



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







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

								UpperCAmelCase :       Dict 							=		shift_tokens_right(__snake_case		,			1		,			2		)

								UpperCAmelCase :       str 							=		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=__snake_case		,			)
								UpperCAmelCase :       List[str] 							=		prepare_blenderbot_inputs_dict(__snake_case		,			__snake_case		,			__snake_case		)
								return config, inputs_dict







	def      A	(						self			:     str		)			->		List[str]:
								UpperCAmelCase :       Any 							=		self.prepare_config_and_inputs()
								return config, inputs_dict







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     Any		,			__snake_case			:     List[Any]		,			__snake_case			:     Optional[Any]		)			->		Any:
								UpperCAmelCase :       Dict 							=		20
								UpperCAmelCase :       Tuple 							=		model_class_name(__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		model.encode(inputs_dict['''input_ids''']		)

								UpperCAmelCase :       List[str] 							=		(
								    inputs_dict["""decoder_input_ids"""],
								    inputs_dict["""decoder_attention_mask"""],
								)

								UpperCAmelCase :       int 							=		model.init_cache(decoder_input_ids.shape[0]		,			__snake_case		,			__snake_case		)
								UpperCAmelCase :       Dict 							=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,			dtype='''i4'''		)

								UpperCAmelCase :       int 							=		jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]		,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,			)
								UpperCAmelCase :       List[str] 							=		model.decode(
								    decoder_input_ids[:, :-1]		,			__snake_case		,			decoder_attention_mask=__snake_case		,			past_key_values=__snake_case		,			decoder_position_ids=__snake_case		,			)

								UpperCAmelCase :       str 							=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,			dtype='''i4'''		)
								UpperCAmelCase :       Optional[int] 							=		model.decode(
								    decoder_input_ids[:, -1:]		,			__snake_case		,			decoder_attention_mask=__snake_case		,			past_key_values=outputs_cache.past_key_values		,			decoder_position_ids=__snake_case		,			)

								UpperCAmelCase :       int 							=		model.decode(__snake_case		,			__snake_case		)

								UpperCAmelCase :       Union[str, 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      A	(						self			:     Optional[int]		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Union[str, Any]		)			->		Optional[int]:
								UpperCAmelCase :       str 							=		20
								UpperCAmelCase :       Tuple 							=		model_class_name(__snake_case		)

								UpperCAmelCase :       List[str] 							=		model.encode(inputs_dict['''input_ids''']		)

								UpperCAmelCase :       int 							=		(
								    inputs_dict["""decoder_input_ids"""],
								    inputs_dict["""decoder_attention_mask"""],
								)

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

								UpperCAmelCase :       int 							=		model.init_cache(decoder_input_ids.shape[0]		,			__snake_case		,			__snake_case		)
								UpperCAmelCase :       Any 							=		jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]		,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,			)

								UpperCAmelCase :       List[str] 							=		model.decode(
								    decoder_input_ids[:, :-1]		,			__snake_case		,			decoder_attention_mask=__snake_case		,			past_key_values=__snake_case		,			decoder_position_ids=__snake_case		,			)
								UpperCAmelCase :       Any 							=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,			dtype='''i4'''		)
								UpperCAmelCase :       Optional[Any] 							=		model.decode(
								    decoder_input_ids[:, -1:]		,			__snake_case		,			past_key_values=outputs_cache.past_key_values		,			decoder_attention_mask=__snake_case		,			decoder_position_ids=__snake_case		,			)

								UpperCAmelCase :       str 							=		model.decode(__snake_case		,			__snake_case		,			decoder_attention_mask=__snake_case		)

								UpperCAmelCase :       Union[str, 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}"""		)

@require_flax
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       99







	def      A	(						self			:     str		)			->		Optional[int]:
								UpperCAmelCase :       Dict 							=		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		,			)

								UpperCAmelCase :       Dict 							=		input_ids.shape[0]
								UpperCAmelCase :       Optional[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      A	(						self			:     List[Any]		)			->		Dict:
								UpperCAmelCase :       int 							=		self._get_config_and_data()
								UpperCAmelCase :       Optional[Any] 							=		FlaxBlenderbotForConditionalGeneration(__snake_case		)
								UpperCAmelCase :       List[Any] 							=		lm_model(input_ids=__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape		,			__snake_case		)







	def      A	(						self			:     Any		)			->		int:
								UpperCAmelCase :       Dict 							=		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		,			)
								UpperCAmelCase :       Union[str, Any] 							=		FlaxBlenderbotForConditionalGeneration(__snake_case		)
								UpperCAmelCase :       str 							=		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,			dtype=np.intaa		)
								UpperCAmelCase :       int 							=		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,			dtype=np.intaa		)
								UpperCAmelCase :       Optional[int] 							=		lm_model(input_ids=__snake_case		,			decoder_input_ids=__snake_case		)
								UpperCAmelCase :       Tuple 							=		(*summary.shape, config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape		,			__snake_case		)







	def      A	(						self			:     Any		)			->		List[str]:
								UpperCAmelCase :       Union[str, Any] 							=		np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,			dtype=np.intaa		)
								UpperCAmelCase :       Any 							=		shift_tokens_right(__snake_case		,			1		,			2		)
								UpperCAmelCase :       Optional[Any] 							=		np.equal(__snake_case		,			1		).astype(np.floataa		).sum()
								UpperCAmelCase :       int 							=		np.equal(__snake_case		,			1		).astype(np.floataa		).sum()
								self.assertEqual(shifted.shape		,			input_ids.shape		)
								self.assertEqual(__snake_case		,			n_pad_before - 1		)
								self.assertTrue(np.equal(shifted[:, 0]		,			2		).all()		)

@require_flax
class 				SCREAMING_SNAKE_CASE(							__SCREAMING_SNAKE_CASE							,		unittest.TestCase							,		__SCREAMING_SNAKE_CASE			):







	"""simple docstring"""






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







	def      A	(						self			:     List[str]		)			->		str:
								UpperCAmelCase :       Tuple 							=		FlaxBlenderbotModelTester(self		)







	def      A	(						self			:     Any		)			->		Optional[Any]:
								UpperCAmelCase :       Union[str, Any] 							=		self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward(__snake_case		,			__snake_case		,			__snake_case		)







	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase :       List[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(__snake_case		,			__snake_case		,			__snake_case		)







	def      A	(						self			:     str		)			->		Any:
								UpperCAmelCase :       int 							=		self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__		):
																						UpperCAmelCase :       str 							=		self._prepare_for_class(__snake_case		,			__snake_case		)
																						UpperCAmelCase :       Union[str, Any] 							=		model_class(__snake_case		)

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

																						with self.subTest('''JIT Enabled'''		):
																													UpperCAmelCase :       Union[str, Any] 							=		encode_jitted(**__snake_case		).to_tuple()

																						with self.subTest('''JIT Disabled'''		):
																													with jax.disable_jit():
																																				UpperCAmelCase :       List[str] 							=		encode_jitted(**__snake_case		).to_tuple()

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







	def      A	(						self			:     Optional[int]		)			->		Optional[Any]:
								UpperCAmelCase :       Optional[Any] 							=		self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__		):
																						UpperCAmelCase :       Any 							=		model_class(__snake_case		)
																						UpperCAmelCase :       Dict 							=		model.encode(inputs_dict['''input_ids''']		,			inputs_dict['''attention_mask''']		)

																						UpperCAmelCase :       Optional[int] 							=		{
																						    """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(__snake_case			:     Dict		,			__snake_case			:     int		,			__snake_case			:     str		):
																													return model.decode(
																													    decoder_input_ids=__snake_case		,			decoder_attention_mask=__snake_case		,			encoder_outputs=__snake_case		,			)

																						with self.subTest('''JIT Enabled'''		):
																													UpperCAmelCase :       Union[str, Any] 							=		decode_jitted(**__snake_case		).to_tuple()

																						with self.subTest('''JIT Disabled'''		):
																													with jax.disable_jit():
																																				UpperCAmelCase :       Union[str, Any] 							=		decode_jitted(**__snake_case		).to_tuple()

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







	@slow
	def      A	(						self			:     Tuple		)			->		Tuple:
								for model_class_name in self.all_model_classes:
															UpperCAmelCase :       Union[str, Any] 							=		model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''		)
															# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
															UpperCAmelCase :       Any 							=		np.ones((1, 1)		) * model.config.eos_token_id
															UpperCAmelCase :       Tuple 							=		model(__snake_case		)
															self.assertIsNotNone(__snake_case		)







	@unittest.skipUnless(jax_device != '''cpu'''		,			'''3B test too slow on CPU.'''		)
	@slow
	def      A	(						self			:     List[str]		)			->		Union[str, Any]:
								UpperCAmelCase :       Any 							=		{"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
								UpperCAmelCase :       Tuple 							=		{"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

								UpperCAmelCase :       Dict 							=		FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''		,			from_pt=__snake_case		)
								UpperCAmelCase :       List[Any] 							=		BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''		)

								UpperCAmelCase :       Union[str, Any] 							=		["""Sam"""]
								UpperCAmelCase :       Optional[Any] 							=		tokenizer(__snake_case		,			return_tensors='''jax'''		)

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

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







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Tuple           =			"""camembert"""
      def __init__(      self    ,  __magic_name__=3_0_5_2_2    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=1_2    ,  __magic_name__=3_0_7_2    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_2    ,  __magic_name__=2    ,  __magic_name__=0.02    ,  __magic_name__=1e-12    ,  __magic_name__=1    ,  __magic_name__=0    ,  __magic_name__=2    ,  __magic_name__="absolute"    ,  __magic_name__=True    ,  __magic_name__=None    ,  **__magic_name__    ,  ):
       super().__init__(pad_token_id=__magic_name__    ,  bos_token_id=__magic_name__    ,  eos_token_id=__magic_name__    ,  **__magic_name__      )

       lowerCamelCase		:	int							=      vocab_size
       lowerCamelCase		:	int							=      hidden_size
       lowerCamelCase		:	int							=      num_hidden_layers
       lowerCamelCase		:	int							=      num_attention_heads
       lowerCamelCase		:	Optional[int]							=      hidden_act
       lowerCamelCase		:	List[Any]							=      intermediate_size
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Optional[int]							=      attention_probs_dropout_prob
       lowerCamelCase		:	Optional[int]							=      max_position_embeddings
       lowerCamelCase		:	str							=      type_vocab_size
       lowerCamelCase		:	Optional[Any]							=      initializer_range
       lowerCamelCase		:	int							=      layer_norm_eps
       lowerCamelCase		:	Any							=      position_embedding_type
       lowerCamelCase		:	Optional[int]							=      use_cache
       lowerCamelCase		:	Union[str, Any]							=      classifier_dropout





class 							A__		(	__SCREAMING_SNAKE_CASE):
      @property
      def 						UpperCamelCase__							(      self      ):
       if self.task == "multiple-choice":
        lowerCamelCase		:	List[str]							=      {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        lowerCamelCase		:	List[str]							=      {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]      )




