






"""simple docstring"""



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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__       :				Tuple 					=					"""platform"""

						import jax
						import jax.numpy as jnp

						from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    shift_tokens_right,
						)

def  _snake_case				(     lowerCamelCase__	:    Dict				,						lowerCamelCase__	:    Any				,						lowerCamelCase__	:    List[str]=None				,						lowerCamelCase__	:    Dict=None				,						lowerCamelCase__	:    str=None				,						lowerCamelCase__	:    Tuple=None				,						lowerCamelCase__	:    Tuple=None				,						lowerCamelCase__	:    Union[str, Any]=None				,						)				->	Optional[Any]:

							if attention_mask is None:
														lowerCamelCase_       :							List[str]									=np.where(input_ids != config.pad_token_id				,						1				,						0  )
							if decoder_attention_mask is None:
														lowerCamelCase_       :							List[Any]									=np.where(decoder_input_ids != config.pad_token_id				,						1				,						0  )
							if head_mask is None:
														lowerCamelCase_       :							List[Any]									=np.ones((config.encoder_layers, config.encoder_attention_heads)  )
							if decoder_head_mask is None:
														lowerCamelCase_       :							int									=np.ones((config.decoder_layers, config.decoder_attention_heads)  )
							if cross_attn_head_mask is None:
														lowerCamelCase_       :							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    lowercase__			:


			def __init__(      self     :  Tuple   ,       snake_case__     :  str   ,       snake_case__     :  Optional[int]=13   ,       snake_case__     :  str=7   ,       snake_case__     :  List[str]=True   ,       snake_case__     :  Optional[int]=False   ,       snake_case__     :  List[Any]=99   ,       snake_case__     :  Tuple=16   ,       snake_case__     :  Optional[Any]=2   ,       snake_case__     :  Tuple=4   ,       snake_case__     :  Union[str, Any]=4   ,       snake_case__     :  Optional[int]="gelu"   ,       snake_case__     :  int=0.1   ,       snake_case__     :  Tuple=0.1   ,       snake_case__     :  Any=32   ,       snake_case__     :  Any=2   ,       snake_case__     :  Any=1   ,       snake_case__     :  Tuple=0   ,       snake_case__     :  Dict=0.02   ,       ):
										lowerCamelCase_       :							int									=parent
										lowerCamelCase_       :							Any									=batch_size
										lowerCamelCase_       :							str									=seq_length
										lowerCamelCase_       :							List[str]									=is_training
										lowerCamelCase_       :							List[Any]									=use_labels
										lowerCamelCase_       :							str									=vocab_size
										lowerCamelCase_       :							Optional[int]									=hidden_size
										lowerCamelCase_       :							int									=num_hidden_layers
										lowerCamelCase_       :							int									=num_attention_heads
										lowerCamelCase_       :							Union[str, Any]									=intermediate_size
										lowerCamelCase_       :							Any									=hidden_act
										lowerCamelCase_       :							Dict									=hidden_dropout_prob
										lowerCamelCase_       :							int									=attention_probs_dropout_prob
										lowerCamelCase_       :							Tuple									=max_position_embeddings
										lowerCamelCase_       :							Optional[Any]									=eos_token_id
										lowerCamelCase_       :							Any									=pad_token_id
										lowerCamelCase_       :							str									=bos_token_id
										lowerCamelCase_       :							Union[str, Any]									=initializer_range


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

										lowerCamelCase_       :							Union[str, Any]									=shift_tokens_right(UpperCAmelCase__   ,       1   ,       2					)

										lowerCamelCase_       :							Optional[int]									=BlenderbotSmallConfig(
										    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__   ,       )
										lowerCamelCase_       :							Optional[int]									=prepare_blenderbot_inputs_dict(UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__					)
										return config, inputs_dict


			def 	UpperCAmelCase__						(      self     :  Optional[int]					):
										lowerCamelCase_							,						lowerCamelCase_       :							Dict									=self.prepare_config_and_inputs()
										return config, inputs_dict


			def 	UpperCAmelCase__						(      self     :  Tuple   ,       snake_case__     :  List[str]   ,       snake_case__     :  List[Any]   ,       snake_case__     :  Union[str, Any]					):
										lowerCamelCase_       :							Dict									=20
										lowerCamelCase_       :							Tuple									=model_class_name(UpperCAmelCase__					)

										lowerCamelCase_       :							int									=model.encode(inputs_dict["input_ids"]					)

										lowerCamelCase_							,						lowerCamelCase_       :							Dict									=(
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

										lowerCamelCase_       :							Optional[Any]									=model.init_cache(decoder_input_ids.shape[0]   ,       UpperCAmelCase__   ,       UpperCAmelCase__					)
										lowerCamelCase_       :							Tuple									=jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,       dtype="i4"					)

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

										lowerCamelCase_       :							Union[str, Any]									=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,       dtype="i4"					)
										lowerCamelCase_       :							List[str]									=model.decode(
										    decoder_input_ids[:, -1:]   ,       UpperCAmelCase__   ,       decoder_attention_mask=UpperCAmelCase__   ,       past_key_values=outputs_cache.past_key_values   ,       decoder_position_ids=UpperCAmelCase__   ,       )

										lowerCamelCase_       :							List[str]									=model.decode(UpperCAmelCase__   ,       UpperCAmelCase__					)

										lowerCamelCase_       :							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 	UpperCAmelCase__						(      self     :  List[str]   ,       snake_case__     :  Union[str, Any]   ,       snake_case__     :  str   ,       snake_case__     :  Union[str, Any]					):
										lowerCamelCase_       :							Any									=20
										lowerCamelCase_       :							Dict									=model_class_name(UpperCAmelCase__					)

										lowerCamelCase_       :							Optional[int]									=model.encode(inputs_dict["input_ids"]					)

										lowerCamelCase_							,						lowerCamelCase_       :							Optional[Any]									=(
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

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

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

										lowerCamelCase_       :							Dict									=model.decode(
										    decoder_input_ids[:, :-1]   ,       UpperCAmelCase__   ,       decoder_attention_mask=UpperCAmelCase__   ,       past_key_values=UpperCAmelCase__   ,       decoder_position_ids=UpperCAmelCase__   ,       )
										lowerCamelCase_       :							Dict									=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,       dtype="i4"					)
										lowerCamelCase_       :							Tuple									=model.decode(
										    decoder_input_ids[:, -1:]   ,       UpperCAmelCase__   ,       past_key_values=outputs_cache.past_key_values   ,       decoder_attention_mask=UpperCAmelCase__   ,       decoder_position_ids=UpperCAmelCase__   ,       )

										lowerCamelCase_       :							int									=model.decode(UpperCAmelCase__   ,       UpperCAmelCase__   ,       decoder_attention_mask=UpperCAmelCase__					)

										lowerCamelCase_       :							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}"""					)







@require_flax
class    lowercase__			(							unittest.TestCase   ):
			_UpperCAmelCase		:List[Any]   			= 99


			def 	UpperCAmelCase__						(      self     :  List[str]					):
										lowerCamelCase_       :							str									=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   ,       )

										lowerCamelCase_       :							List[str]									=input_ids.shape[0]
										lowerCamelCase_       :							Optional[int]									=BlenderbotSmallConfig(
										    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]					):
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_       :							Any									=self._get_config_and_data()
										lowerCamelCase_       :							str									=FlaxBlenderbotSmallForConditionalGeneration(UpperCAmelCase__					)
										lowerCamelCase_       :							Optional[Any]									=lm_model(input_ids=UpperCAmelCase__					)
										lowerCamelCase_       :							List[Any]									=(batch_size, input_ids.shape[1], config.vocab_size)
										self.assertEqual(outputs["logits"].shape   ,       UpperCAmelCase__					)


			def 	UpperCAmelCase__						(      self     :  Optional[int]					):
										lowerCamelCase_       :							Optional[int]									=BlenderbotSmallConfig(
										    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   ,       )
										lowerCamelCase_       :							int									=FlaxBlenderbotSmallForConditionalGeneration(UpperCAmelCase__					)
										lowerCamelCase_       :							List[Any]									=np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]   ,       dtype=np.intaa					)
										lowerCamelCase_       :							Tuple									=np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]   ,       dtype=np.intaa					)
										lowerCamelCase_       :							Union[str, Any]									=lm_model(input_ids=UpperCAmelCase__   ,       decoder_input_ids=UpperCAmelCase__					)
										lowerCamelCase_       :							Union[str, Any]									=(*summary.shape, config.vocab_size)
										self.assertEqual(outputs["logits"].shape   ,       UpperCAmelCase__					)


			def 	UpperCAmelCase__						(      self     :  List[str]					):
										lowerCamelCase_       :							List[str]									=np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]   ,       dtype=np.intaa					)
										lowerCamelCase_       :							Optional[Any]									=shift_tokens_right(UpperCAmelCase__   ,       1   ,       2					)
										lowerCamelCase_       :							List[str]									=np.equal(UpperCAmelCase__   ,       1					).astype(np.floataa					).sum()
										lowerCamelCase_       :							Any									=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__   ):
			_UpperCAmelCase		:Optional[int]   			= True
			_UpperCAmelCase		:List[str]   			= (
			    (
			        FlaxBlenderbotSmallModel,
			        FlaxBlenderbotSmallForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			_UpperCAmelCase		:List[Any]   			= (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


			def 	UpperCAmelCase__						(      self     :  Dict					):
										lowerCamelCase_       :							Tuple									=FlaxBlenderbotSmallModelTester(self					)


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										lowerCamelCase_							,						lowerCamelCase_       :							Tuple									=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 	UpperCAmelCase__						(      self     :  Tuple					):
										lowerCamelCase_							,						lowerCamelCase_       :							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_with_attn_mask(UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__					)


			def 	UpperCAmelCase__						(      self     :  Dict					):
										lowerCamelCase_							,						lowerCamelCase_       :							Dict									=self.model_tester.prepare_config_and_inputs_for_common()

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

																								@jax.jit
																								def encode_jitted(snake_case__     :  Tuple   ,       snake_case__     :  List[Any]=None   ,       **snake_case__     :  str					):
																															return model.encode(input_ids=UpperCAmelCase__   ,       attention_mask=UpperCAmelCase__					)

																								with self.subTest("JIT Enabled"					):
																															lowerCamelCase_       :							Optional[int]									=encode_jitted(**UpperCAmelCase__					).to_tuple()

																								with self.subTest("JIT Disabled"					):
																															with jax.disable_jit():
																																						lowerCamelCase_       :							str									=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 	UpperCAmelCase__						(      self     :  Optional[int]					):
										lowerCamelCase_							,						lowerCamelCase_       :							Optional[int]									=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__					):
																								lowerCamelCase_       :							Optional[int]									=model_class(UpperCAmelCase__					)
																								lowerCamelCase_       :							Union[str, Any]									=model.encode(inputs_dict["input_ids"]   ,       inputs_dict["attention_mask"]					)

																								lowerCamelCase_       :							Optional[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(snake_case__     :  int   ,       snake_case__     :  Any   ,       snake_case__     :  Any					):
																															return model.decode(
																															    decoder_input_ids=UpperCAmelCase__   ,       decoder_attention_mask=UpperCAmelCase__   ,       encoder_outputs=UpperCAmelCase__   ,       )

																								with self.subTest("JIT Enabled"					):
																															lowerCamelCase_       :							int									=decode_jitted(**UpperCAmelCase__					).to_tuple()

																								with self.subTest("JIT Disabled"					):
																															with jax.disable_jit():
																																						lowerCamelCase_       :							Union[str, Any]									=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 	UpperCAmelCase__						(      self     :  Any					):

										for model_class_name in self.all_model_classes:
																	lowerCamelCase_       :							Dict									=model_class_name.from_pretrained("facebook/blenderbot_small-90M"					)
																	# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																	lowerCamelCase_       :							List[Any]									=np.ones((1, 1)					) * model.config.eos_token_id
																	lowerCamelCase_       :							Optional[int]									=model(UpperCAmelCase__					)
																	self.assertIsNotNone(UpperCAmelCase__					)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase	:		List[str]							       =   {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		List[Any]							       =   [
      """VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ViTMSNModel""",
      """ViTMSNForImageClassification""",
      """ViTMSNPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase	:		Tuple							       =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


