




from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__				     = logging.get_logger(__name__)

UpperCAmelCase__				     = {
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       Tuple       	=		'xglm'
						UpperCamelCase_	:       Tuple       	=		['past_key_values']

						UpperCamelCase_	:       str       	=		{
						    'num_attention_heads': 'attention_heads',
						    'hidden_size': 'd_model',
						    'num_hidden_layers': 'num_layers',
						}






						def __init__( self :     Tuple				,							lowerCamelCase__ :     Optional[Any]=256_008				,							lowerCamelCase__ :     Optional[int]=2_048				,							lowerCamelCase__ :     Union[str, Any]=1_024				,							lowerCamelCase__ :     Optional[int]=4_096				,							lowerCamelCase__ :     Tuple=24				,							lowerCamelCase__ :     List[str]=16				,							lowerCamelCase__ :     Union[str, Any]="gelu"				,							lowerCamelCase__ :     Union[str, Any]=0.1				,							lowerCamelCase__ :     Any=0.1				,							lowerCamelCase__ :     Dict=0.0				,							lowerCamelCase__ :     Optional[int]=0.0				,							lowerCamelCase__ :     Optional[Any]=0.0_2				,							lowerCamelCase__ :     List[Any]=True				,							lowerCamelCase__ :     Union[str, Any]=True				,							lowerCamelCase__ :     List[Any]=2				,							lowerCamelCase__ :     Union[str, Any]=1				,							lowerCamelCase__ :     Optional[int]=0				,							lowerCamelCase__ :     str=2				,							**lowerCamelCase__ :     List[Any]				,							)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    vocab_size
													__lowercase      =    max_position_embeddings
													__lowercase      =    d_model
													__lowercase      =    ffn_dim
													__lowercase      =    num_layers
													__lowercase      =    attention_heads
													__lowercase      =    activation_function
													__lowercase      =    dropout
													__lowercase      =    attention_dropout
													__lowercase      =    activation_dropout
													__lowercase      =    layerdrop
													__lowercase      =    init_std
													__lowercase      =    scale_embedding  # scale factor will be sqrt(d_model) if True
													__lowercase      =    use_cache

													super().__init__(
													    pad_token_id=lowerCamelCase__				,							bos_token_id=lowerCamelCase__				,							eos_token_id=lowerCamelCase__				,							decoder_start_token_id=lowerCamelCase__				,							**lowerCamelCase__				,							)












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
			UpperCAmelCase__				     = "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 		_A(       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Dict=None				,       UpperCamelCase__							:			Optional[int]=None				,       UpperCamelCase__							:			int=None				,       UpperCamelCase__							:			Union[str, Any]=None				,       UpperCamelCase__							:			Optional[Any]=None				,       UpperCamelCase__							:			List[str]=None				,       )    -> int:
							'''simple docstring'''


							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__ :     Dict				,							lowerCamelCase__ :     Union[str, Any]=13				,							lowerCamelCase__ :     Union[str, Any]=7				,							lowerCamelCase__ :     Dict=True				,							lowerCamelCase__ :     int=False				,							lowerCamelCase__ :     Any=99				,							lowerCamelCase__ :     str=16				,							lowerCamelCase__ :     Union[str, Any]=2				,							lowerCamelCase__ :     Union[str, Any]=4				,							lowerCamelCase__ :     Tuple=4				,							lowerCamelCase__ :     Optional[Any]="gelu"				,							lowerCamelCase__ :     int=0.1				,							lowerCamelCase__ :     Tuple=0.1				,							lowerCamelCase__ :     Any=32				,							lowerCamelCase__ :     List[str]=2				,							lowerCamelCase__ :     Optional[Any]=1				,							lowerCamelCase__ :     List[Any]=0				,							lowerCamelCase__ :     int=0.0_2				,							)    ->						Optional[int]:
													"""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     UpperCAmelCase_      ( self :     int				)    ->						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      =    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__				,							)
													__lowercase      =    prepare_blenderbot_inputs_dict(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)
													return config, inputs_dict






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[int]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[Any]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Tuple				)    ->						Any:
													"""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     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     List[Any]				)    ->						Optional[Any]:
													"""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"""

						UpperCamelCase_	:       List[Any]       	=		99






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Dict:
													"""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      =    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 :     Tuple				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase     ,     __lowercase     ,     __lowercase      =    self._get_config_and_data()
													__lowercase      =    FlaxBlenderbotForConditionalGeneration(lowerCamelCase__				)
													__lowercase      =    lm_model(input_ids=lowerCamelCase__				)
													__lowercase      =    (batch_size, input_ids.shape[1], config.vocab_size)
													self.assertEqual(outputs['''logits'''].shape				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    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				,							)
													__lowercase      =    FlaxBlenderbotForConditionalGeneration(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     UpperCAmelCase_      ( self :     List[str]				)    ->						List[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 (     __SCREAMING_SNAKE_CASE       ,						unittest.TestCase       ,						__SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

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






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    FlaxBlenderbotModelTester(self				)






						def     UpperCAmelCase_      ( self :     Any				)    ->						List[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     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Optional[Any]:
													"""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     UpperCAmelCase_      ( self :     List[str]				)    ->						int:
													"""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__ :     List[str]				,							lowerCamelCase__ :     Any=None				,							**lowerCamelCase__ :     Dict				):
																																		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     UpperCAmelCase_      ( self :     Dict				)    ->						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__ :     List[str]				,							lowerCamelCase__ :     List[str]				,							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     UpperCAmelCase_      ( self :     int				)    ->						Union[str, Any]:
													"""simple docstring"""


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






						@unittest.skipUnless(jax_device != '''cpu'''				,							'''3B test too slow on CPU.'''				)
						@slow
						def     UpperCAmelCase_      ( self :     Tuple				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
													__lowercase      =    {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

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

													__lowercase      =    ['''Sam''']
													__lowercase      =    tokenizer(lowerCamelCase__				,							return_tensors='''jax'''				)

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

													__lowercase      =    tokenizer.batch_decode(lowerCamelCase__				,							**lowerCamelCase__				)
													assert generated_txt[0].strip() == tgt_text






