




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


_lowerCamelCase			:	int							 =     logging.get_logger(__name__)

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


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






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


    def __init__(    self   :			Tuple      ,				UpperCAmelCase__   :			Optional[int]=True      ,				UpperCAmelCase__   :			List[str]=None      ,				UpperCAmelCase__   :			Optional[Any]=3      ,				UpperCAmelCase__   :			List[str]=100      ,				UpperCAmelCase__   :			Tuple=6      ,				UpperCAmelCase__   :			Optional[Any]=2_048      ,				UpperCAmelCase__   :			Union[str, Any]=8      ,				UpperCAmelCase__   :			Dict=6      ,				UpperCAmelCase__   :			Tuple=2_048      ,				UpperCAmelCase__   :			Optional[int]=8      ,				UpperCAmelCase__   :			str=0.0      ,				UpperCAmelCase__   :			Optional[Any]=0.0      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			Union[str, Any]="relu"      ,				UpperCAmelCase__   :			str=256      ,				UpperCAmelCase__   :			Optional[int]=0.1      ,				UpperCAmelCase__   :			List[Any]=0.0      ,				UpperCAmelCase__   :			List[str]=0.0      ,				UpperCAmelCase__   :			Optional[Any]=0.02      ,				UpperCAmelCase__   :			Any=1.0      ,				UpperCAmelCase__   :			List[str]=False      ,				UpperCAmelCase__   :			str="sine"      ,				UpperCAmelCase__   :			Optional[int]="resnet50"      ,				UpperCAmelCase__   :			List[str]=True      ,				UpperCAmelCase__   :			List[str]=False      ,				UpperCAmelCase__   :			Union[str, Any]=1      ,				UpperCAmelCase__   :			Union[str, Any]=5      ,				UpperCAmelCase__   :			int=2      ,				UpperCAmelCase__   :			Tuple=1      ,				UpperCAmelCase__   :			Dict=1      ,				UpperCAmelCase__   :			Any=5      ,				UpperCAmelCase__   :			List[Any]=2      ,				UpperCAmelCase__   :			Optional[Any]=0.1      ,				**UpperCAmelCase__   :			List[Any]      ,				)		->List[str]:





           '''simple docstring'''

           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__   				=					CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
                  elif isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                         A__   				=					backbone_config.get('''model_type''')
                         A__   				=					CONFIG_MAPPING[backbone_model_type]
                         A__   				=					config_class.from_dict(UpperCAmelCase__)
                  # set timm attributes to None
                  A__				,							A__				,							A__   				=					None, None, None

           A__   				=					use_timm_backbone
           A__   				=					backbone_config
           A__   				=					num_channels
           A__   				=					num_queries
           A__   				=					d_model
           A__   				=					encoder_ffn_dim
           A__   				=					encoder_layers
           A__   				=					encoder_attention_heads
           A__   				=					decoder_ffn_dim
           A__   				=					decoder_layers
           A__   				=					decoder_attention_heads
           A__   				=					dropout
           A__   				=					attention_dropout
           A__   				=					activation_dropout
           A__   				=					activation_function
           A__   				=					init_std
           A__   				=					init_xavier_std
           A__   				=					encoder_layerdrop
           A__   				=					decoder_layerdrop
           A__   				=					encoder_layers
           A__   				=					auxiliary_loss
           A__   				=					position_embedding_type
           A__   				=					backbone
           A__   				=					use_pretrained_backbone
           A__   				=					dilation
           # Hungarian matcher
           A__   				=					class_cost
           A__   				=					bbox_cost
           A__   				=					giou_cost
           # Loss coefficients
           A__   				=					mask_loss_coefficient
           A__   				=					dice_loss_coefficient
           A__   				=					bbox_loss_coefficient
           A__   				=					giou_loss_coefficient
           A__   				=					eos_coefficient
           super().__init__(is_encoder_decoder=UpperCAmelCase__      ,				**UpperCAmelCase__)


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->int:





           '''simple docstring'''

           return self.encoder_attention_heads





    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->int:





           '''simple docstring'''

           return self.d_model


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   version.parse('''1.11'''  )


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Mapping[str, Mapping[int, str]]:





           '''simple docstring'''

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


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->float:





           '''simple docstring'''

           return 1e-5





    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->int:





           '''simple docstring'''

           return 12








from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import tensorflow as tf

  from transformers.models.xglm.modeling_tf_xglm import (
      TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXGLMForCausalLM,
      TFXGLMModel,
  )


@require_tf
class        UpperCamelCase_					:

    '''simple docstring'''






    UpperCAmelCase__													=   XGLMConfig
    UpperCAmelCase__													=   {}
    UpperCAmelCase__													=   '''gelu'''


    def __init__(    self   :			str      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Union[str, Any]=14      ,				UpperCAmelCase__   :			Tuple=7      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Dict=True      ,				UpperCAmelCase__   :			Any=99      ,				UpperCAmelCase__   :			Union[str, Any]=32      ,				UpperCAmelCase__   :			Any=2      ,				UpperCAmelCase__   :			Dict=4      ,				UpperCAmelCase__   :			List[str]=37      ,				UpperCAmelCase__   :			Union[str, Any]="gelu"      ,				UpperCAmelCase__   :			Tuple=0.1      ,				UpperCAmelCase__   :			Any=0.1      ,				UpperCAmelCase__   :			Any=512      ,				UpperCAmelCase__   :			List[Any]=0.02      ,				)		->str:





           '''simple docstring'''

           A__   				=					parent
           A__   				=					batch_size
           A__   				=					seq_length
           A__   				=					is_training
           A__   				=					use_input_mask
           A__   				=					use_labels
           A__   				=					vocab_size
           A__   				=					d_model
           A__   				=					num_hidden_layers
           A__   				=					num_attention_heads
           A__   				=					ffn_dim
           A__   				=					activation_function
           A__   				=					activation_dropout
           A__   				=					attention_dropout
           A__   				=					max_position_embeddings
           A__   				=					initializer_range
           A__   				=					None
           A__   				=					0
           A__   				=					2
           A__   				=					1


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Optional[int]:





           '''simple docstring'''

           return XGLMConfig.from_pretrained('''facebook/xglm-564M''')


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Tuple:





           '''simple docstring'''

           A__   				=					tf.clip_by_value(
               ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size)      ,				clip_value_min=0      ,				clip_value_max=3)

           A__   				=					None
           if self.use_input_mask:
                  A__   				=					random_attention_mask([self.batch_size, self.seq_length])

           A__   				=					self.get_config()

           A__   				=					floats_tensor([self.num_hidden_layers, self.num_attention_heads]      ,				2)

           return (
               config,
               input_ids,
               input_mask,
               head_mask,
           )


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Dict:





           '''simple docstring'''

           return XGLMConfig(
               vocab_size=self.vocab_size      ,				d_model=self.hidden_size      ,				num_layers=self.num_hidden_layers      ,				attention_heads=self.num_attention_heads      ,				ffn_dim=self.ffn_dim      ,				activation_function=self.activation_function      ,				activation_dropout=self.activation_dropout      ,				attention_dropout=self.attention_dropout      ,				max_position_embeddings=self.max_position_embeddings      ,				initializer_range=self.initializer_range      ,				use_cache=UpperCAmelCase__      ,				bos_token_id=self.bos_token_id      ,				eos_token_id=self.eos_token_id      ,				pad_token_id=self.pad_token_id      ,				return_dict=UpperCAmelCase__      ,				)





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           A__   				=					self.prepare_config_and_inputs()

           (
               (
               A__
           )				,							(
               A__
           )				,							(
               A__
           )				,							(
               A__
           )				,							
           )   				=					config_and_inputs

           A__   				=					{
               '''input_ids''': input_ids,
               '''head_mask''': head_mask,
           }

           return config, inputs_dict


@require_tf
class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
    UpperCAmelCase__													=   (TFXGLMForCausalLM,) if is_tf_available() else ()
    UpperCAmelCase__													=   (
        {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
    )
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->List[Any]:





           '''simple docstring'''

           A__   				=					TFXGLMModelTester(self)
           A__   				=					ConfigTester(self      ,				config_class=UpperCAmelCase__      ,				n_embd=37)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->str:





           '''simple docstring'''

           self.config_tester.run_common_tests()


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->List[str]:





           '''simple docstring'''

           for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__   				=					TFXGLMModel.from_pretrained(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)





    @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.''')
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           super().test_resize_token_embeddings()


@require_tf
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Optional[Any]=True)		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')
           A__   				=					tf.convert_to_tensor([[2, 268, 9_865]]      ,				dtype=tf.intaa)  # The dog
           # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
           # fmt: off
           A__   				=					[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
           # fmt: on
           A__   				=					model.generate(UpperCAmelCase__      ,				do_sample=UpperCAmelCase__      ,				num_beams=1)
           if verify_outputs:
                  self.assertListEqual(output_ids[0].numpy().tolist()      ,				UpperCAmelCase__)


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           A__   				=					XGLMTokenizer.from_pretrained('''facebook/xglm-564M''')
           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')

           tf.random.set_seed(0)
           A__   				=					tokenizer('''Today is a nice day and'''      ,				return_tensors='''tf''')
           A__   				=					tokenized.input_ids
           # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
           with tf.device(''':/CPU:0'''):
                  A__   				=					model.generate(UpperCAmelCase__      ,				do_sample=UpperCAmelCase__      ,				seed=[7, 0])
           A__   				=					tokenizer.decode(output_ids[0]      ,				skip_special_tokens=UpperCAmelCase__)

           A__   				=					(
               '''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
           )
           self.assertEqual(UpperCAmelCase__      ,				UpperCAmelCase__)





    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')
           A__   				=					XGLMTokenizer.from_pretrained('''facebook/xglm-564M''')

           A__   				=					'''left'''

           # use different length sentences to test batching
           A__   				=					[
               '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
               '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
               '''regardless of whether left padding is applied or not. When''',
               '''Hello, my dog is a little''',
           ]

           A__   				=					tokenizer(UpperCAmelCase__      ,				return_tensors='''tf'''      ,				padding=UpperCAmelCase__)
           A__   				=					inputs['''input_ids''']

           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				attention_mask=inputs['''attention_mask''']      ,				max_new_tokens=12)

           A__   				=					tokenizer(sentences[0]      ,				return_tensors='''tf''').input_ids
           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				max_new_tokens=12)

           A__   				=					tokenizer(sentences[1]      ,				return_tensors='''tf''').input_ids
           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				max_new_tokens=12)

           A__   				=					tokenizer.batch_decode(UpperCAmelCase__      ,				skip_special_tokens=UpperCAmelCase__)
           A__   				=					tokenizer.decode(output_non_padded[0]      ,				skip_special_tokens=UpperCAmelCase__)
           A__   				=					tokenizer.decode(output_padded[0]      ,				skip_special_tokens=UpperCAmelCase__)

           A__   				=					[
               '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
               '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
               '''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
               '''a single''',
               '''Hello, my dog is a little bit of a shy one, but he is very friendly''',
           ]
           self.assertListEqual(UpperCAmelCase__      ,				UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__      ,				[non_padded_sentence, padded_sentence])


