class(lowerCAmelCase							)

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

				with self.subTest("JIT Enabled"							):
					UpperCAmelCase_       				=			encode_jitted(**lowerCAmelCase							).to_tuple()

				with self.subTest("JIT Disabled"							):
					with jax.disable_jit():
						UpperCAmelCase_       				=			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       __A					(		self	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs_for_common()

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

				UpperCAmelCase_       				=			{
				    "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	:						Optional[int]		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						List[str]							):
					return model.decode(
					    decoder_input_ids=lowerCAmelCase		,	decoder_attention_mask=lowerCAmelCase		,	encoder_outputs=lowerCAmelCase		,	)

				with self.subTest("JIT Enabled"							):
					UpperCAmelCase_       				=			decode_jitted(**lowerCAmelCase							).to_tuple()

				with self.subTest("JIT Disabled"							):
					with jax.disable_jit():
						UpperCAmelCase_       				=			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       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		for model_class_name in self.all_model_classes:
			UpperCAmelCase_       				=			model_class_name.from_pretrained("facebook/blenderbot-400M-distill"							)
			# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
			UpperCAmelCase_       				=			np.ones((1, 1)							) * model.config.eos_token_id
			UpperCAmelCase_       				=			model(lowerCAmelCase							)
			self.assertIsNotNone(lowerCAmelCase							)




	@unittest.skipUnless(jax_device != "cpu"		,	"3B test too slow on CPU."							)
	@slow
	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
		UpperCAmelCase_       				=			{"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

		UpperCAmelCase_       				=			FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"		,	from_pt=lowerCAmelCase							)
		UpperCAmelCase_       				=			BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"							)

		UpperCAmelCase_       				=			["Sam"]
		UpperCAmelCase_       				=			tokenizer(lowerCAmelCase		,	return_tensors="jax"							)

		UpperCAmelCase_       				=			model.generate(**lowerCAmelCase		,	**lowerCAmelCase							)
		UpperCAmelCase_       				=			"Sam is a great name. It means \"sun\" in Gaelic."

		UpperCAmelCase_       				=			tokenizer.batch_decode(lowerCAmelCase		,	**lowerCAmelCase							)
		assert generated_txt[0].strip() == tgt_text


from collections.abc import Sequence



def       __lowerCAmelCase      (						A       ,     A = False    ):
	if not arr:
		return 0

	UpperCAmelCase_       				=			0 if allow_empty_subarrays else float("-inf"    )
	UpperCAmelCase_       				=			0.0
	for num in arr:
		UpperCAmelCase_       				=			max(0 if allow_empty_subarrays else num       ,     curr_sum + num    )
		UpperCAmelCase_       				=			max(A       ,     A    )

	return max_sum


if __name__ == "__main__":
				from doctest import testmod

				testmod()

				_a:    Any 							=			[-2, 1, -3, 4, -1, 2, 1, -5, 4]
				print(F'{max_subarray_sum(nums) = }')