






"""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 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase=None      ,       __UpperCamelCase=None      ,       __UpperCamelCase=None      ,       __UpperCamelCase=None      ,       __UpperCamelCase=None      ,       __UpperCamelCase=None      ,       )						->    Any:

		"""simple docstring"""







		if attention_mask is None:
				lowerCAmelCase_   :							int									=			np.where(input_ids != config.pad_token_id      ,       1      ,       0       )
		if decoder_attention_mask is None:
				lowerCAmelCase_   :							Dict									=			np.where(decoder_input_ids != config.pad_token_id      ,       1      ,       0       )
		if head_mask is None:
				lowerCAmelCase_   :							Any									=			np.ones((config.encoder_layers, config.encoder_attention_heads)       )
		if decoder_head_mask is None:
				lowerCAmelCase_   :							Tuple									=			np.ones((config.decoder_layers, config.decoder_attention_heads)       )
		if cross_attn_head_mask is None:
				lowerCAmelCase_   :							Optional[int]									=			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							:	Optional[Any]						,			a_							:	List[str]						,			a_							:	Optional[int]=13						,			a_							:	Tuple=7						,			a_							:	Tuple=True						,			a_							:	int=False						,			a_							:	str=99						,			a_							:	Union[str, Any]=16						,			a_							:	int=2						,			a_							:	Optional[int]=4						,			a_							:	List[str]=4						,			a_							:	Union[str, Any]="gelu"						,			a_							:	Any=0.1						,			a_							:	List[Any]=0.1						,			a_							:	List[str]=32						,			a_							:	Optional[Any]=2						,			a_							:	Dict=1						,			a_							:	List[str]=0						,			a_							:	Optional[Any]=0.02						,			):
				lowerCAmelCase_   :							str									=			parent
				lowerCAmelCase_   :							List[str]									=			batch_size
				lowerCAmelCase_   :							List[str]									=			seq_length
				lowerCAmelCase_   :							Optional[Any]									=			is_training
				lowerCAmelCase_   :							Tuple									=			use_labels
				lowerCAmelCase_   :							Any									=			vocab_size
				lowerCAmelCase_   :							str									=			hidden_size
				lowerCAmelCase_   :							Tuple									=			num_hidden_layers
				lowerCAmelCase_   :							Optional[int]									=			num_attention_heads
				lowerCAmelCase_   :							Union[str, Any]									=			intermediate_size
				lowerCAmelCase_   :							Optional[int]									=			hidden_act
				lowerCAmelCase_   :							Optional[Any]									=			hidden_dropout_prob
				lowerCAmelCase_   :							Tuple									=			attention_probs_dropout_prob
				lowerCAmelCase_   :							Union[str, Any]									=			max_position_embeddings
				lowerCAmelCase_   :							List[Any]									=			eos_token_id
				lowerCAmelCase_   :							str									=			pad_token_id
				lowerCAmelCase_   :							str									=			bos_token_id
				lowerCAmelCase_   :							Tuple									=			initializer_range





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				lowerCAmelCase_   :							List[Any]									=			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						,			self.vocab_size	)						,			3						,			self.vocab_size	)
				lowerCAmelCase_   :							List[str]									=			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						,			dtype=np.intaa	))						,			-1	)

				lowerCAmelCase_   :							Optional[Any]									=			shift_tokens_right(a_						,			1						,			2	)

				lowerCAmelCase_   :							Optional[Any]									=			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_						,			)
				lowerCAmelCase_   :							Optional[int]									=			prepare_blenderbot_inputs_dict(a_						,			a_						,			a_	)
				return config, inputs_dict





		def    lowerCamelCase       (   self							:	List[str]	):
				lowerCAmelCase_				,	lowerCAmelCase_   :							Any									=			self.prepare_config_and_inputs()
				return config, inputs_dict





		def    lowerCamelCase       (   self							:	Optional[Any]						,			a_							:	Optional[Any]						,			a_							:	List[str]						,			a_							:	Optional[int]	):
				lowerCAmelCase_   :							List[Any]									=			20
				lowerCAmelCase_   :							Tuple									=			model_class_name(a_	)

				lowerCAmelCase_   :							List[Any]									=			model.encode(inputs_dict["input_ids"]	)

				lowerCAmelCase_				,	lowerCAmelCase_   :							Tuple									=			(
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

				lowerCAmelCase_   :							int									=			model.init_cache(decoder_input_ids.shape[0]						,			a_						,			a_	)
				lowerCAmelCase_   :							Tuple									=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,			dtype="i4"	)

				lowerCAmelCase_   :							Union[str, Any]									=			jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]						,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,			)
				lowerCAmelCase_   :							List[Any]									=			model.decode(
				    decoder_input_ids[:, :-1]						,			a_						,			decoder_attention_mask=a_						,			past_key_values=a_						,			decoder_position_ids=a_						,			)

				lowerCAmelCase_   :							Optional[Any]									=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,			dtype="i4"	)
				lowerCAmelCase_   :							Dict									=			model.decode(
				    decoder_input_ids[:, -1:]						,			a_						,			decoder_attention_mask=a_						,			past_key_values=outputs_cache.past_key_values						,			decoder_position_ids=a_						,			)

				lowerCAmelCase_   :							int									=			model.decode(a_						,			a_	)

				lowerCAmelCase_   :							Optional[Any]									=			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    lowerCamelCase       (   self							:	int						,			a_							:	Any						,			a_							:	List[Any]						,			a_							:	Any	):
				lowerCAmelCase_   :							int									=			20
				lowerCAmelCase_   :							Union[str, Any]									=			model_class_name(a_	)

				lowerCAmelCase_   :							Union[str, Any]									=			model.encode(inputs_dict["input_ids"]	)

				lowerCAmelCase_				,	lowerCAmelCase_   :							Optional[Any]									=			(
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

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

				lowerCAmelCase_   :							Any									=			model.init_cache(decoder_input_ids.shape[0]						,			a_						,			a_	)
				lowerCAmelCase_   :							Union[str, Any]									=			jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]						,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,			)

				lowerCAmelCase_   :							List[str]									=			model.decode(
				    decoder_input_ids[:, :-1]						,			a_						,			decoder_attention_mask=a_						,			past_key_values=a_						,			decoder_position_ids=a_						,			)
				lowerCAmelCase_   :							Optional[int]									=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,			dtype="i4"	)
				lowerCAmelCase_   :							Optional[int]									=			model.decode(
				    decoder_input_ids[:, -1:]						,			a_						,			past_key_values=outputs_cache.past_key_values						,			decoder_attention_mask=a_						,			decoder_position_ids=a_						,			)

				lowerCAmelCase_   :							Tuple									=			model.decode(a_						,			a_						,			decoder_attention_mask=a_	)

				lowerCAmelCase_   :							str									=			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'''






		a_	:				Optional[int]            =							99





		def    lowerCamelCase       (   self							:	Dict	):
				lowerCAmelCase_   :							str									=			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_   :							str									=			input_ids.shape[0]
				lowerCAmelCase_   :							List[Any]									=			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    lowerCamelCase       (   self							:	Union[str, Any]	):
				lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_   :							List[str]									=			self._get_config_and_data()
				lowerCAmelCase_   :							int									=			FlaxBlenderbotForConditionalGeneration(a_	)
				lowerCAmelCase_   :							Any									=			lm_model(input_ids=a_	)
				lowerCAmelCase_   :							List[Any]									=			(batch_size, input_ids.shape[1], config.vocab_size)
				self.assertEqual(outputs["logits"].shape						,			a_	)





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				lowerCAmelCase_   :							Optional[int]									=			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						,			)
				lowerCAmelCase_   :							int									=			FlaxBlenderbotForConditionalGeneration(a_	)
				lowerCAmelCase_   :							Optional[int]									=			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						,			dtype=np.intaa	)
				lowerCAmelCase_   :							List[Any]									=			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						,			dtype=np.intaa	)
				lowerCAmelCase_   :							Dict									=			lm_model(input_ids=a_						,			decoder_input_ids=a_	)
				lowerCAmelCase_   :							int									=			(*summary.shape, config.vocab_size)
				self.assertEqual(outputs["logits"].shape						,			a_	)





		def    lowerCamelCase       (   self							:	List[Any]	):
				lowerCAmelCase_   :							Optional[int]									=			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						,			dtype=np.intaa	)
				lowerCAmelCase_   :							Tuple									=			shift_tokens_right(a_						,			1						,			2	)
				lowerCAmelCase_   :							str									=			np.equal(a_						,			1	).astype(np.floataa	).sum()
				lowerCAmelCase_   :							str									=			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'''






		a_	:				List[str]            =							True
		a_	:				Optional[Any]            =							(
		    (
		        FlaxBlenderbotModel,
		        FlaxBlenderbotForConditionalGeneration,
		    )
		    if is_flax_available()
		    else ()
		)
		a_	:				List[Any]            =							(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()





		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				lowerCAmelCase_   :							Tuple									=			FlaxBlenderbotModelTester(self	)





		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_				,	lowerCAmelCase_   :							List[Any]									=			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    lowerCamelCase       (   self							:	Optional[Any]	):
				lowerCAmelCase_				,	lowerCAmelCase_   :							str									=			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    lowerCamelCase       (   self							:	int	):
				lowerCAmelCase_				,	lowerCAmelCase_   :							Any									=			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__	):
								lowerCAmelCase_   :							Optional[Any]									=			self._prepare_for_class(a_						,			a_	)
								lowerCAmelCase_   :							str									=			model_class(a_	)

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

								with self.subTest("JIT Enabled"	):
										lowerCAmelCase_   :							int									=			encode_jitted(**a_	).to_tuple()

								with self.subTest("JIT Disabled"	):
										with jax.disable_jit():
												lowerCAmelCase_   :							List[str]									=			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    lowerCamelCase       (   self							:	int	):
				lowerCAmelCase_				,	lowerCAmelCase_   :							Optional[int]									=			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__	):
								lowerCAmelCase_   :							Any									=			model_class(a_	)
								lowerCAmelCase_   :							int									=			model.encode(inputs_dict["input_ids"]						,			inputs_dict["attention_mask"]	)

								lowerCAmelCase_   :							Union[str, Any]									=			{
								    "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_							:	Optional[int]						,			a_							:	Dict						,			a_							:	Any	):
										return model.decode(
										    decoder_input_ids=a_						,			decoder_attention_mask=a_						,			encoder_outputs=a_						,			)

								with self.subTest("JIT Enabled"	):
										lowerCAmelCase_   :							Union[str, Any]									=			decode_jitted(**a_	).to_tuple()

								with self.subTest("JIT Disabled"	):
										with jax.disable_jit():
												lowerCAmelCase_   :							Optional[Any]									=			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    lowerCamelCase       (   self							:	Union[str, Any]	):
				for model_class_name in self.all_model_classes:
						lowerCAmelCase_   :							Dict									=			model_class_name.from_pretrained("facebook/blenderbot-400M-distill"	)
						# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
						lowerCAmelCase_   :							Dict									=			np.ones((1, 1)	) * model.config.eos_token_id
						lowerCAmelCase_   :							Any									=			model(a_	)
						self.assertIsNotNone(a_	)





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

				lowerCAmelCase_   :							Dict									=			FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"						,			from_pt=a_	)
				lowerCAmelCase_   :							Tuple									=			BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"	)

				lowerCAmelCase_   :							List[str]									=			["Sam"]
				lowerCAmelCase_   :							int									=			tokenizer(a_						,			return_tensors="jax"	)

				lowerCAmelCase_   :							Optional[int]									=			model.generate(**a_						,			**a_	)
				lowerCAmelCase_   :							str									=			"Sam is a great name. It means \"sun\" in Gaelic."

				lowerCAmelCase_   :							Tuple									=			tokenizer.batch_decode(a_						,			**a_	)
				assert generated_txt[0].strip() == tgt_text

def    a__       (						__UpperCamelCase ):
      if not head:
            return True
      # split the list to two parts
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     head.next, head
      while fast and fast.next:
            SCREAMING_SNAKE_CASE_      =     fast.next.next
            SCREAMING_SNAKE_CASE_      =     slow.next
      SCREAMING_SNAKE_CASE_      =     slow.next
      SCREAMING_SNAKE_CASE_      =     None  # Don't forget here! But forget still works!
      # reverse the second part
      SCREAMING_SNAKE_CASE_      =     None
      while second:
            SCREAMING_SNAKE_CASE_      =     second.next
            SCREAMING_SNAKE_CASE_      =     node
            SCREAMING_SNAKE_CASE_      =     second
            SCREAMING_SNAKE_CASE_      =     nxt
      # compare two parts
      # second part has the same or one less node
      while node:
            if node.val != head.val:
                  return False
            SCREAMING_SNAKE_CASE_      =     node.next
            SCREAMING_SNAKE_CASE_      =     head.next
      return True


def    a__       (						__UpperCamelCase ):
      if not head or not head.next:
            return True

      # 1. Get the midpoint (slow)
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     head
      while fast and fast.next:
            SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     fast.next.next, slow.next

      # 2. Push the second half into the stack
      SCREAMING_SNAKE_CASE_      =     [slow.val]
      while slow.next:
            SCREAMING_SNAKE_CASE_      =     slow.next
            stack.append(slow.val )

      # 3. Comparison
      while stack:
            if stack.pop() != cur.val:
                  return False
            SCREAMING_SNAKE_CASE_      =     cur.next

      return True





def    a__       (						__UpperCamelCase ):
      if not head or not head.next:
            return True
      SCREAMING_SNAKE_CASE_      =     {}
      SCREAMING_SNAKE_CASE_      =     0
      while head:
            if head.val in d:
                  d[head.val].append(__UpperCamelCase )
            else:
                  SCREAMING_SNAKE_CASE_      =     [pos]
            SCREAMING_SNAKE_CASE_      =     head.next
            pos += 1
      SCREAMING_SNAKE_CASE_      =     pos - 1
      SCREAMING_SNAKE_CASE_      =     0
      for v in d.values():
            if len(__UpperCamelCase ) % 2 != 0:
                  middle += 1
            else:
                  SCREAMING_SNAKE_CASE_      =     0
                  for i in range(0   ,					len(__UpperCamelCase ) ):
                        if v[i] + v[len(__UpperCamelCase ) - 1 - step] != checksum:
                              return False
                        step += 1
            if middle > 1:
                  return False
      return True


