





'''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
					lowerCAmelCase_					:    Optional[Any]      	=			'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				( lowercase						:      int    ,							lowercase						:      Any    ,							lowercase						:      Optional[Any]=None    ,							lowercase						:      Tuple=None    ,							lowercase						:      List[str]=None    ,							lowercase						:      int=None    ,							lowercase						:      List[str]=None    ,							lowercase						:      str=None    ,							)	->	Optional[Any]:
				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 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				def __init__(  self     :					Optional[Any]					,				__a     :					Tuple					,				__a     :					Optional[int]=13					,				__a     :					Any=7					,				__a     :					Optional[int]=True					,				__a     :					Any=False					,				__a     :					Optional[int]=99					,				__a     :					Dict=16					,				__a     :					Optional[Any]=2					,				__a     :					Dict=4					,				__a     :					List[Any]=4					,				__a     :					Optional[Any]="gelu"					,				__a     :					Any=0.1					,				__a     :					Optional[Any]=0.1					,				__a     :					Dict=32					,				__a     :					Optional[Any]=2					,				__a     :					Optional[Any]=1					,				__a     :					Tuple=0					,				__a     :					Optional[int]=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     :					Dict					):
								_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     :					int					):
								_a	,					_a								= self.prepare_config_and_inputs()
								return config, inputs_dict




				def   UpperCamelCase__   (  self     :					List[Any]					,				__a     :					int					,				__a     :					Any					,				__a     :					int					):
								_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     :					Any					,				__a     :					int					,				__a     :					Optional[int]					,				__a     :					Dict					):
								_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 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				__a            =99




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_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     :					Union[str, Any]					):
								_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     :					List[str]					):
								_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     :					Tuple					):
								_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 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							unittest.TestCase       ,							lowerCamelCase_      ):






				"""simple docstring"""







				__a            =True
				__a            =(
				    (
				        FlaxBlenderbotModel,
				        FlaxBlenderbotForConditionalGeneration,
				    )
				    if is_flax_available()
				    else ()
				)
				__a            =(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= FlaxBlenderbotModelTester(self					)




				def   UpperCamelCase__   (  self     :					List[str]					):
								_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     :					Optional[int]					):
								_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     :					Any					):
								_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     :					Tuple					,				__a     :					Union[str, Any]=None					,				**__a     :					Optional[Any]					):
																				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     :					Optional[int]					):
								_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     :					Dict					,				__a     :					str					,				__a     :					Tuple					):
																				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     :					Dict					):
								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     :					List[str]					):
								_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'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase_					:    Dict      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Optional[int]      	=			{
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='deta'
				__a            ={
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				}




				def __init__(  self     :					List[str]					,				__a     :					List[str]=None					,				__a     :					Dict=9_00					,				__a     :					str=20_48					,				__a     :					Tuple=6					,				__a     :					List[str]=20_48					,				__a     :					str=8					,				__a     :					Union[str, Any]=6					,				__a     :					int=10_24					,				__a     :					List[Any]=8					,				__a     :					Dict=0.0					,				__a     :					Tuple=True					,				__a     :					Optional[Any]="relu"					,				__a     :					Tuple=2_56					,				__a     :					Optional[Any]=0.1					,				__a     :					int=0.0					,				__a     :					List[Any]=0.0					,				__a     :					Optional[int]=0.02					,				__a     :					str=1.0					,				__a     :					Dict=True					,				__a     :					Dict=False					,				__a     :					Optional[int]="sine"					,				__a     :					Any=5					,				__a     :					List[str]=4					,				__a     :					Optional[int]=4					,				__a     :					List[str]=True					,				__a     :					str=3_00					,				__a     :					int=True					,				__a     :					int=True					,				__a     :					Tuple=1					,				__a     :					Optional[int]=5					,				__a     :					Tuple=2					,				__a     :					Dict=1					,				__a     :					Optional[int]=1					,				__a     :					Any=5					,				__a     :					Optional[int]=2					,				__a     :					Dict=0.1					,				__a     :					str=0.25					,				**__a     :					Tuple					,				):
								if backbone_config is None:
												logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."					)
												_a								= CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]					)
								else:
												if isinstance(__a					,				__a					):
																_a								= backbone_config.pop("model_type"					)
																_a								= CONFIG_MAPPING[backbone_model_type]
																_a								= config_class.from_dict(__a					)

								_a								= backbone_config
								_a								= num_queries
								_a								= max_position_embeddings
								_a								= d_model
								_a								= encoder_ffn_dim
								_a								= encoder_layers
								_a								= encoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= activation_function
								_a								= init_std
								_a								= init_xavier_std
								_a								= encoder_layerdrop
								_a								= auxiliary_loss
								_a								= position_embedding_type
								# deformable attributes
								_a								= num_feature_levels
								_a								= encoder_n_points
								_a								= decoder_n_points
								_a								= two_stage
								_a								= two_stage_num_proposals
								_a								= with_box_refine
								_a								= assign_first_stage
								if two_stage is True and with_box_refine is False:
												raise ValueError("If two_stage is True, with_box_refine must be True."					)
								# Hungarian matcher
								_a								= class_cost
								_a								= bbox_cost
								_a								= giou_cost
								# Loss coefficients
								_a								= mask_loss_coefficient
								_a								= dice_loss_coefficient
								_a								= bbox_loss_coefficient
								_a								= giou_loss_coefficient
								_a								= eos_coefficient
								_a								= focal_alpha
								super().__init__(is_encoder_decoder=__a					,				**__a					)




				@property
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								return self.encoder_attention_heads




				@property
				def   UpperCamelCase__   (  self     :					Dict					):
								return self.d_model




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= copy.deepcopy(self.__dict__					)
								_a								= self.backbone_config.to_dict()
								_a								= self.__class__.model_type
								return output
