






'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel


@require_tf
class        _UpperCAmelCase						:
    __lowerCamelCase:   List[Any]		       =    BlenderbotConfig
    __lowerCamelCase:   Dict		       =    {}
    __lowerCamelCase:   int		       =    'gelu'




    def __init__(			self  :       Optional[Any]  ,     a  :       Union[str, Any]  ,     a  :       List[Any]=1_3  ,     a  :       Dict=7  ,     a  :       Dict=True  ,     a  :       Optional[int]=False  ,     a  :       int=9_9  ,     a  :       List[str]=3_2  ,     a  :       int=2  ,     a  :       Tuple=4  ,     a  :       Optional[Any]=3_7  ,     a  :       str=0.1  ,     a  :       Dict=0.1  ,     a  :       Optional[int]=2_0  ,     a  :       str=2  ,     a  :       int=1  ,     a  :       int=0  ,     ):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     parent
       lowercase_	:    Optional[int]           =     batch_size
       lowercase_	:    int           =     seq_length
       lowercase_	:    Dict           =     is_training
       lowercase_	:    List[str]           =     use_labels
       lowercase_	:    Union[str, Any]           =     vocab_size
       lowercase_	:    int           =     hidden_size
       lowercase_	:    Optional[int]           =     num_hidden_layers
       lowercase_	:    str           =     num_attention_heads
       lowercase_	:    List[Any]           =     intermediate_size

       lowercase_	:    Optional[int]           =     hidden_dropout_prob
       lowercase_	:    List[Any]           =     attention_probs_dropout_prob
       lowercase_	:    Any           =     max_position_embeddings
       lowercase_	:    Dict           =     eos_token_id
       lowercase_	:    List[Any]           =     pad_token_id
       lowercase_	:    Optional[Any]           =     bos_token_id




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






       '''simple docstring'''






       lowercase_	:    Any           =     ids_tensor([self.batch_size, self.seq_length - 1]  ,     self.vocab_size			)
       lowercase_	:    int           =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)  ,     1			)
       lowercase_	:    int           =     tf.concat([input_ids, eos_tensor]  ,     axis=1			)

       lowercase_	:    int           =     ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size			)

       lowercase_	:    Tuple           =     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  ,     )
       lowercase_	:    Union[str, Any]           =     prepare_blenderbot_inputs_dict(a  ,     a  ,     a			)
       return config, inputs_dict





    def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       Any  ,     a  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     TFBlenderbotModel(config=a			).get_decoder()
       lowercase_	:    List[str]           =     inputs_dict["input_ids"]

       lowercase_	:    List[Any]           =     input_ids[:1, :]
       lowercase_	:    int           =     inputs_dict["attention_mask"][:1, :]
       lowercase_	:    Any           =     inputs_dict["head_mask"]
       lowercase_	:    List[Any]           =     1

       # first forward pass
       lowercase_	:    Optional[Any]           =     model(a  ,     attention_mask=a  ,     head_mask=a  ,     use_cache=a			)

       lowercase_	:    int           =     outputs.to_tuple()

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

       # append to next input_ids and
       lowercase_	:    List[Any]           =     tf.concat([input_ids, next_tokens]  ,     axis=-1			)
       lowercase_	:    str           =     tf.concat([attention_mask, next_attn_mask]  ,     axis=-1			)

       lowercase_	:    List[Any]           =     model(a  ,     attention_mask=a			)[0]
       lowercase_	:    Union[str, Any]           =     model(a  ,     attention_mask=a  ,     past_key_values=a			)[0]

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

       # select random slice
       lowercase_	:    List[Any]           =     int(ids_tensor((1,)  ,     output_from_past.shape[-1]			)			)
       lowercase_	:    Union[str, Any]           =     output_from_no_past[:, -3:, random_slice_idx]
       lowercase_	:    Optional[int]           =     output_from_past[:, :, random_slice_idx]

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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		):


   """simple docstring"""




   if attention_mask is None:
      lowercase_	:    Dict           =     tf.cast(tf.math.not_equal(_UpperCamelCase						,		config.pad_token_id							)						,		tf.inta							)
   if decoder_attention_mask is None:
      lowercase_	:    List[Any]           =     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:
      lowercase_	:    Any           =     tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
   if decoder_head_mask is None:
      lowercase_	:    Dict           =     tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
   if cross_attn_head_mask is None:
      lowercase_	:    Dict           =     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        _UpperCAmelCase						(		snake_case				,					snake_case				,					unittest.TestCase			):
    __lowerCamelCase:   Union[str, Any]		       =    (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
    __lowerCamelCase:   Optional[Any]		       =    (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
    __lowerCamelCase:   Any		       =    (
        {
            'conversational': TFBlenderbotForConditionalGeneration,
            'feature-extraction': TFBlenderbotModel,
            'summarization': TFBlenderbotForConditionalGeneration,
            'text2text-generation': TFBlenderbotForConditionalGeneration,
            'translation': TFBlenderbotForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    __lowerCamelCase:   Dict		       =    True
    __lowerCamelCase:   List[str]		       =    False
    __lowerCamelCase:   int		       =    False




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     TFBlenderbotModelTester(self			)
       lowercase_	:    Union[str, Any]           =     ConfigTester(self  ,     config_class=a			)




    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       self.config_tester.run_common_tests()





    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*a			)


@require_tokenizers
@require_tf
class        _UpperCAmelCase						(		unittest.TestCase			):
    __lowerCamelCase:   Any		       =    ['My friends are cool but they eat too many carbs.']
    __lowerCamelCase:   Tuple		       =    'facebook/blenderbot-400M-distill'




    @cached_property
    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       return BlenderbotTokenizer.from_pretrained(self.model_name			)




    @cached_property
    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       lowercase_	:    str           =     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
       return model





    @slow
    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     self.tokenizer(self.src_text  ,     return_tensors="tf"			)
       lowercase_	:    Dict           =     self.model.generate(
           model_inputs.input_ids  ,     )
       lowercase_	:    List[str]           =     self.tokenizer.batch_decode(generated_ids.numpy()  ,     skip_special_tokens=a			)[0]
       assert (
           generated_words
           == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
       )












'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


class        _UpperCAmelCase						(		snake_case			):




    def __init__(			self  :       Tuple  ,     a  :       NestedDataStructureLike[PathLike]  ,     a  :       Optional[NamedSplit] = None  ,     a  :       Optional[Features] = None  ,     a  :       str = None  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       Optional[str] = None  ,     a  :       Optional[int] = None  ,     **a  :       List[Any]  ,     ):






       '''simple docstring'''






       super().__init__(
           a  ,     split=a  ,     features=a  ,     cache_dir=a  ,     keep_in_memory=a  ,     streaming=a  ,     num_proc=a  ,     **a  ,     )
       lowercase_	:    str           =     field
       lowercase_	:    Optional[Any]           =     path_or_paths if isinstance(a  ,     a			) else {self.split: path_or_paths}
       lowercase_	:    Any           =     Json(
           cache_dir=a  ,     data_files=a  ,     features=a  ,     field=a  ,     **a  ,     )





    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       if self.streaming:
          lowercase_	:    Any           =     self.builder.as_streaming_dataset(split=self.split			)
       # Build regular (map-style) dataset
       else:
          lowercase_	:    Dict           =     None
          lowercase_	:    Optional[int]           =     None
          lowercase_	:    str           =     None
          lowercase_	:    str           =     None

          self.builder.download_and_prepare(
              download_config=a  ,     download_mode=a  ,     verification_mode=a  ,     base_path=a  ,     num_proc=self.num_proc  ,     )
          lowercase_	:    int           =     self.builder.as_dataset(
              split=self.split  ,     verification_mode=a  ,     in_memory=self.keep_in_memory			)
       return dataset


class        _UpperCAmelCase						:




    def __init__(			self  :       str  ,     a  :       Dataset  ,     a  :       Union[PathLike, BinaryIO]  ,     a  :       Optional[int] = None  ,     a  :       Optional[int] = None  ,     **a  :       List[Any]  ,     ):






       '''simple docstring'''






       if num_proc is not None and num_proc <= 0:
          raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""			)

       lowercase_	:    Dict           =     dataset
       lowercase_	:    Optional[int]           =     path_or_buf
       lowercase_	:    List[Any]           =     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       lowercase_	:    Optional[Any]           =     num_proc
       lowercase_	:    List[Any]           =     "utf-8"
       lowercase_	:    List[str]           =     to_json_kwargs




    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    str           =     self.to_json_kwargs.pop("path_or_buf"  ,     a			)
       lowercase_	:    Any           =     self.to_json_kwargs.pop("orient"  ,     "records"			)
       lowercase_	:    Any           =     self.to_json_kwargs.pop("lines"  ,     True if orient == "records" else False			)
       lowercase_	:    List[str]           =     self.to_json_kwargs.pop("index"  ,     False if orient in ["split", "table"] else True			)
       lowercase_	:    int           =     self.to_json_kwargs.pop("compression"  ,     a			)

       if compression not in [None, "infer", "gzip", "bz2", "xz"]:
          raise NotImplementedError(f"""`datasets` currently does not support {compression} compression"""			)

       if isinstance(self.path_or_buf  ,     (str, bytes, os.PathLike)			):
          with fsspec.open(self.path_or_buf  ,     "wb"  ,     compression=a			) as buffer:
             lowercase_	:    Dict           =     self._write(file_obj=a  ,     orient=a  ,     lines=a  ,     index=a  ,     **self.to_json_kwargs			)
       else:
          if compression:
             raise NotImplementedError(
                 f"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
                 " was passed. Please provide a local path instead."			)
          lowercase_	:    Dict           =     self._write(
              file_obj=self.path_or_buf  ,     orient=a  ,     lines=a  ,     index=a  ,     **self.to_json_kwargs			)
       return written




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       List[str]			):






       '''simple docstring'''






       lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_	:    List[Any]           =     args

       lowercase_	:    Optional[int]           =     query_table(
           table=self.dataset.data  ,     key=slice(a  ,     offset + self.batch_size			)  ,     indices=self.dataset._indices  ,     )
       lowercase_	:    Dict           =     batch.to_pandas().to_json(
           path_or_buf=a  ,     orient=a  ,     lines=a  ,     index=a  ,     **a			)
       if not json_str.endswith("\n"			):
          json_str += "\n"
       return json_str.encode(self.encoding			)





    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       BinaryIO  ,     a  :       int  ,     a  :       str  ,     a  :       Union[str, Any]  ,     **a  :       str  ,     ):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     0

       if self.num_proc is None or self.num_proc == 1:
          for offset in logging.tqdm(
              range(0  ,     len(self.dataset			)  ,     self.batch_size			)  ,     unit="ba"  ,     disable=not logging.is_progress_bar_enabled()  ,     desc="Creating json from Arrow format"  ,     ):
             lowercase_	:    Dict           =     self._batch_json((offset, orient, lines, index, to_json_kwargs)			)
             written += file_obj.write(a			)
       else:
          lowercase_    ,		lowercase_	:    Any           =     len(self.dataset			), self.batch_size
          with multiprocessing.Pool(self.num_proc			) as pool:
             for json_str in logging.tqdm(
                 pool.imap(
                     self._batch_json  ,     [(offset, orient, lines, index, to_json_kwargs) for offset in range(0  ,     a  ,     a			)]  ,     )  ,     total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size  ,     unit="ba"  ,     disable=not logging.is_progress_bar_enabled()  ,     desc="Creating json from Arrow format"  ,     ):
                written += file_obj.write(a			)

       return written




