import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import (
         MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
     )

if is_tf_available() and is_tensorflow_probability_available():
     import tensorflow as tf
     import tensorflow_probability as tfp

     from ..models.auto.modeling_tf_auto import (
         TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
     )







class       __snake_case (  lowerCAmelCase__ ):
 def __call__(	self		, snake_case__=None		, snake_case__=None		, **snake_case__    )						->				Tuple:
       '''simple docstring'''





       requires_backends(self		, '''pandas'''    )
       import pandas as pd

       if table is None:
             raise ValueError('''Keyword argument `table` cannot be None.'''    )
       elif query is None:
             if isinstance(lowercase__		, lowercase__    ) and table.get('''query'''    ) is not None and table.get('''table'''    ) is not None:
                   UpperCAmelCase     :			Dict     	=[table]
             elif isinstance(lowercase__		, lowercase__    ) and len(lowercase__    ) > 0:
                   if not all(isinstance(lowercase__		, lowercase__    ) for d in table    ):
                         raise ValueError(
                             f'''Keyword argument `table` should be a list of dict, but is {(type(lowercase__    ) for d in table)}'''    )

                   if table[0].get('''query'''    ) is not None and table[0].get('''table'''    ) is not None:
                         UpperCAmelCase     :			List[Any]     	=table
                   else:
                         raise ValueError(
                             '''If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`'''
                             f''' and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys.'''    )
             elif Dataset is not None and isinstance(lowercase__		, lowercase__    ) or isinstance(lowercase__		, types.GeneratorType    ):
                   return table
             else:
                   raise ValueError(
                       '''Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '''
                       f'''is {type(lowercase__    )})'''    )
       else:
             UpperCAmelCase     :			List[Any]     	=[{'''table''': table, '''query''': query}]

       for tqa_pipeline_input in tqa_pipeline_inputs:
             if not isinstance(tqa_pipeline_input['''table''']		, pd.DataFrame    ):
                   if tqa_pipeline_input["table"] is None:
                         raise ValueError('''Table cannot be None.'''    )

                   UpperCAmelCase     :			Dict     	=pd.DataFrame(tqa_pipeline_input['''table''']    )

       return tqa_pipeline_inputs







@add_end_docstrings(lowerCAmelCase__ )
class       __snake_case (  lowerCAmelCase__ ):
 __lowerCamelCase     : List[Any] 	=   'table,query'
 def __init__(	self		, snake_case__=TableQuestionAnsweringArgumentHandler()		, *snake_case__		, **snake_case__    )						->				Optional[int]:
       '''simple docstring'''





       super().__init__(*lowercase__		, **lowercase__    )
       UpperCAmelCase     :			List[Any]     	=args_parser

       self.check_model_type(
           dict(
               TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
               + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()    )
           if self.framework == '''tf'''
           else dict(
               MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()    )    )

       UpperCAmelCase     :			Optional[int]     	=bool(getattr(self.model.config		, '''aggregation_labels'''		, lowercase__    )    ) and bool(
           getattr(self.model.config		, '''num_aggregation_labels'''		, lowercase__    )    )
       UpperCAmelCase     :			int     	='''tapas''' if hasattr(self.model.config		, '''aggregation_labels'''    ) else None
 def 						UpperCAmelCase__							(	self		, **snake_case__    )						->				str:
       '''simple docstring'''





       return self.model(**lowercase__    )
 def 						UpperCAmelCase__							(	self		, **snake_case__    )						->				Tuple:
       '''simple docstring'''





       if self.framework == "pt":
             UpperCAmelCase     :			List[str]     	=[]
             UpperCAmelCase     :			Any     	=[]
             UpperCAmelCase     :			Optional[Any]     	=None
             UpperCAmelCase     :			Optional[Any]     	=inputs['''input_ids'''].shape[0]

             UpperCAmelCase     :			str     	=inputs['''input_ids'''].to(self.device    )
             UpperCAmelCase     :			List[str]     	=inputs['''attention_mask'''].to(self.device    )
             UpperCAmelCase     :			int     	=inputs['''token_type_ids'''].to(self.device    )
             UpperCAmelCase     :			str     	=None

             for index in range(lowercase__    ):
                   # If sequences have already been processed, the token type IDs will be created according to the previous
                   # answer.
                   if prev_answers is not None:
                         UpperCAmelCase     :			List[str]     	=token_type_ids_example[:, 3]  # shape (seq_len,)
                         UpperCAmelCase     :			Any     	=np.zeros_like(prev_labels_example.cpu().numpy()    )  # shape (seq_len,)

                         UpperCAmelCase     :			int     	=token_type_ids[index]  # shape (seq_len, 7)
                         for i in range(model_labels.shape[0]    ):
                               UpperCAmelCase     :			Union[str, Any]     	=token_type_ids_example[:, 0].tolist()[i]
                               UpperCAmelCase     :			int     	=token_type_ids_example[:, 1].tolist()[i] - 1
                               UpperCAmelCase     :			Union[str, Any]     	=token_type_ids_example[:, 2].tolist()[i] - 1

                               if row_id >= 0 and col_id >= 0 and segment_id == 1:
                                     UpperCAmelCase     :			Optional[Any]     	=int(prev_answers[(col_id, row_id)]    )

                         UpperCAmelCase     :			Tuple     	=torch.from_numpy(lowercase__    ).type(torch.long    ).to(self.device    )

                   UpperCAmelCase     :			List[Any]     	=input_ids[index]
                   UpperCAmelCase     :			int     	=attention_mask[index]  # shape (seq_len,)
                   UpperCAmelCase     :			Optional[int]     	=token_type_ids[index]  # shape (seq_len, 7)
                   UpperCAmelCase     :			List[Any]     	=self.model(
                       input_ids=input_ids_example.unsqueeze(0    )		, attention_mask=attention_mask_example.unsqueeze(0    )		, token_type_ids=token_type_ids_example.unsqueeze(0    )		, )
                   UpperCAmelCase     :			Any     	=outputs.logits

                   if self.aggregate:
                         all_aggregations.append(outputs.logits_aggregation    )

                   all_logits.append(lowercase__    )

                   UpperCAmelCase     :			Optional[int]     	=torch.distributions.Bernoulli(logits=lowercase__    )
                   UpperCAmelCase     :			List[str]     	=dist_per_token.probs * attention_mask_example.type(torch.floataa    ).to(
                       dist_per_token.probs.device    )

                   UpperCAmelCase     :			Optional[int]     	=collections.defaultdict(lowercase__    )
                   for i, p in enumerate(probabilities.squeeze().tolist()    ):
                         UpperCAmelCase     :			Union[str, Any]     	=token_type_ids_example[:, 0].tolist()[i]
                         UpperCAmelCase     :			Union[str, Any]     	=token_type_ids_example[:, 1].tolist()[i] - 1
                         UpperCAmelCase     :			Dict     	=token_type_ids_example[:, 2].tolist()[i] - 1
                         if col >= 0 and row >= 0 and segment_id == 1:
                               coords_to_probs[(col, row)].append(lowercase__    )

                   UpperCAmelCase     :			Any     	={key: np.array(coords_to_probs[key]    ).mean() > 0.5 for key in coords_to_probs}

             UpperCAmelCase     :			Optional[Any]     	=torch.cat(tuple(lowercase__    )		, 0    )

             return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(lowercase__    )		, 0    ))
       else:
             UpperCAmelCase     :			Union[str, Any]     	=[]
             UpperCAmelCase     :			List[str]     	=[]
             UpperCAmelCase     :			Dict     	=None
             UpperCAmelCase     :			List[Any]     	=inputs['''input_ids'''].shape[0]

             UpperCAmelCase     :			Dict     	=inputs['''input_ids''']
             UpperCAmelCase     :			Optional[Any]     	=inputs['''attention_mask''']
             UpperCAmelCase     :			int     	=inputs['''token_type_ids'''].numpy()
             UpperCAmelCase     :			Optional[Any]     	=None

             for index in range(lowercase__    ):
                   # If sequences have already been processed, the token type IDs will be created according to the previous
                   # answer.
                   if prev_answers is not None:
                         UpperCAmelCase     :			str     	=token_type_ids_example[:, 3]  # shape (seq_len,)
                         UpperCAmelCase     :			str     	=np.zeros_like(lowercase__		, dtype=np.intaa    )  # shape (seq_len,)

                         UpperCAmelCase     :			Tuple     	=token_type_ids[index]  # shape (seq_len, 7)
                         for i in range(model_labels.shape[0]    ):
                               UpperCAmelCase     :			Any     	=token_type_ids_example[:, 0].tolist()[i]
                               UpperCAmelCase     :			str     	=token_type_ids_example[:, 1].tolist()[i] - 1
                               UpperCAmelCase     :			str     	=token_type_ids_example[:, 2].tolist()[i] - 1

                               if row_id >= 0 and col_id >= 0 and segment_id == 1:
                                     UpperCAmelCase     :			List[str]     	=int(prev_answers[(col_id, row_id)]    )

                         UpperCAmelCase     :			List[str]     	=model_labels

                   UpperCAmelCase     :			List[Any]     	=input_ids[index]
                   UpperCAmelCase     :			Dict     	=attention_mask[index]  # shape (seq_len,)
                   UpperCAmelCase     :			List[str]     	=token_type_ids[index]  # shape (seq_len, 7)
                   UpperCAmelCase     :			Optional[Any]     	=self.model(
                       input_ids=np.expand_dims(lowercase__		, axis=0    )		, attention_mask=np.expand_dims(lowercase__		, axis=0    )		, token_type_ids=np.expand_dims(lowercase__		, axis=0    )		, )
                   UpperCAmelCase     :			int     	=outputs.logits

                   if self.aggregate:
                         all_aggregations.append(outputs.logits_aggregation    )

                   all_logits.append(lowercase__    )

                   UpperCAmelCase     :			str     	=tfp.distributions.Bernoulli(logits=lowercase__    )
                   UpperCAmelCase     :			Optional[int]     	=dist_per_token.probs_parameter() * tf.cast(lowercase__		, tf.floataa    )

                   UpperCAmelCase     :			Optional[int]     	=collections.defaultdict(lowercase__    )
                   UpperCAmelCase     :			Dict     	=token_type_ids_example
                   for i, p in enumerate(tf.squeeze(lowercase__    ).numpy().tolist()    ):
                         UpperCAmelCase     :			List[Any]     	=token_type_ids_example[:, 0].tolist()[i]
                         UpperCAmelCase     :			str     	=token_type_ids_example[:, 1].tolist()[i] - 1
                         UpperCAmelCase     :			List[str]     	=token_type_ids_example[:, 2].tolist()[i] - 1
                         if col >= 0 and row >= 0 and segment_id == 1:
                               coords_to_probs[(col, row)].append(lowercase__    )

                   UpperCAmelCase     :			Dict     	={key: np.array(coords_to_probs[key]    ).mean() > 0.5 for key in coords_to_probs}

             UpperCAmelCase     :			str     	=tf.concat(tuple(lowercase__    )		, 0    )

             return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(lowercase__    )		, 0    ))
 def __call__(	self		, *snake_case__		, **snake_case__    )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	=self._args_parser(*lowercase__		, **lowercase__    )

       UpperCAmelCase     :			Optional[int]     	=super().__call__(lowercase__		, **lowercase__    )
       if len(lowercase__    ) == 1:
             return results[0]
       return results
 def 						UpperCAmelCase__							(	self		, snake_case__=None		, snake_case__=None		, snake_case__=None		, **snake_case__    )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			int     	={}
       if padding is not None:
             UpperCAmelCase     :			List[Any]     	=padding
       if truncation is not None:
             UpperCAmelCase     :			Optional[int]     	=truncation

       UpperCAmelCase     :			Union[str, Any]     	={}
       if sequential is not None:
             UpperCAmelCase     :			Union[str, Any]     	=sequential
       return preprocess_params, forward_params, {}
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__=None		, snake_case__=True		, snake_case__=None    )						->				List[str]:
       '''simple docstring'''





       if truncation is None:
             if self.type == "tapas":
                   UpperCAmelCase     :			int     	='''drop_rows_to_fit'''
             else:
                   UpperCAmelCase     :			int     	='''do_not_truncate'''

       UpperCAmelCase	,    UpperCAmelCase     :			Optional[int]     	=pipeline_input['''table'''], pipeline_input['''query''']
       if table.empty:
             raise ValueError('''table is empty'''    )
       if query is None or query == "":
             raise ValueError('''query is empty'''    )
       UpperCAmelCase     :			Any     	=self.tokenizer(lowercase__		, lowercase__		, return_tensors=self.framework		, truncation=lowercase__		, padding=lowercase__    )
       UpperCAmelCase     :			str     	=table
       return inputs
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__=False    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=model_inputs.pop('''table'''    )

       if self.type == "tapas":
             if sequential:
                   UpperCAmelCase     :			Optional[Any]     	=self.sequential_inference(**lowercase__    )
             else:
                   UpperCAmelCase     :			int     	=self.batch_inference(**lowercase__    )
       else:
             UpperCAmelCase     :			Union[str, Any]     	=self.model.generate(**lowercase__    )
       UpperCAmelCase     :			Dict     	={'''model_inputs''': model_inputs, '''table''': table, '''outputs''': outputs}
       return model_outputs






 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=model_outputs['''model_inputs''']
       UpperCAmelCase     :			int     	=model_outputs['''table''']
       UpperCAmelCase     :			List[str]     	=model_outputs['''outputs''']
       if self.type == "tapas":
             if self.aggregate:
                   UpperCAmelCase	,    UpperCAmelCase     :			Optional[Any]     	=outputs[:2]
                   UpperCAmelCase     :			Any     	=self.tokenizer.convert_logits_to_predictions(lowercase__		, lowercase__		, lowercase__    )
                   UpperCAmelCase	,    UpperCAmelCase     :			Optional[int]     	=predictions
                   UpperCAmelCase     :			Optional[int]     	={i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(lowercase__    )}

                   UpperCAmelCase     :			Optional[Any]     	=self.model.config.no_aggregation_label_index
                   UpperCAmelCase     :			List[str]     	={
                       i: aggregators[i] + ''' > ''' for i, pred in enumerate(lowercase__    ) if pred != no_agg_label_index
                   }
             else:
                   UpperCAmelCase     :			Any     	=outputs[0]
                   UpperCAmelCase     :			str     	=self.tokenizer.convert_logits_to_predictions(lowercase__		, lowercase__    )
                   UpperCAmelCase     :			Dict     	=predictions[0]
                   UpperCAmelCase     :			int     	={}
                   UpperCAmelCase     :			List[str]     	={}
             UpperCAmelCase     :			Tuple     	=[]
             for index, coordinates in enumerate(lowercase__    ):
                   UpperCAmelCase     :			List[Any]     	=[table.iat[coordinate] for coordinate in coordinates]
                   UpperCAmelCase     :			str     	=aggregators.get(lowercase__		, ''''''    )
                   UpperCAmelCase     :			Optional[int]     	=aggregators_prefix.get(lowercase__		, ''''''    )
                   UpperCAmelCase     :			Any     	={
                       '''answer''': aggregator_prefix + ''', '''.join(lowercase__    ),
                       '''coordinates''': coordinates,
                       '''cells''': [table.iat[coordinate] for coordinate in coordinates],
                   }
                   if aggregator:
                         UpperCAmelCase     :			Tuple     	=aggregator

                   answers.append(lowercase__    )
             if len(lowercase__    ) == 0:
                   raise PipelineException('''Empty answer'''    )
       else:
             UpperCAmelCase     :			List[Any]     	=[{'''answer''': answer} for answer in self.tokenizer.batch_decode(lowercase__		, skip_special_tokens=lowercase__    )]

       return answers if len(lowercase__    ) > 1 else answers[0]








# Usage:
# ./gen-card-allenai-wmt19.py

import os
from pathlib import Path



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							{
		    "en": "Machine learning is great, isn't it?",
		    "ru": "Машинное обучение - это здорово, не так ли?",
		    "de": "Maschinelles Lernen ist großartig, nicht wahr?",
		}

		# BLUE scores as follows:
		# "pair": [fairseq, transformers]
		__A    =							{
		    "wmt19-de-en-6-6-base": [0, 38.37],
		    "wmt19-de-en-6-6-big": [0, 39.90],
		}
		__A    =							F"""{src_lang}-{tgt_lang}"""

		__A    =							F"""
---

language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

2 models are available:

* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"allenai/{model_name}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   |  transformers
-------|---------
{model_name}  |  {scores[model_name][1]}

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

"""
		model_card_dir.mkdir(parents=lowerCAmelCase__					,     exist_ok=lowerCAmelCase__	)
		__A    =							os.path.join(lowerCAmelCase__					,     "README.md"	)
		print(F"""Generating {path}"""	)
		with open(lowerCAmelCase__					,     "w"					,     encoding="utf-8"	) as f:
				f.write(lowerCAmelCase__	)


# make sure we are under the root of the project
snake_case_   :	Optional[int]							      =Path(__file__).resolve().parent.parent.parent
snake_case_   :	Dict							      =repo_dir / '''model_cards'''

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
					snake_case_   :	Union[str, Any]							      =model_cards_dir / '''allenai''' / model_name
					write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)

