


"""simple docstring"""




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


		'''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							, ):
				__a           =			parent
				__a           =			batch_size
				__a           =			seq_length
				__a           =			is_training
				__a           =			use_labels
				__a           =			vocab_size
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			intermediate_size
				__a           =			hidden_act
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			max_position_embeddings
				__a           =			eos_token_id
				__a           =			pad_token_id
				__a           =			bos_token_id
				__a           =			initializer_range

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

				__a           =			shift_tokens_right(_a							, 1							, 2      )

				__a           =			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							, )
				__a           =			prepare_blenderbot_inputs_dict(_a							, _a							, _a      )
				return config, inputs_dict

		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.prepare_config_and_inputs()
				return config, inputs_dict

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a      ):
				__a           =			20
				__a           =			model_class_name(_a      )

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

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

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

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

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

				__a           =			model.decode(_a							, _a      )

				__a           =			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      ):
				__a           =			20
				__a           =			model_class_name(_a      )

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

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

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

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

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

				__a           =			model.decode(_a							, _a							, decoder_attention_mask=_a      )

				__a           =			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 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				9_9

		def 			__UpperCAmelCase       (  self      ):
				__a           =			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							, )

				__a           =			input_ids.shape[0]
				__a           =			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      ):
				__a      ,     __a      ,     __a           =			self._get_config_and_data()
				__a           =			FlaxBlenderbotForConditionalGeneration(_a      )
				__a           =			lm_model(input_ids=_a      )
				__a           =			(batch_size, input_ids.shape[1], config.vocab_size)
				self.assertEqual(outputs['''logits'''].shape							, _a      )

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





		def 			__UpperCAmelCase       (  self      ):
				__a           =			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]							, dtype=np.intaa      )
				__a           =			shift_tokens_right(_a							, 1							, 2      )
				__a           =			np.equal(_a							, 1      ).astype(np.floataa      ).sum()
				__a           =			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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			unittest.TestCase     ,			__SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[int]										=				True
		__UpperCAmelCase		:			Dict										=				(
		    (
		        FlaxBlenderbotModel,
		        FlaxBlenderbotForConditionalGeneration,
		    )
		    if is_flax_available()
		    else ()
		)
		__UpperCAmelCase		:			List[Any]										=				(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

		def 			__UpperCAmelCase       (  self      ):
				__a           =			FlaxBlenderbotModelTester(self      )

		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			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      ):
				__a      ,     __a           =			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      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__      ):
								__a           =			self._prepare_for_class(_a							, _a      )
								__a           =			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'''      ):
										__a           =			encode_jitted(**_a      ).to_tuple()

								with self.subTest('''JIT Disabled'''      ):
										with jax.disable_jit():
												__a           =			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      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__      ):
								__a           =			model_class(_a      )
								__a           =			model.encode(inputs_dict['''input_ids''']							, inputs_dict['''attention_mask''']      )

								__a           =			{
								    '''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'''      ):
										__a           =			decode_jitted(**_a      ).to_tuple()

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





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

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

				__a           =			['''Sam''']
				__a           =			tokenizer(_a							, return_tensors='''jax'''      )

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

				__a           =			tokenizer.batch_decode(_a							, **_a      )
				assert generated_txt[0].strip() == tgt_text











"""simple docstring"""







def      __UpperCAmelCase      (    __lowerCamelCase = 50	)							->      int:
		lowercase__   :    int				=			[[0] * 3 for _ in range(length + 1	)]

		for row_length in range(length + 1	):
				for tile_length in range(2						,     5	):
						for tile_start in range(row_length - tile_length + 1	):
								different_colour_ways_number[row_length][tile_length - 2] += (
								    different_colour_ways_number[row_length - tile_start - tile_length][
								        tile_length - 2
								    ]
								    + 1
								)

		return sum(different_colour_ways_number[length]	)


if __name__ == "__main__":
							print(F'''{solution() = }''')
