


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
						snake_case							: List[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 							__lowercase     (				__lowerCAmelCase							:       List[str]      ,					__lowerCAmelCase							:       Any      ,					__lowerCAmelCase							:       Union[str, Any]=None      ,					__lowerCAmelCase							:       Tuple=None      ,					__lowerCAmelCase							:       Union[str, Any]=None      ,					__lowerCAmelCase							:       List[str]=None      ,					__lowerCAmelCase							:       str=None      ,					__lowerCAmelCase							:       Dict=None      ,					):




							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  snake_case_     :
					def __init__(   self     :List[Any]    ,__snake_case     :List[Any]    ,__snake_case     :Union[str, Any]=13    ,__snake_case     :Optional[int]=7    ,__snake_case     :str=True    ,__snake_case     :List[Any]=False    ,__snake_case     :Dict=99    ,__snake_case     :List[Any]=16    ,__snake_case     :str=2    ,__snake_case     :Union[str, Any]=4    ,__snake_case     :str=4    ,__snake_case     :int="gelu"    ,__snake_case     :List[Any]=0.1    ,__snake_case     :Optional[Any]=0.1    ,__snake_case     :Union[str, Any]=32    ,__snake_case     :Optional[Any]=2    ,__snake_case     :Any=1    ,__snake_case     :List[Any]=0    ,__snake_case     :Any=0.02    ,)      ->					Union[str, Any]:
												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 				lowerCamelCase__(   self     :List[str]   )      ->					Tuple:
												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(__lowerCamelCase    ,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=__lowerCamelCase    ,)
												a__    =  prepare_blenderbot_inputs_dict(__lowerCamelCase    ,__lowerCamelCase    ,__lowerCamelCase   )
												return config, inputs_dict
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					Optional[int]:
												a__    =  self.prepare_config_and_inputs()
												return config, inputs_dict
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Tuple    ,__snake_case     :Tuple    ,__snake_case     :Tuple   )      ->					Union[str, Any]:
												a__    =  20
												a__    =  model_class_name(__lowerCamelCase   )

												a__    =  model.encode(inputs_dict['input_ids']   )

												a__    =  (
												    inputs_dict["decoder_input_ids"],
												    inputs_dict["decoder_attention_mask"],
												)

												a__    =  model.init_cache(decoder_input_ids.shape[0]    ,__lowerCamelCase    ,__lowerCamelCase   )
												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]    ,__lowerCamelCase    ,decoder_attention_mask=__lowerCamelCase    ,past_key_values=__lowerCamelCase    ,decoder_position_ids=__lowerCamelCase    ,)

												a__    =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,dtype='i4'   )
												a__    =  model.decode(
												    decoder_input_ids[:, -1:]    ,__lowerCamelCase    ,decoder_attention_mask=__lowerCamelCase    ,past_key_values=outputs_cache.past_key_values    ,decoder_position_ids=__lowerCamelCase    ,)

												a__    =  model.decode(__lowerCamelCase    ,__lowerCamelCase   )

												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 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :Optional[int]    ,__snake_case     :Any    ,__snake_case     :Optional[Any]   )      ->					Tuple:
												a__    =  20
												a__    =  model_class_name(__lowerCamelCase   )

												a__    =  model.encode(inputs_dict['input_ids']   )

												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]    ,__lowerCamelCase    ,__lowerCamelCase   )
												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]    ,__lowerCamelCase    ,decoder_attention_mask=__lowerCamelCase    ,past_key_values=__lowerCamelCase    ,decoder_position_ids=__lowerCamelCase    ,)
												a__    =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,dtype='i4'   )
												a__    =  model.decode(
												    decoder_input_ids[:, -1:]    ,__lowerCamelCase    ,past_key_values=outputs_cache.past_key_values    ,decoder_attention_mask=__lowerCamelCase    ,decoder_position_ids=__lowerCamelCase    ,)

												a__    =  model.decode(__lowerCamelCase    ,__lowerCamelCase    ,decoder_attention_mask=__lowerCamelCase   )

												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  snake_case_     (unittest.TestCase   ):
					UpperCAmelCase__      :					Tuple         =    9_9
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					List[str]:
												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 				lowerCamelCase__(   self     :Tuple   )      ->					Union[str, Any]:
												a__    =  self._get_config_and_data()
												a__    =  FlaxBlenderbotForConditionalGeneration(__lowerCamelCase   )
												a__    =  lm_model(input_ids=__lowerCamelCase   )
												a__    =  (batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs['logits'].shape    ,__lowerCamelCase   )
					def 				lowerCamelCase__(   self     :Tuple   )      ->					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(__lowerCamelCase   )
												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=__lowerCamelCase    ,decoder_input_ids=__lowerCamelCase   )
												a__    =  (*summary.shape, config.vocab_size)
												self.assertEqual(outputs['logits'].shape    ,__lowerCamelCase   )



					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					Any:
												a__    =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,dtype=np.intaa   )
												a__    =  shift_tokens_right(__lowerCamelCase    ,1    ,2   )
												a__    =  np.equal(__lowerCamelCase    ,1   ).astype(np.floataa   ).sum()
												a__    =  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  snake_case_     (lowercase__			,		unittest.TestCase			,		lowercase__   ):
					UpperCAmelCase__      :					List[Any]         =    True
					UpperCAmelCase__      :					Optional[Any]         =    (
					    (
					        FlaxBlenderbotModel,
					        FlaxBlenderbotForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					UpperCAmelCase__      :					Optional[Any]         =    (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Any:
												a__    =  FlaxBlenderbotModelTester(self   )
					def 				lowerCamelCase__(   self     :Dict   )      ->					List[str]:
												a__    =  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 				lowerCamelCase__(   self     :List[Any]   )      ->					Tuple:
												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(__lowerCamelCase    ,__lowerCamelCase    ,__lowerCamelCase   )
					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					int:
												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(__lowerCamelCase    ,__lowerCamelCase   )
																										a__    =  model_class(__lowerCamelCase   )

																										@jax.jit
																										def encode_jitted(__snake_case     :List[str]    ,__snake_case     :Tuple=None    ,**__snake_case     :int   ):
																																	return model.encode(input_ids=__lowerCamelCase    ,attention_mask=__lowerCamelCase   )

																										with self.subTest('JIT Enabled'   ):
																																	a__    =  encode_jitted(**__lowerCamelCase   ).to_tuple()

																										with self.subTest('JIT Disabled'   ):
																																	with jax.disable_jit():
																																								a__    =  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 				lowerCamelCase__(   self     :Optional[int]   )      ->					Optional[int]:
												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(__lowerCamelCase   )
																										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(__snake_case     :Dict    ,__snake_case     :Any    ,__snake_case     :str   ):
																																	return model.decode(
																																	    decoder_input_ids=__lowerCamelCase    ,decoder_attention_mask=__lowerCamelCase    ,encoder_outputs=__lowerCamelCase    ,)

																										with self.subTest('JIT Enabled'   ):
																																	a__    =  decode_jitted(**__lowerCamelCase   ).to_tuple()

																										with self.subTest('JIT Disabled'   ):
																																	with jax.disable_jit():
																																								a__    =  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 				lowerCamelCase__(   self     :Any   )      ->					Optional[int]:



												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(__lowerCamelCase   )
																			self.assertIsNotNone(__lowerCamelCase   )



					@unittest.skipUnless(jax_device != 'cpu'    ,'3B test too slow on CPU.'   )
					@slow
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					List[Any]:
												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=__lowerCamelCase   )
												a__    =  BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'   )

												a__    =  ["Sam"]
												a__    =  tokenizer(__lowerCamelCase    ,return_tensors='jax'   )

												a__    =  model.generate(**__lowerCamelCase    ,**__lowerCamelCase   )
												a__    =  "Sam is a great name. It means \"sun\" in Gaelic."

												a__    =  tokenizer.batch_decode(__lowerCamelCase    ,**__lowerCamelCase   )
												assert generated_txt[0].strip() == tgt_text








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


snake_case							: Any      =   {
    '''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Union[str, Any]      =   ['''MobileViTFeatureExtractor''']
						snake_case							: int      =   ['''MobileViTImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Dict      =   [
						    '''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MobileViTForImageClassification''',
						    '''MobileViTForSemanticSegmentation''',
						    '''MobileViTModel''',
						    '''MobileViTPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Tuple      =   [
						    '''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFMobileViTForImageClassification''',
						    '''TFMobileViTForSemanticSegmentation''',
						    '''TFMobileViTModel''',
						    '''TFMobileViTPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilevit import MobileViTFeatureExtractor
												from .image_processing_mobilevit import MobileViTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilevit import (
												    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileViTForImageClassification,
												    MobileViTForSemanticSegmentation,
												    MobileViTModel,
												    MobileViTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilevit import (
												    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileViTForImageClassification,
												    TFMobileViTForSemanticSegmentation,
												    TFMobileViTModel,
												    TFMobileViTPreTrainedModel,
												)


else:
						import sys

						snake_case							: Optional[int]      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




