
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
 __lowerCAmelCase   :     Optional[int]      =						"platform"

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase=None		,   __lowerCAmelCase=None		)	->  str:
    if attention_mask is None:
        __lowercase						:     str		      =    np.where(input_ids != config.pad_token_id		,   1		,   0		)
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
    }


@require_flax
class   __lowerCAmelCase		:



      """simple docstring"""







      def __init__( self						:    Optional[int]			,    _snake_case						:    List[Any]			,    _snake_case						:    str=13			,    _snake_case						:    int=7			,    _snake_case						:    Dict=True			,    _snake_case						:    List[Any]=False			,    _snake_case						:    Union[str, Any]=99			,    _snake_case						:    Union[str, Any]=16			,    _snake_case						:    Tuple=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						:    List[Any]=20			,    _snake_case						:    int=2			,    _snake_case						:    str=1			,    _snake_case						:    List[str]=0			,    _snake_case						:    Optional[Any]=16			,    _snake_case						:    Any=16			,    _snake_case						:    Optional[Any]=0.02			,    ):
          __lowercase						:     Any		      =    parent
          __lowercase						:     Union[str, Any]		      =    batch_size
          __lowercase						:     List[str]		      =    seq_length
          __lowercase						:     int		      =    is_training
          __lowercase						:     Optional[Any]		      =    use_labels
          __lowercase						:     Optional[Any]		      =    vocab_size
          __lowercase						:     Union[str, Any]		      =    hidden_size
          __lowercase						:     str		      =    num_hidden_layers
          __lowercase						:     int		      =    num_attention_heads
          __lowercase						:     Union[str, Any]		      =    intermediate_size
          __lowercase						:     Tuple		      =    hidden_act
          __lowercase						:     Optional[Any]		      =    hidden_dropout_prob
          __lowercase						:     Dict		      =    attention_probs_dropout_prob
          __lowercase						:     List[Any]		      =    max_position_embeddings
          __lowercase						:     List[Any]		      =    eos_token_id
          __lowercase						:     List[str]		      =    pad_token_id
          __lowercase						:     List[str]		      =    bos_token_id
          __lowercase						:     Dict		      =    embed_dim
          __lowercase						:     Optional[int]		      =    word_embed_proj_dim
          __lowercase						:     Tuple		      =    initializer_range
          __lowercase						:     List[Any]		      =    False




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

          __lowercase						:     List[str]		      =    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=_snake_case			,    word_embed_proj_dim=self.word_embed_proj_dim			,    initializer_range=self.initializer_range			,    use_cache=_snake_case			,    )
          __lowercase						:     List[Any]		      =    prepare_opt_inputs_dict(_snake_case			,    _snake_case			)
          return config, inputs_dict




      def 						snake_case_     ( self						:    Dict			):
          __lowercase      , __lowercase						:     str		      =    self.prepare_config_and_inputs()
          return config, inputs_dict




      def 						snake_case_     ( self						:    Tuple			,    _snake_case						:    Dict			,    _snake_case						:    List[Any]			,    _snake_case						:    int			):
          __lowercase						:     Optional[int]		      =    20
          __lowercase						:     str		      =    model_class_name(_snake_case			)

          __lowercase						:     List[Any]		      =    inputs_dict['''input_ids''']
          __lowercase						:     List[str]		      =    inputs_dict['''attention_mask''']

          __lowercase						:     List[str]		      =    model.init_cache(input_ids.shape[0]			,    _snake_case			)
          __lowercase						:     Tuple		      =    jnp.ones((input_ids.shape[0], max_length)			,    dtype='''i4'''			)

          __lowercase						:     Tuple		      =    jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1			)[None, :]			,    (input_ids.shape[0], input_ids.shape[-1] - 1)			,    )
          __lowercase						:     Dict		      =    model(
              input_ids[:, :-1]			,    attention_mask=_snake_case			,    past_key_values=_snake_case			,    position_ids=_snake_case			,    )

          __lowercase						:     str		      =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,    dtype='''i4'''			)
          __lowercase						:     Dict		      =    model(
              input_ids[:, -1:]			,    attention_mask=_snake_case			,    past_key_values=outputs_cache.past_key_values			,    position_ids=_snake_case			,    )

          __lowercase						:     int		      =    model(_snake_case			)

          __lowercase						:     int		      =    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 						snake_case_     ( self						:    Any			,    _snake_case						:    Tuple			,    _snake_case						:    Union[str, Any]			,    _snake_case						:    List[Any]			):
          __lowercase						:     Any		      =    20
          __lowercase						:     Optional[int]		      =    model_class_name(_snake_case			)

          __lowercase      , __lowercase						:     Tuple		      =    (
              inputs_dict['''input_ids'''],
              inputs_dict['''attention_mask'''],
          )

          __lowercase						:     List[str]		      =    jnp.concatenate(
              [
                  attention_mask,
                  jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])			),
              ]			,    axis=-1			,    )

          __lowercase						:     Any		      =    model.init_cache(input_ids.shape[0]			,    _snake_case			)
          __lowercase						:     int		      =    jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1			)[None, :]			,    (input_ids.shape[0], input_ids.shape[-1] - 1)			,    )

          __lowercase						:     int		      =    model(
              input_ids[:, :-1]			,    attention_mask=_snake_case			,    past_key_values=_snake_case			,    position_ids=_snake_case			,    )
          __lowercase						:     Optional[Any]		      =    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,    dtype='''i4'''			)
          __lowercase						:     Optional[Any]		      =    model(
              input_ids[:, -1:]			,    past_key_values=outputs_cache.past_key_values			,    attention_mask=_snake_case			,    position_ids=_snake_case			,    )

          __lowercase						:     Optional[int]		      =    model(_snake_case			,    attention_mask=_snake_case			)

          __lowercase						:     int		      =    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   __lowerCAmelCase		(		lowerCAmelCase_	,	unittest.TestCase	,	lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     List[str]       				=      (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      A__		:     Optional[int]       				=      () if is_flax_available() else ()




      def 						snake_case_     ( self						:    List[Any]			):
          __lowercase						:     List[Any]		      =    FlaxOPTModelTester(self			)




      def 						snake_case_     ( self						:    Any			):
          __lowercase      , __lowercase						:     Optional[int]		      =    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 						snake_case_     ( self						:    Union[str, Any]			):
          __lowercase      , __lowercase						:     Dict		      =    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			)




      @slow
      def 						snake_case_     ( self						:    Tuple			):
          for model_class_name in self.all_model_classes:
              __lowercase						:     Tuple		      =    model_class_name.from_pretrained('''facebook/opt-125m'''			)
              __lowercase						:     Dict		      =    np.ones((1, 1)			) * model.config.eos_token_id
              __lowercase						:     Optional[int]		      =    model(_snake_case			)
              self.assertIsNotNone(_snake_case			)






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



      """simple docstring"""







      @slow
      def 						snake_case_     ( self						:    int			):
          __lowercase						:     List[Any]		      =    FlaxOPTModel.from_pretrained('''facebook/opt-350m'''			)
          __lowercase						:     Optional[Any]		      =    jnp.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]			)
          __lowercase						:     Union[str, Any]		      =    model(input_ids=_snake_case			).last_hidden_state
          __lowercase						:     List[Any]		      =    (1, 11, 512)
          self.assertEqual(output.shape			,    _snake_case			)
          __lowercase						:     Any		      =    jnp.array(
              [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]			)
          self.assertTrue(jnp.allclose(output[:, :3, :3]			,    _snake_case			,    atol=4E-2			)			)






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



      """simple docstring"""







      def 						snake_case_     ( self						:    str			):
          super().setUp()
          __lowercase						:     List[str]		      =    '''facebook/opt-350m'''




      def 						snake_case_     ( self						:    List[Any]			):
          __lowercase						:     List[Any]		      =    FlaxOPTForCausalLM.from_pretrained(self.path_model			)
          __lowercase						:     Union[str, Any]		      =    GPTaTokenizer.from_pretrained(self.path_model			)

          __lowercase						:     List[Any]		      =    [
              '''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
          __lowercase						:     Dict		      =    tokenizer(_snake_case			,    return_tensors='''jax'''			,    padding=_snake_case			,    add_special_tokens=_snake_case			)
          __lowercase						:     str		      =    model(inputs.input_ids			,    attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
          __lowercase						:     int		      =    jnp.array(
              [
                  [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
                  [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
                  [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
                  [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
              ]			)
          self.assertTrue(jnp.allclose(_snake_case			,    _snake_case			,    atol=4E-2			)			)

          __lowercase						:     Any		      =    jax.jit(_snake_case			)
          __lowercase						:     List[Any]		      =    model(inputs.input_ids			,    attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
          self.assertTrue(jnp.allclose(_snake_case			,    _snake_case			,    atol=4E-2			)			)






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



      """simple docstring"""







      @property
      def 						snake_case_     ( self						:    Optional[Any]			):
          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 						snake_case_     ( self						:    List[str]			):
          __lowercase						:     List[Any]		      =    '''facebook/opt-125m'''

          __lowercase						:     Union[str, Any]		      =    [
              '''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''',
          ]

          __lowercase						:     Union[str, Any]		      =    []

          __lowercase						:     str		      =    FlaxOPTForCausalLM.from_pretrained(_snake_case			)
          __lowercase						:     Optional[Any]		      =    GPTaTokenizer.from_pretrained(_snake_case			)

          for prompt in self.prompts:
              __lowercase						:     Tuple		      =    tokenizer(_snake_case			,    return_tensors='''jax'''			).input_ids

              __lowercase						:     str		      =    model.generate(_snake_case			,    max_length=10			)
              __lowercase						:     List[Any]		      =    generated_ids[0]

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

          self.assertListEqual(_snake_case			,    _snake_case			)




      def 						snake_case_     ( self						:    Any			):
          __lowercase						:     List[str]		      =    '''facebook/opt-350m'''

          __lowercase						:     List[str]		      =    [
              '''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''',
          ]

          __lowercase						:     int		      =    []
          __lowercase						:     Dict		      =    FlaxOPTForCausalLM.from_pretrained(_snake_case			)
          __lowercase						:     List[str]		      =    GPTaTokenizer.from_pretrained(_snake_case			)

          for prompt in self.prompts:
              __lowercase						:     Optional[int]		      =    tokenizer(_snake_case			,    return_tensors='''jax'''			).input_ids

              __lowercase						:     List[str]		      =    model.generate(_snake_case			,    max_length=10			)
              __lowercase						:     Optional[Any]		      =    generated_ids[0]

              __lowercase						:     int		      =    tokenizer.batch_decode(_snake_case			,    skip_special_tokens=_snake_case			)
              predicted_outputs += generated_string

          self.assertListEqual(_snake_case			,    _snake_case			)




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

          __lowercase						:     int		      =    jax.jit(model.generate			)

          __lowercase						:     int		      =    jit_generate(inputs['''input_ids''']			,    attention_mask=inputs['''attention_mask''']			).sequences

          __lowercase						:     List[Any]		      =    tokenizer.batch_decode(_snake_case			,    skip_special_tokens=_snake_case			)

          self.assertIsNotNone(_snake_case			,    _snake_case			)




      def 						snake_case_     ( self						:    List[Any]			):
          __lowercase						:     List[Any]		      =    '''facebook/opt-350m'''

          __lowercase						:     int		      =    GPTaTokenizer.from_pretrained(_snake_case			)
          __lowercase						:     Optional[int]		      =    FlaxOPTForCausalLM.from_pretrained(_snake_case			)

          __lowercase						:     int		      =    '''left'''

          # use different length sentences to test batching
          __lowercase						:     List[str]		      =    [
              '''Hello, my dog is a little''',
              '''Today, I''',
          ]

          __lowercase						:     Dict		      =    tokenizer(_snake_case			,    return_tensors='''jax'''			,    padding=_snake_case			)
          __lowercase						:     Dict		      =    inputs['''input_ids''']

          __lowercase						:     Optional[Any]		      =    model.generate(input_ids=_snake_case			,    attention_mask=inputs['''attention_mask''']			,    trace=_snake_case			)

          __lowercase						:     str		      =    tokenizer(sentences[0]			,    return_tensors='''jax'''			).input_ids
          __lowercase						:     int		      =    model.generate(input_ids=_snake_case			)

          __lowercase						:     Any		      =    inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
          __lowercase						:     Any		      =    tokenizer(sentences[1]			,    return_tensors='''jax'''			).input_ids
          __lowercase						:     str		      =    model.generate(input_ids=_snake_case			,    max_length=model.config.max_length - num_paddings			)

          __lowercase						:     int		      =    tokenizer.batch_decode(outputs[0]			,    skip_special_tokens=_snake_case			)
          __lowercase						:     str		      =    tokenizer.decode(output_non_padded[0][0]			,    skip_special_tokens=_snake_case			)
          __lowercase						:     int		      =    tokenizer.decode(output_padded[0][0]			,    skip_special_tokens=_snake_case			)

          __lowercase						:     Any		      =    [
              '''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(_snake_case			,    _snake_case			)
          self.assertListEqual(_snake_case			,    [non_padded_sentence, padded_sentence]			)




from typing import TYPE_CHECKING

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


__lowerCAmelCase   :     Optional[int]      =						{"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase   :     List[Any]      =						[
     "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTHybridForImageClassification",
     "ViTHybridModel",
     "ViTHybridPreTrainedModel",
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase   :     Tuple      =						["ViTHybridImageProcessor"]


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

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


