





"""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
		A	     =					'''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 (     a_  :str				,    a_  :int				,    a_  :Optional[Any]=None				,    a_  :Optional[Any]=None				,    a_  :List[str]=None				,    a_  :Optional[Any]=None				,    a_  :Tuple=None				,    a_  :List[Any]=None				,    )						->						Dict:
				if attention_mask is None:
								__a	:  Tuple         =					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	:  Optional[Any]         =					np.ones((config.encoder_layers, config.encoder_attention_heads))
				if decoder_head_mask is None:
								__a	:  Optional[Any]         =					np.ones((config.decoder_layers, config.decoder_attention_heads))
				if cross_attn_head_mask is None:
								__a	:  int         =					np.ones((config.decoder_layers, config.decoder_attention_heads))
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": attention_mask,
				}




class 			__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	:  Optional[int]         =					parent
									__a	:  Optional[Any]         =					batch_size
									__a	:  Optional[Any]         =					seq_length
									__a	:  Optional[Any]         =					is_training
									__a	:  Optional[int]         =					use_labels
									__a	:  List[str]         =					vocab_size
									__a	:  List[Any]         =					hidden_size
									__a	:  Dict         =					num_hidden_layers
									__a	:  Dict         =					num_attention_heads
									__a	:  List[Any]         =					intermediate_size
									__a	:  Optional[Any]         =					hidden_act
									__a	:  Any         =					hidden_dropout_prob
									__a	:  Optional[int]         =					attention_probs_dropout_prob
									__a	:  Any         =					max_position_embeddings
									__a	:  Tuple         =					eos_token_id
									__a	:  List[str]         =					pad_token_id
									__a	:  Optional[Any]         =					bos_token_id
									__a	:  Optional[Any]         =					initializer_range







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

									__a	:  Optional[Any]         =					shift_tokens_right(_UpperCAmelCase		,     1		,     2       )

									__a	:  Optional[Any]         =					BlenderbotConfig(
									    vocab_size=self.vocab_size		,     d_model=self.hidden_size		,     encoder_layers=self.num_hidden_layers		,     decoder_layers=self.num_hidden_layers		,     encoder_attention_heads=self.num_attention_heads		,     decoder_attention_heads=self.num_attention_heads		,     encoder_ffn_dim=self.intermediate_size		,     decoder_ffn_dim=self.intermediate_size		,     dropout=self.hidden_dropout_prob		,     attention_dropout=self.attention_probs_dropout_prob		,     max_position_embeddings=self.max_position_embeddings		,     eos_token_id=self.eos_token_id		,     bos_token_id=self.bos_token_id		,     pad_token_id=self.pad_token_id		,     initializer_range=self.initializer_range		,     use_cache=_UpperCAmelCase		,     )
									__a	:  Optional[int]         =					prepare_blenderbot_inputs_dict(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       )
									return config, inputs_dict







					def 	_lowerCamelCase						(  self       ):
									__a    ,  __a	:  Dict         =					self.prepare_config_and_inputs()
									return config, inputs_dict







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  Tuple         =					20
									__a	:  Tuple         =					model_class_name(_UpperCAmelCase       )

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

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

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

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

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

									__a	:  List[str]         =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
									self.parent.assertTrue(diff < 1e-3		,     msg=f"""Max diff is {diff}"""       )







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

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

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

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

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

									__a	:  Dict         =					model.decode(_UpperCAmelCase		,     _UpperCAmelCase		,     decoder_attention_mask=_UpperCAmelCase       )

									__a	:  Optional[Any]         =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
									self.parent.assertTrue(diff < 1e-3		,     msg=f"""Max diff is {diff}"""       )






@require_flax
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					99







					def 	_lowerCamelCase						(  self       ):
									__a	:  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	:  Any         =					input_ids.shape[0]
									__a	:  List[str]         =					BlenderbotConfig(
									    vocab_size=self.vocab_size		,     d_model=24		,     encoder_layers=2		,     decoder_layers=2		,     encoder_attention_heads=2		,     decoder_attention_heads=2		,     encoder_ffn_dim=32		,     decoder_ffn_dim=32		,     max_position_embeddings=48		,     eos_token_id=2		,     pad_token_id=1		,     bos_token_id=0		,     )
									return config, input_ids, batch_size







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







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







					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,     dtype=np.intaa       )
									__a	:  List[Any]         =					shift_tokens_right(_UpperCAmelCase		,     1		,     2       )
									__a	:  List[str]         =					np.equal(_UpperCAmelCase		,     1       ).astype(np.floataa       ).sum()
									__a	:  Tuple         =					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					=					(
					    (
					        FlaxBlenderbotModel,
					        FlaxBlenderbotForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					__lowerCAmelCase					=					(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					FlaxBlenderbotModelTester(self       )







					def 	_lowerCamelCase						(  self       ):
									__a    ,  __a	:  int         =					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	:  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(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       )







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

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

																	__a	:  Any         =					{
																	    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
																	    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
																	    '''encoder_outputs''': encoder_outputs,
																	}

																	@jax.jit
																	def decode_jitted(_UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
																					return model.decode(
																					    decoder_input_ids=_UpperCAmelCase		,     decoder_attention_mask=_UpperCAmelCase		,     encoder_outputs=_UpperCAmelCase		,     )

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

																	with self.subTest('''JIT Disabled'''       ):
																					with jax.disable_jit():
																									__a	:  List[str]         =					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-400M-distill'''       )
													# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
													__a	:  Optional[int]         =					np.ones((1, 1)       ) * model.config.eos_token_id
													__a	:  Optional[Any]         =					model(_UpperCAmelCase       )
													self.assertIsNotNone(_UpperCAmelCase       )







					@unittest.skipUnless(jax_device != '''cpu'''		,     '''3B test too slow on CPU.'''       )
					@slow
					def 	_lowerCamelCase						(  self       ):
									__a	:  Tuple         =					{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
									__a	:  Optional[Any]         =					{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

									__a	:  str         =					FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''		,     from_pt=_UpperCAmelCase       )
									__a	:  Union[str, Any]         =					BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''       )

									__a	:  Tuple         =					['''Sam''']
									__a	:  str         =					tokenizer(_UpperCAmelCase		,     return_tensors='''jax'''       )

									__a	:  Tuple         =					model.generate(**_UpperCAmelCase		,     **_UpperCAmelCase       )
									__a	:  List[Any]         =					'''Sam is a great name. It means "sun" in Gaelic.'''

									__a	:  List[str]         =					tokenizer.batch_decode(_UpperCAmelCase		,     **_UpperCAmelCase       )
									assert generated_txt[0].strip() == tgt_text






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A	     =					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					['''MvpTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					[
		    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MvpForCausalLM''',
		    '''MvpForConditionalGeneration''',
		    '''MvpForQuestionAnswering''',
		    '''MvpForSequenceClassification''',
		    '''MvpModel''',
		    '''MvpPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
		from .tokenization_mvp import MvpTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mvp_fast import MvpTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mvp import (
				    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MvpForCausalLM,
				    MvpForConditionalGeneration,
				    MvpForQuestionAnswering,
				    MvpForSequenceClassification,
				    MvpModel,
				    MvpPreTrainedModel,
				)

else:
		import sys

		A	     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)