


"""simple docstring"""




from typing import TYPE_CHECKING

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


A				:   List[Any]		= {"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A				:   Any		= [
        "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "HubertForCTC",
        "HubertForSequenceClassification",
        "HubertModel",
        "HubertPreTrainedModel",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A				:   str		= [
        "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFHubertForCTC",
        "TFHubertModel",
        "TFHubertPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_hubert import (
            HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            HubertForCTC,
            HubertForSequenceClassification,
            HubertModel,
            HubertPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_hubert import (
            TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFHubertForCTC,
            TFHubertModel,
            TFHubertPreTrainedModel,
        )


else:
    import sys

    A				:   Any		= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, 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				:   Optional[Any]		= "platform"

    import jax
    import jax.numpy as jnp

    from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer

def 				lowercase_		(		_A      :   Optional[Any]   , _A      :   str   , _A      :   Union[str, Any]=None   , _A      :   Optional[int]=None						):

      """simple docstring"""




      if attention_mask is None:
            lowerCamelCase__		:    Any   =  np.where(input_ids != config.pad_token_id   , 1   , 0						)
      return {
          "input_ids": input_ids,
          "attention_mask": attention_mask,
      }







@require_flax
class       _lowercase			:


       """simple docstring"""




       def __init__(       self							:      Dict ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      List[str]=13 ,				__lowerCamelCase							:      List[str]=7 ,				__lowerCamelCase							:      Dict=True ,				__lowerCamelCase							:      Optional[int]=False ,				__lowerCamelCase							:      str=99 ,				__lowerCamelCase							:      str=16 ,				__lowerCamelCase							:      List[Any]=2 ,				__lowerCamelCase							:      Optional[int]=4 ,				__lowerCamelCase							:      Tuple=4 ,				__lowerCamelCase							:      str="gelu" ,				__lowerCamelCase							:      List[str]=0.1 ,				__lowerCamelCase							:      str=0.1 ,				__lowerCamelCase							:      List[str]=20 ,				__lowerCamelCase							:      Optional[Any]=2 ,				__lowerCamelCase							:      Optional[Any]=1 ,				__lowerCamelCase							:      int=0 ,				__lowerCamelCase							:      str=16 ,				__lowerCamelCase							:      str=16 ,				__lowerCamelCase							:      int=0.0_2 ,				):


             '''simple docstring'''



             lowerCamelCase__		:    Any   =  parent
             lowerCamelCase__		:    Any   =  batch_size
             lowerCamelCase__		:    Dict   =  seq_length
             lowerCamelCase__		:    Optional[int]   =  is_training
             lowerCamelCase__		:    Any   =  use_labels
             lowerCamelCase__		:    List[Any]   =  vocab_size
             lowerCamelCase__		:    List[Any]   =  hidden_size
             lowerCamelCase__		:    Any   =  num_hidden_layers
             lowerCamelCase__		:    Any   =  num_attention_heads
             lowerCamelCase__		:    str   =  intermediate_size
             lowerCamelCase__		:    Optional[Any]   =  hidden_act
             lowerCamelCase__		:    int   =  hidden_dropout_prob
             lowerCamelCase__		:    Any   =  attention_probs_dropout_prob
             lowerCamelCase__		:    Any   =  max_position_embeddings
             lowerCamelCase__		:    List[Any]   =  eos_token_id
             lowerCamelCase__		:    Union[str, Any]   =  pad_token_id
             lowerCamelCase__		:    Optional[int]   =  bos_token_id
             lowerCamelCase__		:    Optional[Any]   =  embed_dim
             lowerCamelCase__		:    Dict   =  word_embed_proj_dim
             lowerCamelCase__		:    Tuple   =  initializer_range
             lowerCamelCase__		:    List[Any]   =  False





       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,				self.vocab_size	) ,				3 ,				self.vocab_size	)
             lowerCamelCase__		:    Union[str, Any]   =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,				dtype=np.intaa	)) ,				-1	)

             lowerCamelCase__		:    Tuple   =  OPTConfig(
                 vocab_size=self.vocab_size ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				num_attention_heads=self.num_attention_heads ,				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 ,				embed_dim=self.embed_dim ,				is_encoder_decoder=__lowerCamelCase ,				word_embed_proj_dim=self.word_embed_proj_dim ,				initializer_range=self.initializer_range ,				use_cache=__lowerCamelCase ,				)
             lowerCamelCase__		:    str   =  prepare_opt_inputs_dict(__lowerCamelCase ,				__lowerCamelCase	)
             return config, inputs_dict





       def 						lowerCAmelCase					(       self							:      Optional[int]	):


             '''simple docstring'''



             lowerCamelCase__   ,       lowerCamelCase__		:    Union[str, Any]   =  self.prepare_config_and_inputs()
             return config, inputs_dict





       def 						lowerCAmelCase					(       self							:      Optional[int] ,				__lowerCamelCase							:      int ,				__lowerCamelCase							:      List[Any] ,				__lowerCamelCase							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  20
             lowerCamelCase__		:    str   =  model_class_name(__lowerCamelCase	)

             lowerCamelCase__		:    int   =  inputs_dict["input_ids"]
             lowerCamelCase__		:    Dict   =  inputs_dict["attention_mask"]

             lowerCamelCase__		:    int   =  model.init_cache(input_ids.shape[0] ,				__lowerCamelCase	)
             lowerCamelCase__		:    List[Any]   =  jnp.ones((input_ids.shape[0], max_length) ,				dtype="i4"	)

             lowerCamelCase__		:    Optional[Any]   =  jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1	)[None, :] ,				(input_ids.shape[0], input_ids.shape[-1] - 1) ,				)
             lowerCamelCase__		:    Optional[int]   =  model(
                 input_ids[:, :-1] ,				attention_mask=__lowerCamelCase ,				past_key_values=__lowerCamelCase ,				position_ids=__lowerCamelCase ,				)

             lowerCamelCase__		:    Any   =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,				dtype="i4"	)
             lowerCamelCase__		:    Optional[Any]   =  model(
                 input_ids[:, -1:] ,				attention_mask=__lowerCamelCase ,				past_key_values=outputs_cache.past_key_values ,				position_ids=__lowerCamelCase ,				)

             lowerCamelCase__		:    Union[str, Any]   =  model(__lowerCamelCase	)

             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}"	)





       def 						lowerCAmelCase					(       self							:      List[str] ,				__lowerCamelCase							:      List[Any] ,				__lowerCamelCase							:      Optional[Any] ,				__lowerCamelCase							:      Optional[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, Any]   =  20
             lowerCamelCase__		:    Dict   =  model_class_name(__lowerCamelCase	)

             lowerCamelCase__   ,       lowerCamelCase__		:    List[str]   =  (
                 inputs_dict["input_ids"],
                 inputs_dict["attention_mask"],
             )

             lowerCamelCase__		:    Tuple   =  jnp.concatenate(
                 [
                     attention_mask,
                     jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])	),
                 ] ,				axis=-1 ,				)

             lowerCamelCase__		:    List[Any]   =  model.init_cache(input_ids.shape[0] ,				__lowerCamelCase	)
             lowerCamelCase__		:    Dict   =  jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1	)[None, :] ,				(input_ids.shape[0], input_ids.shape[-1] - 1) ,				)

             lowerCamelCase__		:    List[Any]   =  model(
                 input_ids[:, :-1] ,				attention_mask=__lowerCamelCase ,				past_key_values=__lowerCamelCase ,				position_ids=__lowerCamelCase ,				)
             lowerCamelCase__		:    Dict   =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,				dtype="i4"	)
             lowerCamelCase__		:    Optional[Any]   =  model(
                 input_ids[:, -1:] ,				past_key_values=outputs_cache.past_key_values ,				attention_mask=__lowerCamelCase ,				position_ids=__lowerCamelCase ,				)

             lowerCamelCase__		:    int   =  model(__lowerCamelCase ,				attention_mask=__lowerCamelCase	)

             lowerCamelCase__		:    Dict   =  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			(							lowercase__							,  unittest.TestCase							,  lowercase__):


       """simple docstring"""




       A__ 						=      (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
       A__ 						=      () if is_flax_available() else ()





       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  FlaxOPTModelTester(self	)





       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             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(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      str	):


             '''simple docstring'''



             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_with_attn_mask(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase	)





       @slow
       def 						lowerCAmelCase					(       self							:      str	):


             '''simple docstring'''



             for model_class_name in self.all_model_classes:
                   lowerCamelCase__		:    Tuple   =  model_class_name.from_pretrained("facebook/opt-125m"	)
                   lowerCamelCase__		:    Dict   =  np.ones((1, 1)	) * model.config.eos_token_id
                   lowerCamelCase__		:    int   =  model(__lowerCamelCase	)
                   self.assertIsNotNone(__lowerCamelCase	)







@require_sentencepiece
@require_flax
class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 						lowerCAmelCase					(       self							:      Tuple	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  FlaxOPTModel.from_pretrained("facebook/opt-350m"	)
             lowerCamelCase__		:    Optional[int]   =  jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]	)
             lowerCamelCase__		:    int   =  model(input_ids=__lowerCamelCase	).last_hidden_state
             lowerCamelCase__		:    List[Any]   =  (1, 11, 512)
             self.assertEqual(output.shape ,				__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  jnp.array(
                 [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]	)
             self.assertTrue(jnp.allclose(output[:, :3, :3] ,				__lowerCamelCase ,				atol=4E-2	)	)







@require_flax
@slow
class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             super().setUp()
             lowerCamelCase__		:    List[Any]   =  "facebook/opt-350m"





       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    str   =  FlaxOPTForCausalLM.from_pretrained(self.path_model	)
             lowerCamelCase__		:    Optional[Any]   =  GPTaTokenizer.from_pretrained(self.path_model	)

             lowerCamelCase__		:    Tuple   =  [
                 "Today is a beautiful day and I want to",
                 "In the city of",
                 "Paris is the capital of France and",
                 "Computers and mobile phones have taken",
             ]
             # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
             lowerCamelCase__		:    int   =  tokenizer(__lowerCamelCase ,				return_tensors="jax" ,				padding=__lowerCamelCase ,				add_special_tokens=__lowerCamelCase	)
             lowerCamelCase__		:    str   =  model(inputs.input_ids ,				attention_mask=inputs.attention_mask	)[0].mean(axis=-1	)
             lowerCamelCase__		:    Any   =  jnp.array(
                 [
                     [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                     [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
                     [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
                     [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
                 ]	)
             self.assertTrue(jnp.allclose(__lowerCamelCase ,				__lowerCamelCase ,				atol=4E-2	)	)

             lowerCamelCase__		:    List[str]   =  jax.jit(__lowerCamelCase	)
             lowerCamelCase__		:    Optional[int]   =  model(inputs.input_ids ,				attention_mask=inputs.attention_mask	)[0].mean(axis=-1	)
             self.assertTrue(jnp.allclose(__lowerCamelCase ,				__lowerCamelCase ,				atol=4E-2	)	)







@require_flax
@slow
class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       @property
       def 						lowerCAmelCase					(       self							:      Optional[int]	):


             '''simple docstring'''



             return [
                 "Today is a beautiful day and I want",
                 "In the city of",
                 "Paris is the capital of France and",
                 "Computers and mobile phones have taken",
             ]





       def 						lowerCAmelCase					(       self							:      Union[str, Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  "facebook/opt-125m"

             lowerCamelCase__		:    List[str]   =  [
                 "Today is a beautiful day and I want to",
                 "In the city of New York, the city",
                 "Paris is the capital of France and the capital",
                 "Computers and mobile phones have taken over the",
             ]

             lowerCamelCase__		:    str   =  []

             lowerCamelCase__		:    Tuple   =  FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase	)
             lowerCamelCase__		:    List[Any]   =  GPTaTokenizer.from_pretrained(__lowerCamelCase	)

             for prompt in self.prompts:
                   lowerCamelCase__		:    Any   =  tokenizer(__lowerCamelCase ,				return_tensors="jax"	).input_ids

                   lowerCamelCase__		:    int   =  model.generate(__lowerCamelCase ,				max_length=10	)
                   lowerCamelCase__		:    Any   =  generated_ids[0]

                   lowerCamelCase__		:    List[Any]   =  tokenizer.batch_decode(__lowerCamelCase ,				skip_special_tokens=__lowerCamelCase	)
                   predicted_outputs += generated_string

             self.assertListEqual(__lowerCamelCase ,				__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Tuple   =  "facebook/opt-350m"

             lowerCamelCase__		:    Tuple   =  [
                 "Today is a beautiful day and I want to",
                 "In the city of San Francisco, the city",
                 "Paris is the capital of France and the capital",
                 "Computers and mobile phones have taken over the",
             ]

             lowerCamelCase__		:    Optional[int]   =  []
             lowerCamelCase__		:    List[Any]   =  FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase	)
             lowerCamelCase__		:    Any   =  GPTaTokenizer.from_pretrained(__lowerCamelCase	)

             for prompt in self.prompts:
                   lowerCamelCase__		:    Optional[Any]   =  tokenizer(__lowerCamelCase ,				return_tensors="jax"	).input_ids

                   lowerCamelCase__		:    Optional[int]   =  model.generate(__lowerCamelCase ,				max_length=10	)
                   lowerCamelCase__		:    List[str]   =  generated_ids[0]

                   lowerCamelCase__		:    int   =  tokenizer.batch_decode(__lowerCamelCase ,				skip_special_tokens=__lowerCamelCase	)
                   predicted_outputs += generated_string

             self.assertListEqual(__lowerCamelCase ,				__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             lowerCamelCase__		:    List[str]   =  "facebook/opt-125m"
             lowerCamelCase__		:    Tuple   =  [
                 "Today is a beautiful day and I want to thank",
                 "In the city of Rome Canaver Canaver Canaver Canaver",
             ]
             lowerCamelCase__		:    List[str]   =  FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  GPTaTokenizer.from_pretrained(__lowerCamelCase	)
             lowerCamelCase__		:    str   =  tokenizer(
                 [
                     "Today is a beautiful day and I want to",
                     "In the city of",
                 ] ,				return_tensors="jax" ,				padding=__lowerCamelCase ,				)

             lowerCamelCase__		:    str   =  jax.jit(model.generate	)

             lowerCamelCase__		:    Dict   =  jit_generate(inputs["input_ids"] ,				attention_mask=inputs["attention_mask"]	).sequences

             lowerCamelCase__		:    Optional[Any]   =  tokenizer.batch_decode(__lowerCamelCase ,				skip_special_tokens=__lowerCamelCase	)

             self.assertIsNotNone(__lowerCamelCase ,				__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  "facebook/opt-350m"

             lowerCamelCase__		:    Union[str, Any]   =  GPTaTokenizer.from_pretrained(__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase	)

             lowerCamelCase__		:    str   =  "left"

             # use different length sentences to test batching
             lowerCamelCase__		:    Optional[Any]   =  [
                 "Hello, my dog is a little",
                 "Today, I",
             ]

             lowerCamelCase__		:    Tuple   =  tokenizer(__lowerCamelCase ,				return_tensors="jax" ,				padding=__lowerCamelCase	)
             lowerCamelCase__		:    List[Any]   =  inputs["input_ids"]

             lowerCamelCase__		:    Dict   =  model.generate(input_ids=__lowerCamelCase ,				attention_mask=inputs["attention_mask"] ,				trace=__lowerCamelCase	)

             lowerCamelCase__		:    List[Any]   =  tokenizer(sentences[0] ,				return_tensors="jax"	).input_ids
             lowerCamelCase__		:    str   =  model.generate(input_ids=__lowerCamelCase	)

             lowerCamelCase__		:    Dict   =  inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
             lowerCamelCase__		:    Optional[Any]   =  tokenizer(sentences[1] ,				return_tensors="jax"	).input_ids
             lowerCamelCase__		:    Union[str, Any]   =  model.generate(input_ids=__lowerCamelCase ,				max_length=model.config.max_length - num_paddings	)

             lowerCamelCase__		:    str   =  tokenizer.batch_decode(outputs[0] ,				skip_special_tokens=__lowerCamelCase	)
             lowerCamelCase__		:    List[str]   =  tokenizer.decode(output_non_padded[0][0] ,				skip_special_tokens=__lowerCamelCase	)
             lowerCamelCase__		:    int   =  tokenizer.decode(output_padded[0][0] ,				skip_special_tokens=__lowerCamelCase	)

             lowerCamelCase__		:    Dict   =  [
                 "Hello, my dog is a little bit of a dork.\nI'm a little bit",
                 "Today, I was in the middle of a conversation with a friend about the",
             ]
             self.assertListEqual(__lowerCamelCase ,				__lowerCamelCase	)
             self.assertListEqual(__lowerCamelCase ,				[non_padded_sentence, padded_sentence]	)


