





"""simple docstring"""






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
		A			      =	'''platform'''

		import jax
		import jax.numpy as jnp

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

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







					'''simple docstring'''




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







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

									__a			:    int      							=	shift_tokens_right(_UpperCAmelCase						,      1						,      2  )

									__a			:    Union[str, Any]      							=	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=_UpperCAmelCase						,      )
									__a			:    Tuple      							=	prepare_blenderbot_inputs_dict(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  )
									return config, inputs_dict







					def 			_lowerCamelCase		(    self  ):
									__a							,    __a			:    List[Any]      							=	self.prepare_config_and_inputs()
									return config, inputs_dict







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    Optional[int]      							=	20
									__a			:    List[Any]      							=	model_class_name(_UpperCAmelCase  )

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

									__a							,    __a			:    List[str]      							=	(
									    inputs_dict['''decoder_input_ids'''],
									    inputs_dict['''decoder_attention_mask'''],
									)

									__a			:    str      							=	model.init_cache(decoder_input_ids.shape[0]						,      _UpperCAmelCase						,      _UpperCAmelCase  )
									__a			:    Union[str, Any]      							=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,      dtype='''i4'''  )

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

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

									__a			:    Optional[int]      							=	model.decode(_UpperCAmelCase						,      _UpperCAmelCase  )

									__a			:    int      							=	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						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    Union[str, Any]      							=	20
									__a			:    str      							=	model_class_name(_UpperCAmelCase  )

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

									__a							,    __a			:    Optional[int]      							=	(
									    inputs_dict['''decoder_input_ids'''],
									    inputs_dict['''decoder_attention_mask'''],
									)

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

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

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

									__a			:    Optional[Any]      							=	model.decode(_UpperCAmelCase						,      _UpperCAmelCase						,      decoder_attention_mask=_UpperCAmelCase  )

									__a			:    int      							=	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 					__lowercase      (      unittest.TestCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	99







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[int]      							=	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			:    List[Any]      							=	input_ids.shape[0]
									__a			:    int      							=	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 			_lowerCamelCase		(    self  ):
									__a							,    __a							,    __a			:    List[str]      							=	self._get_config_and_data()
									__a			:    List[str]      							=	FlaxBlenderbotSmallForConditionalGeneration(_UpperCAmelCase  )
									__a			:    Tuple      							=	lm_model(input_ids=_UpperCAmelCase  )
									__a			:    Any      							=	(batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape						,      _UpperCAmelCase  )







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







					def 			_lowerCamelCase		(    self  ):
									__a			:    Tuple      							=	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						,      dtype=np.intaa  )
									__a			:    Union[str, Any]      							=	shift_tokens_right(_UpperCAmelCase						,      1						,      2  )
									__a			:    Tuple      							=	np.equal(_UpperCAmelCase						,      1  ).astype(np.floataa  ).sum()
									__a			:    Union[str, Any]      							=	np.equal(_UpperCAmelCase						,      1  ).astype(np.floataa  ).sum()
									self.assertEqual(shifted.shape						,      input_ids.shape  )
									self.assertEqual(_UpperCAmelCase						,      n_pad_before - 1  )
									self.assertTrue(np.equal(shifted[:, 0]						,      2  ).all()  )






@require_flax
class 					__lowercase      (      _UpperCamelCase ,   unittest.TestCase ,   _UpperCamelCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	True
					__lowerCAmelCase	=	(
					    (
					        FlaxBlenderbotSmallModel,
					        FlaxBlenderbotSmallForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					__lowerCAmelCase	=	(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()







					def 			_lowerCamelCase		(    self  ):
									__a			:    List[Any]      							=	FlaxBlenderbotSmallModelTester(self  )







					def 			_lowerCamelCase		(    self  ):
									__a							,    __a			:    Tuple      							=	self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									__a							,    __a			:    Optional[Any]      							=	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(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									__a							,    __a			:    Tuple      							=	self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__  ):
																	__a			:    Optional[Any]      							=	self._prepare_for_class(_UpperCAmelCase						,      _UpperCAmelCase  )
																	__a			:    int      							=	model_class(_UpperCAmelCase  )

																	@jax.jit
																	def encode_jitted(_UpperCAmelCase						,      _UpperCAmelCase=None						,      **_UpperCAmelCase  ):
																					return model.encode(input_ids=_UpperCAmelCase						,      attention_mask=_UpperCAmelCase  )

																	with self.subTest('''JIT Enabled'''  ):
																					__a			:    List[Any]      							=	encode_jitted(**_UpperCAmelCase  ).to_tuple()

																	with self.subTest('''JIT Disabled'''  ):
																					with jax.disable_jit():
																									__a			:    int      							=	encode_jitted(**_UpperCAmelCase  ).to_tuple()

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







					def 			_lowerCamelCase		(    self  ):
									__a							,    __a			:    List[str]      							=	self.model_tester.prepare_config_and_inputs_for_common()

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

																	__a			:    Dict      							=	{
																	    '''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(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
																					return model.decode(
																					    decoder_input_ids=_UpperCAmelCase						,      decoder_attention_mask=_UpperCAmelCase						,      encoder_outputs=_UpperCAmelCase						,      )

																	with self.subTest('''JIT Enabled'''  ):
																					__a			:    Any      							=	decode_jitted(**_UpperCAmelCase  ).to_tuple()

																	with self.subTest('''JIT Disabled'''  ):
																					with jax.disable_jit():
																									__a			:    Optional[int]      							=	decode_jitted(**_UpperCAmelCase  ).to_tuple()

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







					@slow
					def 			_lowerCamelCase		(    self  ):
									for model_class_name in self.all_model_classes:
													__a			:    Tuple      							=	model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''  )
													# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
													__a			:    Optional[Any]      							=	np.ones((1, 1)  ) * model.config.eos_token_id
													__a			:    Tuple      							=	model(_UpperCAmelCase  )
													self.assertIsNotNone(_UpperCAmelCase  )







from __future__ import annotations

from collections.abc import MutableSequence





class 					A__     :

     '''simple docstring'''






     def __init__( self      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    int   ,						_SCREAMING_SNAKE_CASE      :    MutableSequence[float] ):






           """simple docstring"""




           if len(_SCREAMING_SNAKE_CASE ) != degree + 1:
                 raise ValueError(
                     'The number of coefficients should be equal to the degree + 1.' )

           UpperCamelCase					       =  list(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  degree







     def __add__( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    Polynomial ):






           """simple docstring"""




           if self.degree > polynomial_a.degree:
                 UpperCamelCase					       =  self.coefficients[:]
                 for i in range(polynomial_a.degree + 1 ):
                       coefficients[i] += polynomial_a.coefficients[i]
                 return Polynomial(self.degree   ,						_SCREAMING_SNAKE_CASE )
           else:
                 UpperCamelCase					       =  polynomial_a.coefficients[:]
                 for i in range(self.degree + 1 ):
                       coefficients[i] += self.coefficients[i]
                 return Polynomial(polynomial_a.degree   ,						_SCREAMING_SNAKE_CASE )







     def __sub__( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    Polynomial ):






           """simple docstring"""




           return self + polynomial_a * Polynomial(0   ,						[-1] )







     def __neg__( self      :    Any ):






           """simple docstring"""




           return Polynomial(self.degree   ,						[-c for c in self.coefficients] )







     def __mul__( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Polynomial ):






           """simple docstring"""




           UpperCamelCase					       =  [0] * (self.degree + polynomial_a.degree + 1)
           for i in range(self.degree + 1 ):
                 for j in range(polynomial_a.degree + 1 ):
                       coefficients[i + j] += (
                           self.coefficients[i] * polynomial_a.coefficients[j]
                       )

           return Polynomial(self.degree + polynomial_a.degree   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    int | float ):






           """simple docstring"""




           UpperCamelCase					       =  0
           for i in range(self.degree + 1 ):
                 result += self.coefficients[i] * (substitution**i)
           return result







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






           """simple docstring"""




           UpperCamelCase					       =  ''
           for i in range(self.degree   ,						-1   ,						-1 ):
                 if self.coefficients[i] == 0:
                       continue
                 elif self.coefficients[i] > 0:
                       if polynomial:
                             polynomial += " + "
                 else:
                       polynomial += " - "

                 if i == 0:
                       polynomial += str(abs(self.coefficients[i] ) )
                 elif i == 1:
                       polynomial += str(abs(self.coefficients[i] ) ) + "x"
                 else:
                       polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(_SCREAMING_SNAKE_CASE )

           return polynomial







     def __repr__( self      :    str ):






           """simple docstring"""




           return self.__str__()







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  [0] * self.degree
           for i in range(self.degree ):
                 UpperCamelCase					       =  self.coefficients[i + 1] * (i + 1)
           return Polynomial(self.degree - 1   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    int | float = 0 ):






           """simple docstring"""




           UpperCamelCase					       =  [0] * (self.degree + 2)
           UpperCamelCase					       =  constant
           for i in range(self.degree + 1 ):
                 UpperCamelCase					       =  self.coefficients[i] / (i + 1)
           return Polynomial(self.degree + 1   ,						_SCREAMING_SNAKE_CASE )







     def __eq__( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    object ):






           """simple docstring"""




           if not isinstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE ):
                 return False

           if self.degree != polynomial_a.degree:
                 return False

           for i in range(self.degree + 1 ):
                 if self.coefficients[i] != polynomial_a.coefficients[i]:
                       return False

           return True







     def __ne__( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    object ):






           """simple docstring"""




           return not self.__eq__(_SCREAMING_SNAKE_CASE )



