





"""simple docstring"""





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
  import torch






class 					_lowerCAmelCase   :
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase=1_4    , __UpperCAmelCase=7    , __UpperCAmelCase=True    , __UpperCAmelCase=True    , __UpperCAmelCase=False    , __UpperCAmelCase=True    , __UpperCAmelCase=9_9    , __UpperCAmelCase=3_2    , __UpperCAmelCase=4    , __UpperCAmelCase=4    , __UpperCAmelCase=4    , __UpperCAmelCase=3_7    , __UpperCAmelCase="gelu"    , __UpperCAmelCase=0.1    , __UpperCAmelCase=0.1    , __UpperCAmelCase=5_1_2    , __UpperCAmelCase=0.02    , ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							parent
         lowerCAmelCase__			:int											=							batch_size
         lowerCAmelCase__			:Dict											=							seq_length
         lowerCAmelCase__			:Union[str, Any]											=							is_training
         lowerCAmelCase__			:int											=							use_input_mask
         lowerCAmelCase__			:Tuple											=							use_token_type_ids
         lowerCAmelCase__			:Optional[int]											=							use_labels
         lowerCAmelCase__			:Dict											=							vocab_size
         lowerCAmelCase__			:str											=							hidden_size
         lowerCAmelCase__			:Union[str, Any]											=							rotary_dim
         lowerCAmelCase__			:Dict											=							num_hidden_layers
         lowerCAmelCase__			:Optional[int]											=							num_attention_heads
         lowerCAmelCase__			:Tuple											=							intermediate_size
         lowerCAmelCase__			:Dict											=							hidden_act
         lowerCAmelCase__			:Tuple											=							hidden_dropout_prob
         lowerCAmelCase__			:Dict											=							attention_probs_dropout_prob
         lowerCAmelCase__			:Any											=							max_position_embeddings
         lowerCAmelCase__			:str											=							initializer_range
         lowerCAmelCase__			:str											=							None
         lowerCAmelCase__			:List[Any]											=							vocab_size - 1
         lowerCAmelCase__			:List[str]											=							vocab_size - 1
         lowerCAmelCase__			:str											=							vocab_size - 1






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size      )

         lowerCAmelCase__			:List[str]											=							None
         if self.use_input_mask:
           lowerCAmelCase__			:Optional[int]											=							random_attention_mask([self.batch_size, self.seq_length]      )

         lowerCAmelCase__			:List[str]											=							GPTJConfig(
             vocab_size=self.vocab_size    , n_embd=self.hidden_size    , n_layer=self.num_hidden_layers    , n_head=self.num_attention_heads    , n_positions=self.max_position_embeddings    , use_cache=__UpperCAmelCase    , bos_token_id=self.bos_token_id    , eos_token_id=self.eos_token_id    , pad_token_id=self.pad_token_id    , rotary_dim=self.rotary_dim    , )

         return (config, input_ids, input_mask)






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							self.prepare_config_and_inputs()
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Any											=							config_and_inputs
         lowerCAmelCase__			:Union[str, Any]											=							{'input_ids': input_ids, 'attention_mask': attention_mask}
         return config, inputs_dict






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							2_0
         lowerCAmelCase__			:Union[str, Any]											=							model_class_name(__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							model.init_cache(input_ids.shape[0]    , __UpperCAmelCase      )
         lowerCAmelCase__			:str											=							jnp.ones((input_ids.shape[0], max_decoder_length)    , dtype='i4'      )

         lowerCAmelCase__			:List[Any]											=							jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1      )[None, :]    , (input_ids.shape[0], input_ids.shape[-1] - 1)      )
         lowerCAmelCase__			:Tuple											=							model(
             input_ids[:, :-1]    , attention_mask=__UpperCAmelCase    , past_key_values=__UpperCAmelCase    , position_ids=__UpperCAmelCase    , )

         lowerCAmelCase__			:Optional[int]											=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    , dtype='i4'      )
         lowerCAmelCase__			:List[str]											=							model(
             input_ids[:, -1:]    , attention_mask=__UpperCAmelCase    , past_key_values=outputs_cache.past_key_values    , position_ids=__UpperCAmelCase    , )

         lowerCAmelCase__			:List[str]											=							model(__UpperCAmelCase      )

         lowerCAmelCase__			: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      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							2_0
         lowerCAmelCase__			:Any											=							model_class_name(__UpperCAmelCase      )

         lowerCAmelCase__			:Any											=							jnp.concatenate(
             [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])      )]    , axis=-1    , )

         lowerCAmelCase__			:Any											=							model.init_cache(input_ids.shape[0]    , __UpperCAmelCase      )
         lowerCAmelCase__			:Union[str, Any]											=							jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1      )[None, :]    , (input_ids.shape[0], input_ids.shape[-1] - 1)      )

         lowerCAmelCase__			:Union[str, Any]											=							model(
             input_ids[:, :-1]    , attention_mask=__UpperCAmelCase    , past_key_values=__UpperCAmelCase    , position_ids=__UpperCAmelCase    , )
         lowerCAmelCase__			:Tuple											=							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    , dtype='i4'      )
         lowerCAmelCase__			:List[Any]											=							model(
             input_ids[:, -1:]    , past_key_values=outputs_cache.past_key_values    , attention_mask=__UpperCAmelCase    , position_ids=__UpperCAmelCase    , )

         lowerCAmelCase__			:Optional[int]											=							model(__UpperCAmelCase    , attention_mask=__UpperCAmelCase      )

         lowerCAmelCase__			:Tuple											=							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   (     a  ,   a  ,   unittest.TestCase       ):
       """simple docstring"""




       __magic_name__     :Any		  =					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
       __magic_name__     :Union[str, Any]		  =					(FlaxGPTJForCausalLM,) if is_flax_available() else ()






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							FlaxGPTJModelTester(self      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         for model_class_name in self.all_model_classes:
           lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Optional[Any]											=							self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         for model_class_name in self.all_model_classes:
           lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:str											=							self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      )






       @tooslow
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							GPTaTokenizer.from_pretrained('gpt2'    , pad_token='<|endoftext|>'    , padding_side='left'      )
         lowerCAmelCase__			:Dict											=							tokenizer(['Hello this is a long string', 'Hey']    , return_tensors='np'    , padding=__UpperCAmelCase    , truncation=__UpperCAmelCase      )

         lowerCAmelCase__			:List[Any]											=							FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'      )
         lowerCAmelCase__			:Dict											=							False
         lowerCAmelCase__			:Dict											=							model.config.eos_token_id

         lowerCAmelCase__			:Dict											=							jax.jit(model.generate      )

         lowerCAmelCase__			:Any											=							jit_generate(
             inputs['input_ids']    , attention_mask=inputs['attention_mask']    , pad_token_id=tokenizer.pad_token_id      ).sequences

         lowerCAmelCase__			:Optional[Any]											=							tokenizer.batch_decode(__UpperCAmelCase    , skip_special_tokens=__UpperCAmelCase      )

         lowerCAmelCase__			:Optional[Any]											=							[
             'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
             'Hey, I\'m a little late to the party. I\'m going to',
         ]

         self.assertListEqual(__UpperCAmelCase    , __UpperCAmelCase      )






       @is_pt_flax_cross_test
       def      snake_case						(    self      ):



         '''simple docstring'''

         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__      ):
             # prepare inputs
             lowerCAmelCase__			:Tuple											=							self._prepare_for_class(__UpperCAmelCase    , __UpperCAmelCase      )
             lowerCAmelCase__			:Optional[Any]											=							{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCAmelCase__			:Dict											=							model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCAmelCase__			:int											=							getattr(__UpperCAmelCase    , __UpperCAmelCase      )

             lowerCAmelCase__							,				lowerCAmelCase__			:str											=							pt_inputs['input_ids'].shape
             lowerCAmelCase__			:Tuple											=							np.random.randint(0    , seq_length - 1    , size=(batch_size,)      )
             for batch_idx, start_index in enumerate(__UpperCAmelCase      ):
               lowerCAmelCase__			:Tuple											=							0
               lowerCAmelCase__			:str											=							1
               lowerCAmelCase__			:int											=							0
               lowerCAmelCase__			:Dict											=							1
             lowerCAmelCase__			:Union[str, Any]											=							pt_model_class(__UpperCAmelCase      ).eval()
             lowerCAmelCase__			:List[Any]											=							model_class(__UpperCAmelCase    , dtype=jnp.floataa      )

             lowerCAmelCase__			:Optional[int]											=							convert_pytorch_state_dict_to_flax(pt_model.state_dict()    , __UpperCAmelCase      )
             lowerCAmelCase__			:Optional[int]											=							fx_state

             with torch.no_grad():
               lowerCAmelCase__			:Optional[int]											=							pt_model(**__UpperCAmelCase      ).to_tuple()

             lowerCAmelCase__			:Union[str, Any]											=							fx_model(**__UpperCAmelCase      ).to_tuple()
             self.assertEqual(len(__UpperCAmelCase      )    , len(__UpperCAmelCase      )    , 'Output lengths differ between Flax and PyTorch'      )
             for fx_output, pt_output in zip(__UpperCAmelCase    , __UpperCAmelCase      ):
               self.assert_almost_equals(fx_output[:, -1]    , pt_output[:, -1].numpy()    , 4E-2      )

             with tempfile.TemporaryDirectory() as tmpdirname:
               pt_model.save_pretrained(__UpperCAmelCase      )
               lowerCAmelCase__			:Any											=							model_class.from_pretrained(__UpperCAmelCase    , from_pt=__UpperCAmelCase      )

             lowerCAmelCase__			:Any											=							fx_model_loaded(**__UpperCAmelCase      ).to_tuple()
             self.assertEqual(
                 len(__UpperCAmelCase      )    , len(__UpperCAmelCase      )    , 'Output lengths differ between Flax and PyTorch'      )
             for fx_output_loaded, pt_output in zip(__UpperCAmelCase    , __UpperCAmelCase      ):
               self.assert_almost_equals(fx_output_loaded[:, -1]    , pt_output[:, -1].numpy()    , 4E-2      )






       @is_pt_flax_cross_test
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__							,				lowerCAmelCase__			:int											=							self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__      ):
             # prepare inputs
             lowerCAmelCase__			:Any											=							self._prepare_for_class(__UpperCAmelCase    , __UpperCAmelCase      )
             lowerCAmelCase__			:Optional[int]											=							{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCAmelCase__			:str											=							model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCAmelCase__			:Optional[int]											=							getattr(__UpperCAmelCase    , __UpperCAmelCase      )

             lowerCAmelCase__			:Tuple											=							pt_model_class(__UpperCAmelCase      ).eval()
             lowerCAmelCase__			:Union[str, Any]											=							model_class(__UpperCAmelCase    , dtype=jnp.floataa      )

             lowerCAmelCase__			:str											=							load_flax_weights_in_pytorch_model(__UpperCAmelCase    , fx_model.params      )
             lowerCAmelCase__							,				lowerCAmelCase__			:Union[str, Any]											=							pt_inputs['input_ids'].shape
             lowerCAmelCase__			:Optional[Any]											=							np.random.randint(0    , seq_length - 1    , size=(batch_size,)      )
             for batch_idx, start_index in enumerate(__UpperCAmelCase      ):
               lowerCAmelCase__			:Any											=							0
               lowerCAmelCase__			:Tuple											=							1
               lowerCAmelCase__			:Dict											=							0
               lowerCAmelCase__			:Optional[int]											=							1

             # make sure weights are tied in PyTorch
             pt_model.tie_weights()

             with torch.no_grad():
               lowerCAmelCase__			:str											=							pt_model(**__UpperCAmelCase      ).to_tuple()

             lowerCAmelCase__			:int											=							fx_model(**__UpperCAmelCase      ).to_tuple()
             self.assertEqual(len(__UpperCAmelCase      )    , len(__UpperCAmelCase      )    , 'Output lengths differ between Flax and PyTorch'      )
             for fx_output, pt_output in zip(__UpperCAmelCase    , __UpperCAmelCase      ):
               self.assert_almost_equals(fx_output[:, -1]    , pt_output[:, -1].numpy()    , 4E-2      )

             with tempfile.TemporaryDirectory() as tmpdirname:
               fx_model.save_pretrained(__UpperCAmelCase      )
               lowerCAmelCase__			:List[Any]											=							pt_model_class.from_pretrained(__UpperCAmelCase    , from_flax=__UpperCAmelCase      )

             with torch.no_grad():
               lowerCAmelCase__			:List[Any]											=							pt_model_loaded(**__UpperCAmelCase      ).to_tuple()

             self.assertEqual(
                 len(__UpperCAmelCase      )    , len(__UpperCAmelCase      )    , 'Output lengths differ between Flax and PyTorch'      )
             for fx_output, pt_output in zip(__UpperCAmelCase    , __UpperCAmelCase      ):
               self.assert_almost_equals(fx_output[:, -1]    , pt_output[:, -1].numpy()    , 4E-2      )






       @tooslow
       def      snake_case						(    self      ):



         '''simple docstring'''

         for model_class_name in self.all_model_classes:
           lowerCAmelCase__			:Optional[int]											=							model_class_name.from_pretrained('EleutherAI/gpt-j-6B'      )
           lowerCAmelCase__			:Tuple											=							model(np.ones((1, 1)      )      )
           self.assertIsNotNone(__UpperCAmelCase      )







"""simple docstring"""





from typing import TYPE_CHECKING

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


__A											=		{
    """configuration_owlvit""": [
        """OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """OwlViTConfig""",
        """OwlViTOnnxConfig""",
        """OwlViTTextConfig""",
        """OwlViTVisionConfig""",
    ],
    """processing_owlvit""": ["""OwlViTProcessor"""],
}


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		["""OwlViTFeatureExtractor"""]
  __A											=		["""OwlViTImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """OwlViTModel""",
      """OwlViTPreTrainedModel""",
      """OwlViTTextModel""",
      """OwlViTVisionModel""",
      """OwlViTForObjectDetection""",
  ]

if TYPE_CHECKING:
  from .configuration_owlvit import (
      OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      OwlViTConfig,
      OwlViTOnnxConfig,
      OwlViTTextConfig,
      OwlViTVisionConfig,
  )
  from .processing_owlvit import OwlViTProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_owlvit import OwlViTFeatureExtractor
    from .image_processing_owlvit import OwlViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_owlvit import (
        OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OwlViTForObjectDetection,
        OwlViTModel,
        OwlViTPreTrainedModel,
        OwlViTTextModel,
        OwlViTVisionModel,
    )

else:
  import sys

  __A											=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
