import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from ....modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
from ....modeling_utils import ModuleUtilsMixin
from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings


__a  :					Optional[int]          =  logging.get_logger(__name__)

__a  :					Dict          =  """MMBTConfig"""




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> int:

							'''simple docstring'''


							super().__init__()
							__lowercase			      =							config
							__lowercase			      =							encoder
							__lowercase			      =							nn.Linear(config.modal_hidden_size				,       config.hidden_size						)
							__lowercase			      =							embeddings.position_embeddings
							__lowercase			      =							embeddings.token_type_embeddings
							__lowercase			      =							embeddings.word_embeddings
							__lowercase			      =							embeddings.LayerNorm
							__lowercase			      =							nn.Dropout(p=config.hidden_dropout_prob						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase			      =							self.proj_embeddings(self.encoder(lowerCAmelCase__						)						)
							__lowercase			      =							token_embeddings.size(1						)

							if start_token is not None:
											__lowercase			      =							self.word_embeddings(lowerCAmelCase__						)
											seq_length += 1
											__lowercase			      =							torch.cat([start_token_embeds.unsqueeze(1						), token_embeddings]				,       dim=1						)

							if end_token is not None:
											__lowercase			      =							self.word_embeddings(lowerCAmelCase__						)
											seq_length += 1
											__lowercase			      =							torch.cat([token_embeddings, end_token_embeds.unsqueeze(1						)]				,       dim=1						)

							if position_ids is None:
											__lowercase			      =							torch.arange(lowerCAmelCase__				,       dtype=torch.long				,       device=input_modal.device						)
											__lowercase			      =							position_ids.unsqueeze(0						).expand(input_modal.size(0						)				,       lowerCAmelCase__						)

							if token_type_ids is None:
											__lowercase			      =							torch.zeros(
											    (input_modal.size(0						), seq_length)				,       dtype=torch.long				,       device=input_modal.device						)

							__lowercase			      =							self.position_embeddings(lowerCAmelCase__						)
							__lowercase			      =							self.token_type_embeddings(lowerCAmelCase__						)
							__lowercase			      =							token_embeddings + position_embeddings + token_type_embeddings
							__lowercase			      =							self.LayerNorm(lowerCAmelCase__						)
							__lowercase			      =							self.dropout(lowerCAmelCase__						)
							return embeddings


__a  :					Dict          =  r"""
    MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and
    Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
    It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and
    obtain state-of-the-art performance on various multimodal classification benchmark tasks.

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration.
        transformer (`nn.Module`): A text transformer that is used by MMBT.
            It should have embeddings, encoder, and pooler attributes.
        encoder (`nn.Module`): Encoder for the second modality.
            It should take in a batch of modal inputs and return k, n dimension embeddings.
"""

__a  :					List[Any]          =  r"""
    Args:
        input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):
            The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image
            Encoder, the shape would be (batch_size, channels, height, width)
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's
            appended to the end of other modality embeddings. Indices can be obtained using [`AutoTokenizer`]. See
            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification
            tasks.
        modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.
        attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:
            Segment token indices to indicate different portions of the non-text modality. The embeddings from these
            tokens will be summed with the respective token embeddings for the non-text modality.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.

            [What are position IDs?](../glossary#position-ids)
        modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.
            Selected in the range `[0, config.max_position_embeddings - 1]`.

            [What are position IDs?](../glossary#position-ids)
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""




@add_start_docstrings(
    '''The bare MMBT Model outputting raw hidden-states without any specific head on top.'''						,_UpperCAmelCase						,)
class        _UpperCamelCase				(						nn.Module						,_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Optional[int]:

							'''simple docstring'''


							super().__init__()
							__lowercase			      =							config
							__lowercase			      =							transformer
							__lowercase			      =							ModalEmbeddings(lowerCAmelCase__				,       lowerCAmelCase__				,       transformer.embeddings						)





			@add_start_docstrings_to_model_forward(lowerCAmelCase__						)
			@replace_return_docstrings(output_type=lowerCAmelCase__				,       config_class=_CONFIG_FOR_DOC						)
			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       )					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							output_attentions if output_attentions is not None else self.config.output_attentions
							__lowercase			      =							(
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							__lowercase			      =							return_dict if return_dict is not None else self.config.use_return_dict

							if input_ids is not None and inputs_embeds is not None:
											raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''						)
							elif input_ids is not None:
											__lowercase			      =							input_ids.size()
							elif inputs_embeds is not None:
											__lowercase			      =							inputs_embeds.size()[:-1]
							else:
											raise ValueError('''You have to specify either input_ids or inputs_embeds'''						)

							__lowercase			      =							input_ids.device if input_ids is not None else inputs_embeds.device

							__lowercase			      =							self.modal_encoder(
							    lowerCAmelCase__				,       start_token=lowerCAmelCase__				,       end_token=lowerCAmelCase__				,       position_ids=lowerCAmelCase__				,       token_type_ids=lowerCAmelCase__				,       )

							__lowercase			      =							modal_embeddings.size()[:-1]

							if token_type_ids is None:
											__lowercase			      =							torch.ones(lowerCAmelCase__				,       dtype=torch.long				,       device=lowerCAmelCase__						)

							__lowercase			      =							self.transformer.embeddings(
							    input_ids=lowerCAmelCase__				,       position_ids=lowerCAmelCase__				,       token_type_ids=lowerCAmelCase__				,       inputs_embeds=lowerCAmelCase__						)

							__lowercase			      =							torch.cat([modal_embeddings, txt_embeddings]				,       1						)

							__lowercase			      =							embedding_output.size()[:-1]

							if attention_mask is None:
											__lowercase			      =							torch.ones(lowerCAmelCase__				,       device=lowerCAmelCase__						)
							else:
											__lowercase			      =							torch.cat(
											    [torch.ones(lowerCAmelCase__				,       device=lowerCAmelCase__				,       dtype=torch.long						), attention_mask]				,       dim=1						)
							if encoder_attention_mask is None:
											__lowercase			      =							torch.ones(lowerCAmelCase__				,       device=lowerCAmelCase__						)
							else:
											__lowercase			      =							torch.cat(
											    [torch.ones(lowerCAmelCase__				,       device=lowerCAmelCase__						), encoder_attention_mask]				,       dim=1						)

							__lowercase			      =							self.get_extended_attention_mask(lowerCAmelCase__				,       lowerCAmelCase__						)
							__lowercase			      =							self.invert_attention_mask(lowerCAmelCase__						)
							__lowercase			      =							self.get_head_mask(lowerCAmelCase__				,       self.config.num_hidden_layers						)

							__lowercase			      =							self.transformer.encoder(
							    lowerCAmelCase__				,       attention_mask=lowerCAmelCase__				,       head_mask=lowerCAmelCase__				,       encoder_hidden_states=lowerCAmelCase__				,       encoder_attention_mask=lowerCAmelCase__				,       output_attentions=lowerCAmelCase__				,       output_hidden_states=lowerCAmelCase__				,       return_dict=lowerCAmelCase__				,       )

							__lowercase			      =							encoder_outputs[0]
							__lowercase			      =							self.transformer.pooler(lowerCAmelCase__						)

							if not return_dict:
											return (sequence_output, pooled_output) + encoder_outputs[1:]

							return BaseModelOutputWithPooling(
							    last_hidden_state=lowerCAmelCase__				,       pooler_output=lowerCAmelCase__				,       hidden_states=encoder_outputs.hidden_states				,       attentions=encoder_outputs.attentions				,       )





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							return self.embeddings.word_embeddings





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							value




@add_start_docstrings(
    '''
    MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)
    '''						,_UpperCAmelCase						,_UpperCAmelCase						,)
class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							__lowercase			      =							config.num_labels

							__lowercase			      =							MMBTModel(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)
							__lowercase			      =							nn.Dropout(config.hidden_dropout_prob						)
							__lowercase			      =							nn.Linear(config.hidden_size				,       config.num_labels						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       )					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							return_dict if return_dict is not None else self.config.use_return_dict

							__lowercase			      =							self.mmbt(
							    input_modal=lowerCAmelCase__				,       input_ids=lowerCAmelCase__				,       modal_start_tokens=lowerCAmelCase__				,       modal_end_tokens=lowerCAmelCase__				,       attention_mask=lowerCAmelCase__				,       token_type_ids=lowerCAmelCase__				,       modal_token_type_ids=lowerCAmelCase__				,       position_ids=lowerCAmelCase__				,       modal_position_ids=lowerCAmelCase__				,       head_mask=lowerCAmelCase__				,       inputs_embeds=lowerCAmelCase__				,       return_dict=lowerCAmelCase__				,       )

							__lowercase			      =							outputs[1]

							__lowercase			      =							self.dropout(lowerCAmelCase__						)
							__lowercase			      =							self.classifier(lowerCAmelCase__						)

							__lowercase			      =							None
							if labels is not None:
											if self.num_labels == 1:
															#  We are doing regression
															__lowercase			      =							MSELoss()
															__lowercase			      =							loss_fct(logits.view(-1						)				,       labels.view(-1						)						)
											else:
															__lowercase			      =							CrossEntropyLoss()
															__lowercase			      =							loss_fct(logits.view(-1				,       self.num_labels						)				,       labels.view(-1						)						)

							if not return_dict:
											__lowercase			      =							(logits,) + outputs[2:]
											return ((loss,) + output) if loss is not None else output

							return SequenceClassifierOutput(
							    loss=lowerCAmelCase__				,       logits=lowerCAmelCase__				,       hidden_states=outputs.hidden_states				,       attentions=outputs.attentions				,       )
import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


__a  :					List[Any]          =  {0: """null""", 1: """short""", 2: """long""", 3: """yes""", 4: """no"""}
__a  :					List[Any]          =  set("""""".join(["""‘""", """’""", """´""", """`""", """.""", """,""", """-""", """\""""]))





def     UpperCAmelCase						(   lowercase							,      lowercase=0							,      lowercase=None							):







				"""simple docstring"""



				return [" ".join(x.split(''' '''							)[begin:end]							) for x in answers if len(x.split(''' '''							)							) > 1]





def     UpperCAmelCase						(   lowercase							,      lowercase=False							):







				"""simple docstring"""



				if make_sub_answers:
								# if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
								# *e.g.* if the correct answer contains a prefix such as "the", or "a"
								__lowercase			      =							(
								    given_answers + get_sub_answers(lowercase							,      begin=1							) + get_sub_answers(lowercase							,      end=-1							)
								)
				__lowercase			      =							[]
				for answer in given_answers:
								__lowercase			      =							answer.replace('''_'''							,      ''' '''							).lower()
								__lowercase			      =							''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias							)
								answers.append(''' '''.join(alias.split()							).strip()							)
				return set(lowercase							)





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase=1							,      lowercase=100							):







				"""simple docstring"""



				__lowercase      ,					__lowercase			      =							jax.lax.top_k(lowercase							,      lowercase							)
				__lowercase      ,					__lowercase			      =							jax.lax.top_k(lowercase							,      lowercase							)

				__lowercase			      =							best_end_idx[:, None] - best_start_idx[None, :]
				__lowercase			      =							jnp.logical_or(widths < 0							,      widths > max_size							)
				__lowercase			      =							(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
				__lowercase			      =							jnp.argmax(lowercase							).item()

				return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							sample['''question''']['''text''']
				__lowercase			      =							sample['''document''']['''tokens''']['''token''']
				__lowercase			      =							sample['''document''']['''tokens''']['''is_html''']
				__lowercase			      =							sample['''annotations''']['''long_answer''']
				__lowercase			      =							sample['''annotations''']['''short_answers''']

				__lowercase			      =							''' '''.join([context[i] for i in range(len(lowercase							)							) if not is_html[i]]							)

				# 0 - No ; 1 - Yes
				for answer in sample["annotations"]["yes_no_answer"]:
								if answer == 0 or answer == 1:
												return {
												    "question": question,
												    "context": context_string,
												    "short": [],
												    "long": [],
												    "category": "no" if answer == 0 else "yes",
												}

				__lowercase			      =							[]
				for s in short_answers:
								short_targets.extend(s['''text''']							)
				__lowercase			      =							list(set(lowercase							)							)

				__lowercase			      =							[]
				for s in long_answers:
								if s["start_token"] == -1:
												continue
								__lowercase			      =							context[s['''start_token'''] : s['''end_token''']]
								__lowercase			      =							is_html[s['''start_token'''] : s['''end_token''']]
								__lowercase			      =							''' '''.join([answer[i] for i in range(len(lowercase							)							) if not html[i]]							)
								if new_answer not in long_targets:
												long_targets.append(lowercase							)

				__lowercase			      =							'''long_short''' if len(short_targets + long_targets							) > 0 else '''null'''

				return {
				    "question": question,
				    "context": context_string,
				    "short": short_targets,
				    "long": long_targets,
				    "category": category,
				}





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							load_from_disk('''natural-questions-validation'''							)
				__lowercase			      =							dataset.map(lowercase							).remove_columns(['''annotations''', '''document''', '''id''']							)
				print(lowercase							)

				__lowercase			      =							dataset.filter(lambda lowercase							: (len(x['''question''']							) + len(x['''context''']							)) < 4 * 4096							)
				__lowercase			      =							short_validation_dataset.filter(lambda lowercase							: x["category"] != "null"							)
				short_validation_dataset

				__lowercase			      =							'''vasudevgupta/flax-bigbird-natural-questions'''
				__lowercase			      =							FlaxBigBirdForNaturalQuestions.from_pretrained(lowercase							)
				__lowercase			      =							BigBirdTokenizerFast.from_pretrained(lowercase							)

				@jax.jit
				def forward(*lowercase							,      **lowercase							):
								__lowercase      ,					__lowercase      ,					__lowercase			      =							model(*lowercase							,      **lowercase							)
								return start_logits, end_logits, jnp.argmax(lowercase							,      axis=-1							)

				def evaluate(lowercase							):
								# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
								__lowercase			      =							tokenizer(
								    example['''question''']							,      example['''context''']							,      return_tensors='''np'''							,      max_length=4096							,      padding='''max_length'''							,      truncation=lowercase							,      )

								__lowercase      ,					__lowercase      ,					__lowercase			      =							forward(**lowercase							)

								__lowercase			      =							CATEGORY_MAPPING[category.item()]

								__lowercase			      =							example['''long'''] + example['''short''']
								if example["category"] in ["yes", "no", "null"]:
												__lowercase			      =							[example['''category''']]
								__lowercase			      =							example['''category'''] != '''null'''
								# Now target can be: "yes", "no", "null", "list of long & short answers"

								if predicted_category in ["yes", "no", "null"]:
												__lowercase			      =							[predicted_category]
												__lowercase			      =							example['''output'''] == example['''targets''']
												__lowercase			      =							predicted_category != '''null'''
												return example

								__lowercase			      =							38 if predicted_category == '''short''' else 1024
								__lowercase      ,					__lowercase			      =							get_best_valid_start_end_idx(
								    start_scores[0]							,      end_scores[0]							,      top_k=8							,      max_size=lowercase							)

								__lowercase			      =							inputs['''input_ids'''][0].tolist()
								__lowercase			      =							[tokenizer.decode(input_ids[start_score : end_score + 1]							)]

								__lowercase			      =							expand_to_aliases(example['''targets''']							,      make_sub_answers=lowercase							)
								__lowercase			      =							expand_to_aliases(example['''output''']							)

								# some preprocessing to both prediction and answer
								__lowercase			      =							{''''''.join(a.split()							) for a in answers}
								__lowercase			      =							{''''''.join(p.split()							) for p in predictions}
								__lowercase			      =							{s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

								# if there is a common element, it's a exact match
								__lowercase			      =							len(list(answers & predictions							)							) > 0
								__lowercase			      =							predicted_category != '''null''' and len(lowercase							) > 0

								return example

				__lowercase			      =							short_validation_dataset.map(lowercase							)

				__lowercase			      =							len(lowercase							)
				__lowercase			      =							len(short_validation_dataset.filter(lambda lowercase							: x["match"] == 1							)							)
				print('''EM score:'''							,      (matched / total) * 100							,      '''%'''							)


if __name__ == "__main__":
		main()