
"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel







@require_tf
class   _lowercase						:


     '''simple docstring'''





     _A								=							BlenderbotSmallConfig
     _A								=							{}
     _A								=							'gelu'



     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase=13 ,			__UpperCamelCase=7 ,			__UpperCamelCase=True ,			__UpperCamelCase=False ,			__UpperCamelCase=99 ,			__UpperCamelCase=32 ,			__UpperCamelCase=2 ,			__UpperCamelCase=4 ,			__UpperCamelCase=37 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=20 ,			__UpperCamelCase=2 ,			__UpperCamelCase=1 ,			__UpperCamelCase=0 ,			)->					str:
          UpperCAmelCase__							: List[str] 							=						parent
          UpperCAmelCase__							: int 							=						batch_size
          UpperCAmelCase__							: str 							=						seq_length
          UpperCAmelCase__							: Dict 							=						is_training
          UpperCAmelCase__							: Optional[Any] 							=						use_labels
          UpperCAmelCase__							: str 							=						vocab_size
          UpperCAmelCase__							: Optional[Any] 							=						hidden_size
          UpperCAmelCase__							: Optional[int] 							=						num_hidden_layers
          UpperCAmelCase__							: Tuple 							=						num_attention_heads
          UpperCAmelCase__							: Dict 							=						intermediate_size

          UpperCAmelCase__							: str 							=						hidden_dropout_prob
          UpperCAmelCase__							: int 							=						attention_probs_dropout_prob
          UpperCAmelCase__							: Tuple 							=						max_position_embeddings
          UpperCAmelCase__							: Tuple 							=						eos_token_id
          UpperCAmelCase__							: Optional[Any] 							=						pad_token_id
          UpperCAmelCase__							: Tuple 							=						bos_token_id



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						ids_tensor([self.batch_size, self.seq_length - 1] ,			self.vocab_size  )
          UpperCAmelCase__							: Optional[Any] 							=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  ) ,			1  )
          UpperCAmelCase__							: Dict 							=						tf.concat([input_ids, eos_tensor] ,			axis=1  )

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

          UpperCAmelCase__							: Optional[Any] 							=						self.config_cls(
              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_ids=[2] ,			bos_token_id=self.bos_token_id ,			pad_token_id=self.pad_token_id ,			decoder_start_token_id=self.pad_token_id ,			**self.config_updates ,			)
          UpperCAmelCase__							: List[str] 							=						prepare_blenderbot_small_inputs_dict(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )
          return config, inputs_dict



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					Any:
          UpperCAmelCase__							: Union[str, Any] 							=						TFBlenderbotSmallModel(config=__UpperCamelCase  ).get_decoder()
          UpperCAmelCase__							: Any 							=						inputs_dict["input_ids"]

          UpperCAmelCase__							: Tuple 							=						input_ids[:1, :]
          UpperCAmelCase__							: str 							=						inputs_dict["attention_mask"][:1, :]
          UpperCAmelCase__							: int 							=						inputs_dict["head_mask"]
          UpperCAmelCase__							: Dict 							=						1

          # first forward pass
          UpperCAmelCase__							: str 							=						model(__UpperCamelCase ,			attention_mask=__UpperCamelCase ,			head_mask=__UpperCamelCase ,			use_cache=__UpperCamelCase  )

          UpperCAmelCase__							,	UpperCAmelCase__							: Dict 							=						outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          UpperCAmelCase__							: Any 							=						ids_tensor((self.batch_size, 3) ,			config.vocab_size  )
          UpperCAmelCase__							: Union[str, Any] 							=						tf.cast(ids_tensor((self.batch_size, 3) ,			2  ) ,			tf.inta  )

          # append to next input_ids and
          UpperCAmelCase__							: str 							=						tf.concat([input_ids, next_tokens] ,			axis=-1  )
          UpperCAmelCase__							: Optional[int] 							=						tf.concat([attention_mask, next_attn_mask] ,			axis=-1  )

          UpperCAmelCase__							: Dict 							=						model(__UpperCamelCase ,			attention_mask=__UpperCamelCase  )[0]
          UpperCAmelCase__							: List[str] 							=						model(__UpperCamelCase ,			attention_mask=__UpperCamelCase ,			past_key_values=__UpperCamelCase  )[0]

          self.parent.assertEqual(next_tokens.shape[1] ,			output_from_past.shape[1]  )

          # select random slice
          UpperCAmelCase__							: Dict 							=						int(ids_tensor((1,) ,			output_from_past.shape[-1]  )  )
          UpperCAmelCase__							: List[str] 							=						output_from_no_past[:, -3:, random_slice_idx]
          UpperCAmelCase__							: Tuple 							=						output_from_past[:, :, random_slice_idx]

          # test that outputs are equal for slice
          tf.debugging.assert_near(__UpperCamelCase ,			__UpperCamelCase ,			rtol=1E-3  )





def 							a__ (				lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       int=None       ,    lowerCAmelCase	:       List[str]=None       ,    lowerCAmelCase	:       Tuple=None       ,    lowerCAmelCase	:       Any=None       ,    lowerCAmelCase	:       List[Any]=None       ,    ):






     '''simple docstring'''
     if attention_mask is None:
          UpperCAmelCase__							: List[str] 							=						tf.cast(tf.math.not_equal(lowerCAmelCase       ,    config.pad_token_id		)       ,    tf.inta		)
     if decoder_attention_mask is None:
          UpperCAmelCase__							: Tuple 							=						tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape       ,    dtype=tf.inta		),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]       ,    config.pad_token_id		)       ,    tf.inta		),
              ]       ,    axis=-1       ,    )
     if head_mask is None:
          UpperCAmelCase__							: Dict 							=						tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
     if decoder_head_mask is None:
          UpperCAmelCase__							: List[str] 							=						tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
     if cross_attn_head_mask is None:
          UpperCAmelCase__							: int 							=						tf.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": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }







@require_tf
class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     _A								=							(
         (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
     )
     _A								=							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
     _A								=							(
         {
             'conversational': TFBlenderbotSmallForConditionalGeneration,
             'feature-extraction': TFBlenderbotSmallModel,
             'summarization': TFBlenderbotSmallForConditionalGeneration,
             'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
             'translation': TFBlenderbotSmallForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     _A								=							True
     _A								=							False
     _A								=							False



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[int] 							=						TFBlenderbotSmallModelTester(self  )
          UpperCAmelCase__							: Union[str, Any] 							=						ConfigTester(self ,			config_class=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Any:
          self.config_tester.run_common_tests()



     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: List[str] 							=						self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase  )







@require_tokenizers
@require_tf
class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     _A								=							[
         'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
         '  i\'m going to throw up.\nand why is that?'
     ]
     _A								=							'facebook/blenderbot_small-90M'



     @cached_property
     def  lowerCAmelCase__       (      self  )->					List[str]:
          # use "old" tokenizer here because of bug when downloading new tokenizer
          return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"  )



     @cached_property
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          UpperCAmelCase__							: str 							=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
          return model



     @slow
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: Optional[Any] 							=						self.tokenizer(self.src_text ,			return_tensors="tf"  )
          UpperCAmelCase__							: Union[str, Any] 							=						self.model.generate(
              model_inputs.input_ids ,			attention_mask=model_inputs.attention_mask ,			num_beams=2 ,			use_cache=__UpperCamelCase ,			)
          UpperCAmelCase__							: List[str] 							=						self.tokenizer.batch_decode(generated_ids.numpy() ,			skip_special_tokens=__UpperCamelCase  )[0]
          assert generated_words in (
              "i don't know. i just feel like i'm going to throw up. it's not fun.",
              "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
              "i'm not sure. i just feel like i've been in a bad situation.",
          )





"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

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


A__      :	Union[str, Any]          =    logging.get_logger(__name__)

A__      :	Optional[int]          =    {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							'table-transformer'
     _A								=							['past_key_values']
     _A								=							{
         'hidden_size': 'd_model',
         'num_attention_heads': 'encoder_attention_heads',
     }



     def __init__(      self ,			__UpperCamelCase=True ,			__UpperCamelCase=None ,			__UpperCamelCase=3 ,			__UpperCamelCase=1_00 ,			__UpperCamelCase=6 ,			__UpperCamelCase=20_48 ,			__UpperCamelCase=8 ,			__UpperCamelCase=6 ,			__UpperCamelCase=20_48 ,			__UpperCamelCase=8 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=True ,			__UpperCamelCase="relu" ,			__UpperCamelCase=2_56 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=0.02 ,			__UpperCamelCase=1.0 ,			__UpperCamelCase=False ,			__UpperCamelCase="sine" ,			__UpperCamelCase="resnet50" ,			__UpperCamelCase=True ,			__UpperCamelCase=False ,			__UpperCamelCase=1 ,			__UpperCamelCase=5 ,			__UpperCamelCase=2 ,			__UpperCamelCase=1 ,			__UpperCamelCase=1 ,			__UpperCamelCase=5 ,			__UpperCamelCase=2 ,			__UpperCamelCase=0.1 ,			**__UpperCamelCase ,			)->					List[Any]:
          if backbone_config is not None and use_timm_backbone:
               raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."  )

          if not use_timm_backbone:
               if backbone_config is None:
                    logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."  )
                    UpperCAmelCase__							: Any 							=						CONFIG_MAPPING["resnet"](out_features=["stage4"]  )
               elif isinstance(__UpperCamelCase ,			__UpperCamelCase  ):
                    UpperCAmelCase__							: int 							=						backbone_config.get("model_type"  )
                    UpperCAmelCase__							: Optional[Any] 							=						CONFIG_MAPPING[backbone_model_type]
                    UpperCAmelCase__							: int 							=						config_class.from_dict(__UpperCamelCase  )
               # set timm attributes to None
               UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						None, None, None

          UpperCAmelCase__							: Optional[int] 							=						use_timm_backbone
          UpperCAmelCase__							: Dict 							=						backbone_config
          UpperCAmelCase__							: List[Any] 							=						num_channels
          UpperCAmelCase__							: Any 							=						num_queries
          UpperCAmelCase__							: int 							=						d_model
          UpperCAmelCase__							: Optional[int] 							=						encoder_ffn_dim
          UpperCAmelCase__							: str 							=						encoder_layers
          UpperCAmelCase__							: Dict 							=						encoder_attention_heads
          UpperCAmelCase__							: Optional[Any] 							=						decoder_ffn_dim
          UpperCAmelCase__							: Tuple 							=						decoder_layers
          UpperCAmelCase__							: Optional[Any] 							=						decoder_attention_heads
          UpperCAmelCase__							: List[str] 							=						dropout
          UpperCAmelCase__							: Tuple 							=						attention_dropout
          UpperCAmelCase__							: List[Any] 							=						activation_dropout
          UpperCAmelCase__							: Dict 							=						activation_function
          UpperCAmelCase__							: Optional[Any] 							=						init_std
          UpperCAmelCase__							: List[str] 							=						init_xavier_std
          UpperCAmelCase__							: int 							=						encoder_layerdrop
          UpperCAmelCase__							: Tuple 							=						decoder_layerdrop
          UpperCAmelCase__							: int 							=						encoder_layers
          UpperCAmelCase__							: Dict 							=						auxiliary_loss
          UpperCAmelCase__							: Union[str, Any] 							=						position_embedding_type
          UpperCAmelCase__							: List[str] 							=						backbone
          UpperCAmelCase__							: List[Any] 							=						use_pretrained_backbone
          UpperCAmelCase__							: List[str] 							=						dilation
          # Hungarian matcher
          UpperCAmelCase__							: Dict 							=						class_cost
          UpperCAmelCase__							: Any 							=						bbox_cost
          UpperCAmelCase__							: Tuple 							=						giou_cost
          # Loss coefficients
          UpperCAmelCase__							: Any 							=						mask_loss_coefficient
          UpperCAmelCase__							: Dict 							=						dice_loss_coefficient
          UpperCAmelCase__							: Any 							=						bbox_loss_coefficient
          UpperCAmelCase__							: Tuple 							=						giou_loss_coefficient
          UpperCAmelCase__							: List[Any] 							=						eos_coefficient
          super().__init__(is_encoder_decoder=__UpperCamelCase ,			**__UpperCamelCase  )



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return self.encoder_attention_heads



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return self.d_model







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							version.parse('1.11'				)



     @property
     def  lowerCAmelCase__       (      self  )->					Mapping[str, Mapping[int, str]]:
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ("pixel_mask", {0: "batch"}),
              ]  )



     @property
     def  lowerCAmelCase__       (      self  )->					float:
          return 1E-5



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return 12



