"""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 						__A							:






     def __init__(		self    ,					a__    ,					a__=14    ,					a__=7    ,					a__=True    ,					a__=True    ,					a__=False    ,					a__=True    ,					a__=99    ,					a__=32    ,					a__=4    ,					a__=4    ,					a__=4    ,					a__=37    ,					a__="gelu"    ,					a__=0.1    ,					a__=0.1    ,					a__=512    ,					a__=0.0_2    ,					):
       _lowerCAmelCase						:	Dict        =					parent
       _lowerCAmelCase						:	Any        =					batch_size
       _lowerCAmelCase						:	Union[str, Any]        =					seq_length
       _lowerCAmelCase						:	Tuple        =					is_training
       _lowerCAmelCase						:	List[str]        =					use_input_mask
       _lowerCAmelCase						:	Optional[int]        =					use_token_type_ids
       _lowerCAmelCase						:	Dict        =					use_labels
       _lowerCAmelCase						:	List[Any]        =					vocab_size
       _lowerCAmelCase						:	Optional[Any]        =					hidden_size
       _lowerCAmelCase						:	List[Any]        =					rotary_dim
       _lowerCAmelCase						:	List[Any]        =					num_hidden_layers
       _lowerCAmelCase						:	Optional[int]        =					num_attention_heads
       _lowerCAmelCase						:	List[str]        =					intermediate_size
       _lowerCAmelCase						:	str        =					hidden_act
       _lowerCAmelCase						:	Any        =					hidden_dropout_prob
       _lowerCAmelCase						:	Union[str, Any]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Any        =					max_position_embeddings
       _lowerCAmelCase						:	int        =					initializer_range
       _lowerCAmelCase						:	Any        =					None
       _lowerCAmelCase						:	int        =					vocab_size - 1
       _lowerCAmelCase						:	str        =					vocab_size - 1
       _lowerCAmelCase						:	List[Any]        =					vocab_size - 1






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					ids_tensor([self.batch_size, self.seq_length]    ,					self.vocab_size  )

       _lowerCAmelCase						:	List[str]        =					None
       if self.use_input_mask:
         _lowerCAmelCase						:	Union[str, Any]        =					random_attention_mask([self.batch_size, self.seq_length]  )

       _lowerCAmelCase						:	Optional[Any]        =					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=a__    ,					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    __A  (		self  ):
       _lowerCAmelCase						:	int        =					self.prepare_config_and_inputs()
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	int        =					config_and_inputs
       _lowerCAmelCase						:	str        =					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
       return config, inputs_dict






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	List[str]        =					20
       _lowerCAmelCase						:	int        =					model_class_name(a__  )

       _lowerCAmelCase						:	Tuple        =					model.init_cache(input_ids.shape[0]    ,					a__  )
       _lowerCAmelCase						:	Dict        =					jnp.ones((input_ids.shape[0], max_decoder_length)    ,					dtype="""i4"""  )

       _lowerCAmelCase						:	List[str]        =					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=a__    ,					past_key_values=a__    ,					position_ids=a__    ,					)

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

       _lowerCAmelCase						:	Any        =					model(a__  )

       _lowerCAmelCase						:	List[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    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Dict        =					20
       _lowerCAmelCase						:	Union[str, Any]        =					model_class_name(a__  )

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

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

       _lowerCAmelCase						:	int        =					model(
           input_ids[:, :-1]    ,					attention_mask=a__    ,					past_key_values=a__    ,					position_ids=a__    ,					)
       _lowerCAmelCase						:	Any        =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,					dtype="""i4"""  )
       _lowerCAmelCase						:	Dict        =					model(
           input_ids[:, -1:]    ,					past_key_values=outputs_cache.past_key_values    ,					attention_mask=a__    ,					position_ids=a__    ,					)

       _lowerCAmelCase						:	Optional[Any]        =					model(a__    ,					attention_mask=a__  )

       _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}"  )







@require_flax
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      List[str]		 =		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
     _UpperCamelCase							:      Union[str, Any]		 =		(FlaxGPTJForCausalLM,) if is_flax_available() else ()






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					FlaxGPTJModelTester(self  )






     def    __A  (		self  ):
       for model_class_name in self.all_model_classes:
         _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Union[str, Any]        =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(a__    ,					a__    ,					a__    ,					a__  )






     def    __A  (		self  ):
       for model_class_name in self.all_model_classes:
         _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	List[str]        =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             a__    ,					a__    ,					a__    ,					a__  )






     @tooslow
     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					GPTaTokenizer.from_pretrained("""gpt2"""    ,					pad_token="""<|endoftext|>"""    ,					padding_side="""left"""  )
       _lowerCAmelCase						:	Dict        =					tokenizer(["""Hello this is a long string""", """Hey"""]    ,					return_tensors="""np"""    ,					padding=a__    ,					truncation=a__  )

       _lowerCAmelCase						:	Tuple        =					FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""  )
       _lowerCAmelCase						:	Optional[Any]        =					False
       _lowerCAmelCase						:	Optional[Any]        =					model.config.eos_token_id

       _lowerCAmelCase						:	Any        =					jax.jit(model.generate  )

       _lowerCAmelCase						:	Dict        =					jit_generate(
           inputs["""input_ids"""]    ,					attention_mask=inputs["""attention_mask"""]    ,					pad_token_id=tokenizer.pad_token_id  ).sequences

       _lowerCAmelCase						:	Any        =					tokenizer.batch_decode(a__    ,					skip_special_tokens=a__  )

       _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(a__    ,					a__  )






     @is_pt_flax_cross_test
     def    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase						:	str        =					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						:	str        =					self._prepare_for_class(a__    ,					a__  )
           _lowerCAmelCase						:	Any        =					{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

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

           _lowerCAmelCase						:	Union[str, Any]        =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,					a__  )
           _lowerCAmelCase						:	str        =					fx_state

           with torch.no_grad():
             _lowerCAmelCase						:	List[str]        =					pt_model(**a__  ).to_tuple()

           _lowerCAmelCase						:	Tuple        =					fx_model(**a__  ).to_tuple()
           self.assertEqual(len(a__  )    ,					len(a__  )    ,					"""Output lengths differ between Flax and PyTorch"""  )
           for fx_output, pt_output in zip(a__    ,					a__  ):
             self.assert_almost_equals(fx_output[:, -1]    ,					pt_output[:, -1].numpy()    ,					4e-2  )

           with tempfile.TemporaryDirectory() as tmpdirname:
             pt_model.save_pretrained(a__  )
             _lowerCAmelCase						:	Dict        =					model_class.from_pretrained(a__    ,					from_pt=a__  )

           _lowerCAmelCase						:	Optional[int]        =					fx_model_loaded(**a__  ).to_tuple()
           self.assertEqual(
               len(a__  )    ,					len(a__  )    ,					"""Output lengths differ between Flax and PyTorch"""  )
           for fx_output_loaded, pt_output in zip(a__    ,					a__  ):
             self.assert_almost_equals(fx_output_loaded[:, -1]    ,					pt_output[:, -1].numpy()    ,					4e-2  )






     @is_pt_flax_cross_test
     def    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase						:	Optional[Any]        =					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						:	Optional[Any]        =					self._prepare_for_class(a__    ,					a__  )
           _lowerCAmelCase						:	Optional[Any]        =					{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

           # load corresponding PyTorch class
           _lowerCAmelCase						:	Union[str, Any]        =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
           _lowerCAmelCase						:	Union[str, Any]        =					getattr(a__    ,					a__  )

           _lowerCAmelCase						:	Union[str, Any]        =					pt_model_class(a__  ).eval()
           _lowerCAmelCase						:	List[str]        =					model_class(a__    ,					dtype=jnp.floataa  )

           _lowerCAmelCase						:	Any        =					load_flax_weights_in_pytorch_model(a__    ,					fx_model.params  )
           _lowerCAmelCase      ,     _lowerCAmelCase						:	Tuple        =					pt_inputs["""input_ids"""].shape
           _lowerCAmelCase						:	Optional[int]        =					np.random.randint(0    ,					seq_length - 1    ,					size=(batch_size,)  )
           for batch_idx, start_index in enumerate(a__  ):
             _lowerCAmelCase						:	str        =					0
             _lowerCAmelCase						:	int        =					1
             _lowerCAmelCase						:	List[str]        =					0
             _lowerCAmelCase						:	int        =					1

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

           with torch.no_grad():
             _lowerCAmelCase						:	Any        =					pt_model(**a__  ).to_tuple()

           _lowerCAmelCase						:	Optional[int]        =					fx_model(**a__  ).to_tuple()
           self.assertEqual(len(a__  )    ,					len(a__  )    ,					"""Output lengths differ between Flax and PyTorch"""  )
           for fx_output, pt_output in zip(a__    ,					a__  ):
             self.assert_almost_equals(fx_output[:, -1]    ,					pt_output[:, -1].numpy()    ,					4e-2  )

           with tempfile.TemporaryDirectory() as tmpdirname:
             fx_model.save_pretrained(a__  )
             _lowerCAmelCase						:	Dict        =					pt_model_class.from_pretrained(a__    ,					from_flax=a__  )

           with torch.no_grad():
             _lowerCAmelCase						:	List[str]        =					pt_model_loaded(**a__  ).to_tuple()

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






     @tooslow
     def    __A  (		self  ):
       for model_class_name in self.all_model_classes:
         _lowerCAmelCase						:	Dict        =					model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""  )
         _lowerCAmelCase						:	int        =					model(np.ones((1, 1)  )  )
         self.assertIsNotNone(a__  )





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a				:		Any	   =       logging.get_logger(__name__)

_a				:		Any	   =       {
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[Any]		 =		"visual_bert"






     def __init__(		self    ,					a__=30522    ,					a__=768    ,					a__=512    ,					a__=12    ,					a__=12    ,					a__=3072    ,					a__="gelu"    ,					a__=0.1    ,					a__=0.1    ,					a__=512    ,					a__=2    ,					a__=0.0_2    ,					a__=1e-12    ,					a__=False    ,					a__=True    ,					a__=1    ,					a__=0    ,					a__=2    ,					**a__    ,					):
       super().__init__(pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					**a__  )

       _lowerCAmelCase						:	Optional[Any]        =					vocab_size
       _lowerCAmelCase						:	int        =					max_position_embeddings
       _lowerCAmelCase						:	Optional[Any]        =					hidden_size
       _lowerCAmelCase						:	Optional[int]        =					visual_embedding_dim
       _lowerCAmelCase						:	Optional[int]        =					num_hidden_layers
       _lowerCAmelCase						:	List[Any]        =					num_attention_heads
       _lowerCAmelCase						:	Any        =					intermediate_size
       _lowerCAmelCase						:	List[Any]        =					hidden_act
       _lowerCAmelCase						:	int        =					hidden_dropout_prob
       _lowerCAmelCase						:	Optional[Any]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Any        =					initializer_range
       _lowerCAmelCase						:	List[Any]        =					type_vocab_size
       _lowerCAmelCase						:	Union[str, Any]        =					layer_norm_eps
       _lowerCAmelCase						:	Optional[Any]        =					bypass_transformer
       _lowerCAmelCase						:	List[Any]        =					special_visual_initialize




