


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, 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
       lowercase_	   =     'platform'

       import jax
       import jax.numpy as jnp

       from transformers import BlenderbotTokenizer
       from transformers.models.blenderbot.modeling_flax_blenderbot import (
           FlaxBlenderbotForConditionalGeneration,
           FlaxBlenderbotModel,
           shift_tokens_right,
       )
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=None      ,	):
 if attention_mask is None:
  __lowerCamelCase		:							Optional[Any]		 =						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		:							Optional[Any]		 =						np.ones((config.encoder_layers, config.encoder_attention_heads)						)
 if decoder_head_mask is None:
  __lowerCamelCase		:							Union[str, Any]		 =						np.ones((config.decoder_layers, config.decoder_attention_heads)						)
 if cross_attn_head_mask is None:
  __lowerCamelCase		:							Optional[int]		 =						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 	A_ :




      '''simple docstring'''


      def __init__(    self:	List[Any]						,				a:	List[Any]						,				a:	str=13						,				a:	Dict=7						,				a:	Optional[Any]=True						,				a:	int=False						,				a:	int=99						,				a:	Dict=16						,				a:	Union[str, Any]=2						,				a:	Tuple=4						,				a:	Union[str, Any]=4						,				a:	List[str]="gelu"						,				a:	Union[str, Any]=0.1						,				a:	str=0.1						,				a:	List[Any]=32						,				a:	Optional[Any]=2						,				a:	str=1						,				a:	List[Any]=0						,				a:	Optional[int]=0.0_2						,				):
       __lowerCamelCase		:							Optional[int]		 =						parent
       __lowerCamelCase		:							Dict		 =						batch_size
       __lowerCamelCase		:							List[Any]		 =						seq_length
       __lowerCamelCase		:							Optional[Any]		 =						is_training
       __lowerCamelCase		:							Optional[Any]		 =						use_labels
       __lowerCamelCase		:							Tuple		 =						vocab_size
       __lowerCamelCase		:							Union[str, Any]		 =						hidden_size
       __lowerCamelCase		:							Dict		 =						num_hidden_layers
       __lowerCamelCase		:							Dict		 =						num_attention_heads
       __lowerCamelCase		:							Dict		 =						intermediate_size
       __lowerCamelCase		:							int		 =						hidden_act
       __lowerCamelCase		:							List[str]		 =						hidden_dropout_prob
       __lowerCamelCase		:							Tuple		 =						attention_probs_dropout_prob
       __lowerCamelCase		:							List[Any]		 =						max_position_embeddings
       __lowerCamelCase		:							Tuple		 =						eos_token_id
       __lowerCamelCase		:							Tuple		 =						pad_token_id
       __lowerCamelCase		:							Any		 =						bos_token_id
       __lowerCamelCase		:							Dict		 =						initializer_range




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

       __lowerCamelCase		:							List[str]		 =						shift_tokens_right(a						,				1						,				2    )

       __lowerCamelCase		:							List[str]		 =						BlenderbotConfig(
           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=a						,				)
       __lowerCamelCase		:							List[str]		 =						prepare_blenderbot_inputs_dict(a						,				a						,				a    )
       return config, inputs_dict




      def 					_snake_case     (    self:	List[Any]    ):
       __lowerCamelCase ,					__lowerCamelCase		:							int		 =						self.prepare_config_and_inputs()
       return config, inputs_dict




      def 					_snake_case     (    self:	Optional[int]						,				a:	Any						,				a:	List[str]						,				a:	Tuple    ):
       __lowerCamelCase		:							Optional[int]		 =						20
       __lowerCamelCase		:							int		 =						model_class_name(a    )

       __lowerCamelCase		:							Optional[Any]		 =						model.encode(inputs_dict['input_ids']    )

       __lowerCamelCase ,					__lowerCamelCase		:							Any		 =						(
           inputs_dict['decoder_input_ids'],
           inputs_dict['decoder_attention_mask'],
       )

       __lowerCamelCase		:							List[str]		 =						model.init_cache(decoder_input_ids.shape[0]						,				a						,				a    )
       __lowerCamelCase		:							int		 =						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,				dtype='i4'    )

       __lowerCamelCase		:							Union[str, 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		:							List[str]		 =						model.decode(
           decoder_input_ids[:, :-1]						,				a						,				decoder_attention_mask=a						,				past_key_values=a						,				decoder_position_ids=a						,				)

       __lowerCamelCase		:							Optional[int]		 =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,				dtype='i4'    )
       __lowerCamelCase		:							str		 =						model.decode(
           decoder_input_ids[:, -1:]						,				a						,				decoder_attention_mask=a						,				past_key_values=outputs_cache.past_key_values						,				decoder_position_ids=a						,				)

       __lowerCamelCase		:							List[str]		 =						model.decode(a						,				a    )

       __lowerCamelCase		:							Optional[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 					_snake_case     (    self:	Optional[int]						,				a:	List[Any]						,				a:	List[Any]						,				a:	List[Any]    ):
       __lowerCamelCase		:							Optional[Any]		 =						20
       __lowerCamelCase		:							Any		 =						model_class_name(a    )

       __lowerCamelCase		:							Tuple		 =						model.encode(inputs_dict['input_ids']    )

       __lowerCamelCase ,					__lowerCamelCase		:							List[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[Any]		 =						model.init_cache(decoder_input_ids.shape[0]						,				a						,				a    )
       __lowerCamelCase		:							Union[str, 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		:							Any		 =						model.decode(
           decoder_input_ids[:, :-1]						,				a						,				decoder_attention_mask=a						,				past_key_values=a						,				decoder_position_ids=a						,				)
       __lowerCamelCase		:							List[Any]		 =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,				dtype='i4'    )
       __lowerCamelCase		:							Dict		 =						model.decode(
           decoder_input_ids[:, -1:]						,				a						,				past_key_values=outputs_cache.past_key_values						,				decoder_attention_mask=a						,				decoder_position_ids=a						,				)

       __lowerCamelCase		:							List[Any]		 =						model.decode(a						,				a						,				decoder_attention_mask=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}'    )



@require_flax
class 	A_ (	unittest.TestCase   ):




      '''simple docstring'''


      __snake_case 	=       99




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Dict		 =						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		:							Tuple		 =						input_ids.shape[0]
       __lowerCamelCase		:							Optional[int]		 =						BlenderbotConfig(
           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 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		:							List[str]		 =						self._get_config_and_data()
       __lowerCamelCase		:							List[Any]		 =						FlaxBlenderbotForConditionalGeneration(a    )
       __lowerCamelCase		:							Optional[int]		 =						lm_model(input_ids=a    )
       __lowerCamelCase		:							Dict		 =						(batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['logits'].shape						,				a    )




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Union[str, Any]		 =						BlenderbotConfig(
           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		:							str		 =						FlaxBlenderbotForConditionalGeneration(a    )
       __lowerCamelCase		:							Union[str, Any]		 =						np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]						,				dtype=np.intaa    )
       __lowerCamelCase		:							int		 =						np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]						,				dtype=np.intaa    )
       __lowerCamelCase		:							Any		 =						lm_model(input_ids=a						,				decoder_input_ids=a    )
       __lowerCamelCase		:							Tuple		 =						(*summary.shape, config.vocab_size)
       self.assertEqual(outputs['logits'].shape						,				a    )




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Union[str, Any]		 =						np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]						,				dtype=np.intaa    )
       __lowerCamelCase		:							List[Any]		 =						shift_tokens_right(a						,				1						,				2    )
       __lowerCamelCase		:							List[str]		 =						np.equal(a						,				1    ).astype(np.floataa    ).sum()
       __lowerCamelCase		:							Dict		 =						np.equal(a						,				1    ).astype(np.floataa    ).sum()
       self.assertEqual(shifted.shape						,				input_ids.shape    )
       self.assertEqual(a						,				n_pad_before - 1    )
       self.assertTrue(np.equal(shifted[:, 0]						,				2    ).all()    )



@require_flax
class 	A_ (	__UpperCamelCase ,				unittest.TestCase ,				__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       True
      __snake_case 	=       (
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      __snake_case 	=       (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							int		 =						FlaxBlenderbotModelTester(self    )




      def 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase ,					__lowerCamelCase		:							List[str]		 =						self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(a						,				a						,				a    )




      def 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase ,					__lowerCamelCase		:							List[str]		 =						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(a						,				a						,				a    )




      def 					_snake_case     (    self:	Optional[Any]    ):
       __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		:							List[str]		 =						self._prepare_for_class(a						,				a    )
         __lowerCamelCase		:							str		 =						model_class(a    )

         @jax.jit
         def encode_jitted(a:	Any						,				a:	Dict=None						,				**a:	Union[str, Any]    ):
          return model.encode(input_ids=a						,				attention_mask=a    )

         with self.subTest('JIT Enabled'    ):
          __lowerCamelCase		:							Tuple		 =						encode_jitted(**a    ).to_tuple()

         with self.subTest('JIT Disabled'    ):
          with jax.disable_jit():
           __lowerCamelCase		:							int		 =						encode_jitted(**a    ).to_tuple()

         self.assertEqual(len(a    )						,				len(a    )    )
         for jitted_output, output in zip(a						,				a    ):
          self.assertEqual(jitted_output.shape						,				output.shape    )




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase ,					__lowerCamelCase		:							List[str]		 =						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__    ):
         __lowerCamelCase		:							Tuple		 =						model_class(a    )
         __lowerCamelCase		:							Tuple		 =						model.encode(inputs_dict['input_ids']						,				inputs_dict['attention_mask']    )

         __lowerCamelCase		:							Tuple		 =						{
             '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(a:	List[Any]						,				a:	List[str]						,				a:	Optional[Any]    ):
          return model.decode(
              decoder_input_ids=a						,				decoder_attention_mask=a						,				encoder_outputs=a						,				)

         with self.subTest('JIT Enabled'    ):
          __lowerCamelCase		:							Union[str, Any]		 =						decode_jitted(**a    ).to_tuple()

         with self.subTest('JIT Disabled'    ):
          with jax.disable_jit():
           __lowerCamelCase		:							List[Any]		 =						decode_jitted(**a    ).to_tuple()

         self.assertEqual(len(a    )						,				len(a    )    )
         for jitted_output, output in zip(a						,				a    ):
          self.assertEqual(jitted_output.shape						,				output.shape    )




      @slow
      def 					_snake_case     (    self:	int    ):
       for model_class_name in self.all_model_classes:
        __lowerCamelCase		:							List[str]		 =						model_class_name.from_pretrained('facebook/blenderbot-400M-distill'    )
        # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
        __lowerCamelCase		:							str		 =						np.ones((1, 1)    ) * model.config.eos_token_id
        __lowerCamelCase		:							Optional[Any]		 =						model(a    )
        self.assertIsNotNone(a    )




      @unittest.skipUnless(jax_device != 'cpu'						,				'3B test too slow on CPU.'    )
      @slow
      def 					_snake_case     (    self:	Any    ):
       __lowerCamelCase		:							Tuple		 =						{'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
       __lowerCamelCase		:							Tuple		 =						{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

       __lowerCamelCase		:							List[Any]		 =						FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'						,				from_pt=a    )
       __lowerCamelCase		:							Optional[int]		 =						BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'    )

       __lowerCamelCase		:							Tuple		 =						['Sam']
       __lowerCamelCase		:							Union[str, Any]		 =						tokenizer(a						,				return_tensors='jax'    )

       __lowerCamelCase		:							Optional[Any]		 =						model.generate(**a						,				**a    )
       __lowerCamelCase		:							Tuple		 =						'Sam is a great name. It means "sun" in Gaelic.'

       __lowerCamelCase		:							Optional[Any]		 =						tokenizer.batch_decode(a						,				**a    )
       assert generated_txt[0].strip() == tgt_text





import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
       from ...processing_utils import ProcessorMixin
       from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """owlvit_text_model"""




      def __init__(    self:	Optional[int]						,				a:	Dict=4_9408						,				a:	Optional[Any]=512						,				a:	Dict=2048						,				a:	Optional[Any]=12						,				a:	Tuple=8						,				a:	Union[str, Any]=16						,				a:	str="quick_gelu"						,				a:	List[Any]=1e-5						,				a:	Dict=0.0						,				a:	Optional[int]=0.0_2						,				a:	Dict=1.0						,				a:	Any=0						,				a:	Union[str, Any]=4_9406						,				a:	Any=4_9407						,				**a:	Dict						,				):
       super().__init__(pad_token_id=a						,				bos_token_id=a						,				eos_token_id=a						,				**a    )

       __lowerCamelCase		:							List[Any]		 =						vocab_size
       __lowerCamelCase		:							int		 =						hidden_size
       __lowerCamelCase		:							Optional[Any]		 =						intermediate_size
       __lowerCamelCase		:							List[Any]		 =						num_hidden_layers
       __lowerCamelCase		:							Any		 =						num_attention_heads
       __lowerCamelCase		:							Union[str, Any]		 =						max_position_embeddings
       __lowerCamelCase		:							List[Any]		 =						hidden_act
       __lowerCamelCase		:							List[str]		 =						layer_norm_eps
       __lowerCamelCase		:							Tuple		 =						attention_dropout
       __lowerCamelCase		:							Optional[int]		 =						initializer_range
       __lowerCamelCase		:							Tuple		 =						initializer_factor




      @classmethod
      def 					_snake_case     (    cls:	Dict						,				a:	Union[str, os.PathLike]						,				**a:	Optional[int]    ):
       cls._set_token_in_kwargs(a    )

       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						cls.get_config_dict(a						,				**a    )

       # get the text config dict if we are loading from OwlViTConfig
       if config_dict.get('model_type'    ) == "owlvit":
        __lowerCamelCase		:							Optional[Any]		 =						config_dict['text_config']

       if "model_type" in config_dict and hasattr(cls						,				'model_type'    ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
            F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

       return cls.from_dict(a						,				**a    )



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """owlvit_vision_model"""




      def __init__(    self:	int						,				a:	Tuple=768						,				a:	int=3072						,				a:	List[str]=12						,				a:	Optional[Any]=12						,				a:	Optional[int]=3						,				a:	Optional[int]=768						,				a:	Optional[Any]=32						,				a:	Optional[int]="quick_gelu"						,				a:	Union[str, Any]=1e-5						,				a:	Union[str, Any]=0.0						,				a:	Union[str, Any]=0.0_2						,				a:	int=1.0						,				**a:	Union[str, Any]						,				):
       super().__init__(**a    )

       __lowerCamelCase		:							str		 =						hidden_size
       __lowerCamelCase		:							Tuple		 =						intermediate_size
       __lowerCamelCase		:							Dict		 =						num_hidden_layers
       __lowerCamelCase		:							Optional[Any]		 =						num_attention_heads
       __lowerCamelCase		:							int		 =						num_channels
       __lowerCamelCase		:							Optional[Any]		 =						image_size
       __lowerCamelCase		:							Tuple		 =						patch_size
       __lowerCamelCase		:							List[str]		 =						hidden_act
       __lowerCamelCase		:							Tuple		 =						layer_norm_eps
       __lowerCamelCase		:							List[Any]		 =						attention_dropout
       __lowerCamelCase		:							Tuple		 =						initializer_range
       __lowerCamelCase		:							List[Any]		 =						initializer_factor




      @classmethod
      def 					_snake_case     (    cls:	Optional[int]						,				a:	Union[str, os.PathLike]						,				**a:	int    ):
       cls._set_token_in_kwargs(a    )

       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						cls.get_config_dict(a						,				**a    )

       # get the vision config dict if we are loading from OwlViTConfig
       if config_dict.get('model_type'    ) == "owlvit":
        __lowerCamelCase		:							Any		 =						config_dict['vision_config']

       if "model_type" in config_dict and hasattr(cls						,				'model_type'    ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
            F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

       return cls.from_dict(a						,				**a    )



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """owlvit"""
      __snake_case 	=       True




      def __init__(    self:	Dict						,				a:	int=None						,				a:	str=None						,				a:	Tuple=512						,				a:	Tuple=2.6_5_9_2						,				a:	int=True						,				**a:	int						,				):
       super().__init__(**a    )

       if text_config is None:
        __lowerCamelCase		:							List[str]		 =						{}
        logger.info('text_config is None. Initializing the OwlViTTextConfig with default values.'    )

       if vision_config is None:
        __lowerCamelCase		:							str		 =						{}
        logger.info('vision_config is None. initializing the OwlViTVisionConfig with default values.'    )

       __lowerCamelCase		:							List[Any]		 =						OwlViTTextConfig(**a    )
       __lowerCamelCase		:							str		 =						OwlViTVisionConfig(**a    )

       __lowerCamelCase		:							Union[str, Any]		 =						projection_dim
       __lowerCamelCase		:							Tuple		 =						logit_scale_init_value
       __lowerCamelCase		:							Dict		 =						return_dict
       __lowerCamelCase		:							Tuple		 =						1.0




      @classmethod
      def 					_snake_case     (    cls:	str						,				a:	Union[str, os.PathLike]						,				**a:	List[Any]    ):
       cls._set_token_in_kwargs(a    )

       __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						cls.get_config_dict(a						,				**a    )

       if "model_type" in config_dict and hasattr(cls						,				'model_type'    ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
            F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

       return cls.from_dict(a						,				**a    )




      @classmethod
      def 					_snake_case     (    cls:	Tuple						,				a:	Dict						,				a:	Dict						,				**a:	str    ):
       __lowerCamelCase		:							List[str]		 =						{}
       __lowerCamelCase		:							List[str]		 =						text_config
       __lowerCamelCase		:							Optional[int]		 =						vision_config

       return cls.from_dict(a						,				**a    )




      def 					_snake_case     (    self:	Optional[int]    ):
       __lowerCamelCase		:							Optional[Any]		 =						copy.deepcopy(self.__dict__    )
       __lowerCamelCase		:							List[Any]		 =						self.text_config.to_dict()
       __lowerCamelCase		:							List[str]		 =						self.vision_config.to_dict()
       __lowerCamelCase		:							Optional[Any]		 =						self.__class__.model_type
       return output



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	str    ):
       return OrderedDict(
           [
               ('input_ids', {0: 'batch', 1: 'sequence'}),
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
               ('attention_mask', {0: 'batch', 1: 'sequence'}),
           ]    )




      @property
      def 					_snake_case     (    self:	Dict    ):
       return OrderedDict(
           [
               ('logits_per_image', {0: 'batch'}),
               ('logits_per_text', {0: 'batch'}),
               ('text_embeds', {0: 'batch'}),
               ('image_embeds', {0: 'batch'}),
           ]    )




      @property
      def 					_snake_case     (    self:	int    ):
       return 1e-4




      def 					_snake_case     (    self:	Any						,				a:	"ProcessorMixin"						,				a:	int = -1						,				a:	int = -1						,				a:	Optional["TensorType"] = None						,				):
       __lowerCamelCase		:							List[str]		 =						super().generate_dummy_inputs(
           processor.tokenizer						,				batch_size=a						,				seq_length=a						,				framework=a    )
       __lowerCamelCase		:							int		 =						super().generate_dummy_inputs(
           processor.image_processor						,				batch_size=a						,				framework=a    )
       return {**text_input_dict, **image_input_dict}




      @property
      def 					_snake_case     (    self:	int    ):
       return 14

