





'''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


snake_case_     =	logging.get_logger(__name__)

snake_case_     =	{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}


class   _lowercase						(							a				):
   _UpperCamelCase       		=   """table-transformer"""
   _UpperCamelCase       		=   ["""past_key_values"""]
   _UpperCamelCase       		=   {
       """hidden_size""": """d_model""",
       """num_attention_heads""": """encoder_attention_heads""",
   }


   def __init__( self    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=3    ,   _UpperCAmelCase=100    ,   _UpperCAmelCase=6    ,   _UpperCAmelCase=2_048    ,   _UpperCAmelCase=8    ,   _UpperCAmelCase=6    ,   _UpperCAmelCase=2_048    ,   _UpperCAmelCase=8    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase="relu"    ,   _UpperCAmelCase=256    ,   _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    ,   ):
       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.'''					)
               A       :       List[Any]							=						CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
           elif isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
               A       :       List[Any]							=						backbone_config.get('''model_type'''					)
               A       :       Tuple							=						CONFIG_MAPPING[backbone_model_type]
               A       :       str							=						config_class.from_dict(_UpperCAmelCase					)
           # set timm attributes to None
           A,	A,	A       :       List[str]							=						None, None, None

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


   @property
   def 					snake_case				( self					):
       return self.encoder_attention_heads


   @property
   def 					snake_case				( self					):
       return self.d_model







class   _lowercase						(							a				):
   _UpperCamelCase       		=   version.parse("""1.11"""				)


   @property
   def 					snake_case				( self					):
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ('''pixel_mask''', {0: '''batch'''}),
           ]					)


   @property
   def 					snake_case				( self					):
       return 1E-5


   @property
   def 					snake_case				( self					):
       return 12







'''simple docstring'''






import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
     import os

     import jax.numpy as jnp
     from jax import jit

     from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
     from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

     snake_case_     =	"""0.12"""  # assumed parallelism: 8


if is_torch_available():
     import torch
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : Optional[int]    ,       UpperCamelCase__    : Any=None		)		->  List[Any]:
    if rng is None:
        A       :       int							=						random.Random()

    A       :       int							=						1
    for dim in shape:
        total_dims *= dim

    A       :       Union[str, Any]							=						[]
    for _ in range(UpperCamelCase__		):
        values.append(rng.randint(0    ,       vocab_size - 1		)		)

    A       :       Union[str, Any]							=						np.array(UpperCamelCase__    ,       dtype=jnp.intaa		).reshape(UpperCamelCase__		)

    return output
def 							_lowerCamelCase(  UpperCamelCase__    : Dict    ,       UpperCamelCase__    : Optional[Any]=None		)		->  Dict:
    A       :       Union[str, Any]							=						ids_tensor(UpperCamelCase__    ,       vocab_size=2    ,       rng=UpperCamelCase__		)
    # make sure that at least one token is attended to for each batch
    A       :       Any							=						1
    return attn_mask


@require_flax
class   _lowercase						:
   _UpperCamelCase       		=   None
   _UpperCamelCase       		=   ()


   def 					snake_case				( self					):
       A,	A       :       Optional[int]							=						self.model_tester.prepare_config_and_inputs_for_common()

       # cut to half length & take max batch_size 3
       A       :       Dict							=						2
       A       :       List[str]							=						inputs['''input_ids'''].shape[-1] // 2
       A       :       int							=						inputs['''input_ids'''][:max_batch_size, :sequence_length]

       A       :       Optional[Any]							=						jnp.ones_like(_UpperCAmelCase					)
       A       :       int							=						attention_mask[:max_batch_size, :sequence_length]

       # generate max 5 tokens
       A       :       Optional[int]							=						input_ids.shape[-1] + 5
       if config.eos_token_id is not None and config.pad_token_id is None:
           # hack to allow generate for models such as GPT2 as is done in `generate()`
           A       :       str							=						config.eos_token_id
       return config, input_ids, attention_mask, max_length


   @is_pt_flax_cross_test
   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[Any]							=						self._get_input_ids_and_config()
       A       :       Optional[int]							=						False
       A       :       Union[str, Any]							=						max_length
       A       :       str							=						0

       for model_class in self.all_generative_model_classes:
           A       :       int							=						model_class(_UpperCAmelCase					)

           A       :       Dict							=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
           A       :       int							=						getattr(_UpperCAmelCase    ,   _UpperCAmelCase					)
           A       :       Union[str, Any]							=						pt_model_class(_UpperCAmelCase					).eval()
           A       :       Tuple							=						load_flax_weights_in_pytorch_model(_UpperCAmelCase    ,   flax_model.params					)

           A       :       Union[str, Any]							=						flax_model.generate(_UpperCAmelCase					).sequences
           A       :       Optional[int]							=						pt_model.generate(torch.tensor(_UpperCAmelCase    ,   dtype=torch.long					)					)

           if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
               A       :       List[Any]							=						flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

           self.assertListEqual(pt_generation_outputs.numpy().tolist()    ,   flax_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       str							=						self._get_input_ids_and_config()
       A       :       Optional[int]							=						False
       A       :       Dict							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       Any							=						model_class(_UpperCAmelCase					)

           A       :       Dict							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       str							=						jit(model.generate					)
           A       :       List[Any]							=						jit_generate(_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       List[Any]							=						self._get_input_ids_and_config()
       A       :       List[str]							=						True
       A       :       List[Any]							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       Any							=						model_class(_UpperCAmelCase					)

           A       :       Tuple							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       Tuple							=						jit(model.generate					)
           A       :       int							=						jit_generate(_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       List[str]							=						self._get_input_ids_and_config()
       A       :       Any							=						False
       A       :       str							=						max_length
       A       :       Optional[int]							=						2

       for model_class in self.all_generative_model_classes:
           A       :       Dict							=						model_class(_UpperCAmelCase					)

           A       :       Any							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       List[Any]							=						jit(model.generate					)
           A       :       Optional[Any]							=						jit_generate(_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[int]							=						self._get_input_ids_and_config()
       A       :       Dict							=						False
       A       :       List[Any]							=						max_length
       A       :       Optional[int]							=						2
       A       :       int							=						2

       for model_class in self.all_generative_model_classes:
           A       :       List[str]							=						model_class(_UpperCAmelCase					)

           A       :       List[str]							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[0]    ,   input_ids.shape[0] * config.num_return_sequences					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       List[Any]							=						self._get_input_ids_and_config()
       A       :       Any							=						True
       A       :       Any							=						max_length
       A       :       Any							=						0.8
       A       :       List[Any]							=						10
       A       :       List[Any]							=						0.3
       A       :       Union[str, Any]							=						1
       A       :       Any							=						8
       A       :       List[Any]							=						9

       for model_class in self.all_generative_model_classes:
           A       :       List[Any]							=						model_class(_UpperCAmelCase					)

           A       :       Any							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       int							=						jit(model.generate					)
           A       :       List[Any]							=						jit_generate(_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[Any]							=						self._get_input_ids_and_config()
       A       :       int							=						max_length
       A       :       Dict							=						1
       A       :       Tuple							=						8
       A       :       Optional[Any]							=						9

       for model_class in self.all_generative_model_classes:
           A       :       Dict							=						model_class(_UpperCAmelCase					)

           A       :       str							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       int							=						jit(model.generate					)
           A       :       Optional[int]							=						jit_generate(_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       int							=						self._get_input_ids_and_config()
       A       :       List[Any]							=						max_length
       A       :       Optional[Any]							=						2
       A       :       Tuple							=						1
       A       :       List[Any]							=						8
       A       :       Dict							=						9

       for model_class in self.all_generative_model_classes:
           A       :       Union[str, Any]							=						model_class(_UpperCAmelCase					)

           A       :       Any							=						model.generate(_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       Union[str, Any]							=						jit(model.generate					)
           A       :       int							=						jit_generate(_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Dict							=						self._get_input_ids_and_config()

       # pad attention mask on the left
       A       :       Union[str, Any]							=						attention_mask.at[(0, 0)].set(0					)

       A       :       int							=						False
       A       :       List[Any]							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       List[str]							=						model_class(_UpperCAmelCase					)

           A       :       Dict							=						model.generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       Dict							=						jit(model.generate					)
           A       :       Optional[Any]							=						jit_generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[Any]							=						self._get_input_ids_and_config()

       # pad attention mask on the left
       A       :       Optional[Any]							=						attention_mask.at[(0, 0)].set(0					)

       A       :       Optional[int]							=						True
       A       :       int							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       int							=						model_class(_UpperCAmelCase					)

           A       :       Union[str, Any]							=						model.generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       Optional[Any]							=						jit(model.generate					)
           A       :       Dict							=						jit_generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)


   def 					snake_case				( self					):
       A,	A,	A,	A       :       Optional[Any]							=						self._get_input_ids_and_config()

       # pad attention mask on the left
       A       :       Optional[int]							=						attention_mask.at[(0, 0)].set(0					)

       A       :       Any							=						2
       A       :       Any							=						max_length

       for model_class in self.all_generative_model_classes:
           A       :       Dict							=						model_class(_UpperCAmelCase					)

           A       :       Optional[Any]							=						model.generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences
           self.assertEqual(generation_outputs.shape[-1]    ,   _UpperCAmelCase					)

           A       :       str							=						jit(model.generate					)
           A       :       List[str]							=						jit_generate(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					).sequences

           self.assertListEqual(generation_outputs.tolist()    ,   jit_generation_outputs.tolist()					)







@require_flax
class   _lowercase						(							unittest.TestCase				):


   def 					snake_case				( self					):
       A       :       List[Any]							=						AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''					)
       A       :       Dict							=						FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''					)

       A       :       Union[str, Any]							=						'''Hello world'''
       A       :       Union[str, Any]							=						tokenizer(_UpperCAmelCase    ,   return_tensors='''np'''					).input_ids

       # typos are quickly detected (the correct argument is `do_sample`)
       with self.assertRaisesRegex(_UpperCAmelCase    ,   '''do_samples'''					):
           model.generate(_UpperCAmelCase    ,   do_samples=_UpperCAmelCase					)

       # arbitrary arguments that will not be used anywhere are also not accepted
       with self.assertRaisesRegex(_UpperCAmelCase    ,   '''foo'''					):
           A       :       List[Any]							=						{'''foo''': '''bar'''}
           model.generate(_UpperCAmelCase    ,   **_UpperCAmelCase					)
