





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Any							=   'blenderbot-small'
      A_						:    Optional[Any]							=   ['past_key_values']
      A_						:    Tuple							=   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


      def __init__(self	:				Tuple    ,      a__	:				List[Any]=5_0265    ,      a__	:				Any=512    ,      a__	:				Optional[int]=8    ,      a__	:				Dict=2048    ,      a__	:				Dict=16    ,      a__	:				Optional[Any]=8    ,      a__	:				int=2048    ,      a__	:				Any=16    ,      a__	:				List[Any]=0.0    ,      a__	:				str=0.0    ,      a__	:				Optional[Any]=True    ,      a__	:				List[Any]=True    ,      a__	:				List[Any]="gelu"    ,      a__	:				List[str]=512    ,      a__	:				List[str]=0.1    ,      a__	:				Union[str, Any]=0.0    ,      a__	:				List[str]=0.0    ,      a__	:				Union[str, Any]=0.0_2    ,      a__	:				Any=1    ,      a__	:				Any=False    ,      a__	:				Any=0    ,      a__	:				Union[str, Any]=1    ,      a__	:				Dict=2    ,      a__	:				Union[str, Any]=2    ,      **a__	:				Optional[Any]    ,      ):





         """simple docstring"""



         __snake_case								=       vocab_size
         __snake_case								=       max_position_embeddings
         __snake_case								=       d_model
         __snake_case								=       encoder_ffn_dim
         __snake_case								=       encoder_layers
         __snake_case								=       encoder_attention_heads
         __snake_case								=       decoder_ffn_dim
         __snake_case								=       decoder_layers
         __snake_case								=       decoder_attention_heads
         __snake_case								=       dropout
         __snake_case								=       attention_dropout
         __snake_case								=       activation_dropout
         __snake_case								=       activation_function
         __snake_case								=       init_std
         __snake_case								=       encoder_layerdrop
         __snake_case								=       decoder_layerdrop
         __snake_case								=       use_cache
         __snake_case								=       encoder_layers
         __snake_case								=       scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             pad_token_id=a__    ,      bos_token_id=a__    ,      eos_token_id=a__    ,      is_encoder_decoder=a__    ,      decoder_start_token_id=a__    ,      forced_eos_token_id=a__    ,      **a__    ,      )






class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      @property
      def        a			(self	:				Any				):





         """simple docstring"""



         if self.task in ["default", "seq2seq-lm"]:
            __snake_case								=       OrderedDict(
                [
                    ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ]				)

            if self.use_past:
               __snake_case								=       {0: '''batch'''}
               __snake_case								=       {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
            else:
               __snake_case								=       {0: '''batch''', 1: '''decoder_sequence'''}
               __snake_case								=       {0: '''batch''', 1: '''decoder_sequence'''}

            if self.use_past:
               self.fill_with_past_key_values_(a__    ,      direction='''inputs'''				)
         elif self.task == "causal-lm":
            # TODO: figure this case out.
            __snake_case								=       OrderedDict(
                [
                    ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ]				)
            if self.use_past:
               __snake_case	,     __snake_case								=       self.num_layers
               for i in range(a__				):
                  __snake_case								=       {0: '''batch''', 2: '''past_sequence + sequence'''}
                  __snake_case								=       {0: '''batch''', 2: '''past_sequence + sequence'''}
         else:
            __snake_case								=       OrderedDict(
                [
                    ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                    ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                ]				)

         return common_inputs


      @property
      def        a			(self	:				Any				):





         """simple docstring"""



         if self.task in ["default", "seq2seq-lm"]:
            __snake_case								=       super().outputs
         else:
            __snake_case								=       super(a__    ,      self				).outputs
            if self.use_past:
               __snake_case	,     __snake_case								=       self.num_layers
               for i in range(a__				):
                  __snake_case								=       {0: '''batch''', 2: '''past_sequence + sequence'''}
                  __snake_case								=       {0: '''batch''', 2: '''past_sequence + sequence'''}
         return common_outputs


      def        a			(self	:				Any    ,      a__	:				PreTrainedTokenizer    ,      a__	:				int = -1    ,      a__	:				int = -1    ,      a__	:				bool = False    ,      a__	:				Optional[TensorType] = None    ,      ):





         """simple docstring"""



         __snake_case								=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             a__    ,      a__    ,      a__    ,      a__    ,      a__				)

         # Generate decoder inputs
         __snake_case								=       seq_length if not self.use_past else 1
         __snake_case								=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             a__    ,      a__    ,      a__    ,      a__    ,      a__				)
         __snake_case								=       {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         __snake_case								=       dict(**a__    ,      **a__				)

         if self.use_past:
            if not is_torch_available():
               raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''				)
            else:
               import torch
            __snake_case	,     __snake_case								=       common_inputs['''input_ids'''].shape
            __snake_case								=       common_inputs['''decoder_input_ids'''].shape[1]
            __snake_case	,     __snake_case								=       self.num_attention_heads
            __snake_case								=       (
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            __snake_case								=       decoder_seq_length + 3
            __snake_case								=       (
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            __snake_case								=       torch.cat(
                [common_inputs['''decoder_attention_mask'''], torch.ones(a__    ,      a__				)]    ,      dim=1				)

            __snake_case								=       []
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            __snake_case	,     __snake_case								=       self.num_layers
            __snake_case								=       min(a__    ,      a__				)
            __snake_case								=       max(a__    ,      a__				) - min_num_layers
            __snake_case								=       '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

            for _ in range(a__				):
               common_inputs["past_key_values"].append(
                   (
                       torch.zeros(a__				),
                       torch.zeros(a__				),
                       torch.zeros(a__				),
                       torch.zeros(a__				),
                   )				)
            # TODO: test this.
            __snake_case								=       encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
            for _ in range(a__    ,      a__				):
               common_inputs["past_key_values"].append((torch.zeros(a__				), torch.zeros(a__				))				)
         return common_inputs


      def        a			(self	:				Union[str, Any]    ,      a__	:				PreTrainedTokenizer    ,      a__	:				int = -1    ,      a__	:				int = -1    ,      a__	:				bool = False    ,      a__	:				Optional[TensorType] = None    ,      ):





         """simple docstring"""



         __snake_case								=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             a__    ,      a__    ,      a__    ,      a__    ,      a__				)

         if self.use_past:
            if not is_torch_available():
               raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''				)
            else:
               import torch
            __snake_case	,     __snake_case								=       common_inputs['''input_ids'''].shape
            # Not using the same length for past_key_values
            __snake_case								=       seqlen + 2
            __snake_case	,     __snake_case								=       self.num_layers
            __snake_case	,     __snake_case								=       self.num_attention_heads
            __snake_case								=       (
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )

            __snake_case								=       common_inputs['''attention_mask'''].dtype
            __snake_case								=       torch.cat(
                [common_inputs['''attention_mask'''], torch.ones(a__    ,      a__    ,      dtype=a__				)]    ,      dim=1				)
            __snake_case								=       [
                (torch.zeros(a__				), torch.zeros(a__				)) for _ in range(a__				)
            ]
         return common_inputs


      def        a			(self	:				Optional[Any]    ,      a__	:				PreTrainedTokenizer    ,      a__	:				int = -1    ,      a__	:				int = -1    ,      a__	:				bool = False    ,      a__	:				Optional[TensorType] = None    ,      ):





         """simple docstring"""



         __snake_case								=       compute_effective_axis_dimension(
             a__    ,      fixed_dimension=OnnxConfig.default_fixed_batch    ,      num_token_to_add=0				)

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         __snake_case								=       tokenizer.num_special_tokens_to_add(a__				)
         __snake_case								=       compute_effective_axis_dimension(
             a__    ,      fixed_dimension=OnnxConfig.default_fixed_sequence    ,      num_token_to_add=a__				)

         # Generate dummy inputs according to compute batch and sequence
         __snake_case								=       [''' '''.join([tokenizer.unk_token]				) * seq_length] * batch_size
         __snake_case								=       dict(tokenizer(a__    ,      return_tensors=a__				)				)
         return common_inputs


      def        a			(self	:				Optional[Any]    ,      a__	:				PreTrainedTokenizer    ,      a__	:				int = -1    ,      a__	:				int = -1    ,      a__	:				bool = False    ,      a__	:				Optional[TensorType] = None    ,      ):





         """simple docstring"""



         if self.task in ["default", "seq2seq-lm"]:
            __snake_case								=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                a__    ,      batch_size=a__    ,      seq_length=a__    ,      is_pair=a__    ,      framework=a__				)

         elif self.task == "causal-lm":
            __snake_case								=       self._generate_dummy_inputs_for_causal_lm(
                a__    ,      batch_size=a__    ,      seq_length=a__    ,      is_pair=a__    ,      framework=a__				)
         else:
            __snake_case								=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                a__    ,      batch_size=a__    ,      seq_length=a__    ,      is_pair=a__    ,      framework=a__				)

         return common_inputs


      def        a			(self	:				int    ,      a__	:				Any    ,      a__	:				Any    ,      a__	:				Dict    ,      a__	:				Tuple				):





         """simple docstring"""



         if self.task in ["default", "seq2seq-lm"]:
            __snake_case								=       super()._flatten_past_key_values_(a__    ,      a__    ,      a__    ,      a__				)
         else:
            __snake_case								=       super(a__    ,      self				)._flatten_past_key_values_(
                a__    ,      a__    ,      a__    ,      a__				)








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    int							=   XLMTokenizer
      A_						:    Optional[Any]							=   False


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __snake_case								=       [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''w</w>''',
             '''r</w>''',
             '''t</w>''',
             '''lo''',
             '''low''',
             '''er</w>''',
             '''low</w>''',
             '''lowest</w>''',
             '''newer</w>''',
             '''wider</w>''',
             '''<unk>''',
         ]
         __snake_case								=       dict(zip(a__    ,      range(len(a__				)				)				)				)
         __snake_case								=       ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

         __snake_case								=       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''vocab_file''']				)
         __snake_case								=       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''merges_file''']				)
         with open(self.vocab_file    ,      '''w'''				) as fp:
            fp.write(json.dumps(a__				)				)
         with open(self.merges_file    ,      '''w'''				) as fp:
            fp.write('''\n'''.join(a__				)				)


      def        a			(self	:				int    ,      a__	:				Any				):





         """simple docstring"""



         __snake_case								=       '''lower newer'''
         __snake_case								=       '''lower newer'''
         return input_text, output_text


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       XLMTokenizer(self.vocab_file    ,      self.merges_file				)

         __snake_case								=       '''lower'''
         __snake_case								=       ['''low''', '''er</w>''']
         __snake_case								=       tokenizer.tokenize(a__				)
         self.assertListEqual(a__    ,      a__				)

         __snake_case								=       tokens + ['''<unk>''']
         __snake_case								=       [14, 15, 20]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__				)    ,      a__				)


      @slow
      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''				)

         __snake_case								=       tokenizer.encode('''sequence builders'''    ,      add_special_tokens=a__				)
         __snake_case								=       tokenizer.encode('''multi-sequence build'''    ,      add_special_tokens=a__				)

         __snake_case								=       tokenizer.build_inputs_with_special_tokens(a__				)
         __snake_case								=       tokenizer.build_inputs_with_special_tokens(a__    ,      a__				)

         assert encoded_sentence == [0] + text + [1]
         assert encoded_pair == [0] + text + [1] + text_a + [1]

