






"""simple docstring"""







from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

lowerCAmelCase_		:			Dict				   =					{
    name: (getattr(transformers, name), getattr(transformers, name + '''Fast''')) for name in SLOW_TO_FAST_CONVERTERS
}

lowerCAmelCase_		:			Tuple				   =					datasets.load_dataset('''xnli''', split='''test+validation''')

lowerCAmelCase_		:			int				   =					0
lowerCAmelCase_		:			Dict				   =					0
lowerCAmelCase_		:			List[str]				   =					0
lowerCAmelCase_		:			Tuple				   =					0






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''






       if spm_diff == list(reversed(_snake_case						)						):
              # AAA -> AA+A vs A+AA case.
              return True
       elif len(_snake_case						) == len(_snake_case						) and fast.decode(_snake_case						) == fast.decode(_snake_case						):
              # Second order OK
              # Barrich -> Barr + ich vs Bar + rich
              return True
       UpperCAmelCase					=	slow.encode(slow.decode(_snake_case						)						)
       UpperCAmelCase					=	fast.encode(fast.decode(_snake_case						)						)
       if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
              # Type 3 error.
              # Snehagatha ->
              #       Sne, h, aga, th, a
              #       Sne, ha, gat, ha
              # Encoding the wrong with sp does not even recover what spm gave us
              # It fits tokenizer however...
              return True
       return False






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	fast.encode_plus(_snake_case						)[0]
       UpperCAmelCase					=	enc.offsets
       UpperCAmelCase			,				UpperCAmelCase					=	offsets[idx], offsets[idx - 1]
       if curr is not None and line[curr[0] : curr[1]] == "\u200f":
              return True
       if prev is not None and line[prev[0] : prev[1]] == "\u200f":
              return True






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''






       for i, (spm_id, tok_id) in enumerate(zip(_snake_case		,					_snake_case						)						):
              if spm_id != tok_id:
                     break
       UpperCAmelCase					=	i
       for i, (spm_id, tok_id) in enumerate(zip(reversed(_snake_case						)		,					reversed(_snake_case						)						)						):
              if spm_id != tok_id:
                     break
       UpperCAmelCase					=	len(_snake_case						) - i

       UpperCAmelCase					=	spm_ids[first:last]
       UpperCAmelCase					=	tok_ids[first:last]

       if check_diff(_snake_case		,					_snake_case		,					_snake_case		,					_snake_case						):
              return True

       if check_LTR_mark(_snake_case		,					_snake_case		,					_snake_case						):
              return True

       if last - first > 5:
              # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
              UpperCAmelCase					=	Counter(spm_ids[first:last]						)
              UpperCAmelCase					=	Counter(tok_ids[first:last]						)

              UpperCAmelCase					=	{spm_ for (spm_, si) in spms.items() if toks.get(spm_		,					0						) == si}
              UpperCAmelCase					=	3
              for i in range(last - first - min_width						):
                     if all(spm_ids[first + i + j] in removable_tokens for j in range(_snake_case						)						):
                            UpperCAmelCase					=	[
                                k
                                for k in range(last - first - min_width						)
                                if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
                            ]
                            for j in possible_matches:
                                   if check_diff(spm_ids[first : first + i]		,					tok_ids[first : first + j]		,					_snake_case		,					_snake_case						) and check_details(
                                       _snake_case		,					spm_ids[first + i : last]		,					tok_ids[first + j : last]		,					_snake_case		,					_snake_case		,					):
                                          return True

       print(F'''Spm: {[fast.decode([spm_ids[i]]						) for i in range(_snake_case		,					_snake_case						)]}'''						)
       try:
              print(F'''Tok: {[fast.decode([tok_ids[i]]						) for i in range(_snake_case		,					_snake_case						)]}'''						)
       except Exception:
              pass

       UpperCAmelCase					=	fast.decode(spm_ids[:first]						)
       UpperCAmelCase					=	fast.decode(spm_ids[last:]						)
       UpperCAmelCase					=	fast.decode(spm_ids[first:last]						)
       print()
       print(_snake_case						)
       return False






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       global perfect
       global imperfect
       global wrong
       global total

       UpperCAmelCase					=	slow.encode(_snake_case						)
       UpperCAmelCase					=	fast.encode(_snake_case						)

       UpperCAmelCase					=	False
       total += 1

       if slow_ids != fast_ids:
              if check_details(_snake_case		,					_snake_case		,					_snake_case		,					_snake_case		,					_snake_case						):
                     UpperCAmelCase					=	True
                     imperfect += 1
              else:
                     wrong += 1
       else:
              perfect += 1

       if total % 10000 == 0:
              print(F'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''						)

       if skip_assert:
              return

       assert (
           slow_ids == fast_ids
       ), F'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(_snake_case						)}\n{fast.tokenize(_snake_case						)}'''






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       global batch_total
       for i in range(len(_snake_case						)						):
              # premise, all languages
              for text in dataset[i]["premise"].values():
                     test_string(_snake_case		,					_snake_case		,					_snake_case						)

              # hypothesis, all languages
              for text in dataset[i]["hypothesis"]["translation"]:
                     test_string(_snake_case		,					_snake_case		,					_snake_case						)


if __name__ == "__main__":
 for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
  lowerCAmelCase_		:			int				   =					list(slow_class.max_model_input_sizes.keys())
  for checkpoint in checkpoint_names:
   lowerCAmelCase_		:			Dict				   =					0
   lowerCAmelCase_		:			List[Any]				   =					0
   lowerCAmelCase_		:			List[str]				   =					0
   lowerCAmelCase_		:			Any				   =					0

   print(F'========================== Checking {name}: {checkpoint} ==========================')
   lowerCAmelCase_		:			str				   =					slow_class.from_pretrained(checkpoint, force_download=True)
   lowerCAmelCase_		:			List[str]				   =					fast_class.from_pretrained(checkpoint, force_download=True)
   test_tokenizer(slow, fast)
   print(F'Accuracy {perfect * 1_0_0 / total:.2f}')








"""simple docstring"""







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

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


_UpperCamelCase     			=  logging.get_logger(__name__)

_UpperCamelCase     			=  {
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}







class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			'''mbart'''
    SCREAMING_SNAKE_CASE		=			['''past_key_values''']
    SCREAMING_SNAKE_CASE		=			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






    def __init__( self	,A=50_265	,A=1_024	,A=12	,A=4_096	,A=16	,A=12	,A=4_096	,A=16	,A=0.0	,A=0.0	,A=True	,A=True	,A="gelu"	,A=1_024	,A=0.1	,A=0.0	,A=0.0	,A=0.02	,A=0.0	,A=False	,A=1	,A=0	,A=2	,A=2	,**A	,):
          UpperCAmelCase							=       vocab_size
          UpperCAmelCase							=       max_position_embeddings
          UpperCAmelCase							=       d_model
          UpperCAmelCase							=       encoder_ffn_dim
          UpperCAmelCase							=       encoder_layers
          UpperCAmelCase							=       encoder_attention_heads
          UpperCAmelCase							=       decoder_ffn_dim
          UpperCAmelCase							=       decoder_layers
          UpperCAmelCase							=       decoder_attention_heads
          UpperCAmelCase							=       dropout
          UpperCAmelCase							=       attention_dropout
          UpperCAmelCase							=       activation_dropout
          UpperCAmelCase							=       activation_function
          UpperCAmelCase							=       init_std
          UpperCAmelCase							=       encoder_layerdrop
          UpperCAmelCase							=       decoder_layerdrop
          UpperCAmelCase							=       classifier_dropout
          UpperCAmelCase							=       use_cache
          UpperCAmelCase							=       encoder_layers
          UpperCAmelCase							=       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	,forced_eos_token_id=A	,**A	,)







class 					lowerCamelCase__					(       snake_case ):






    @property
    def 				_UpperCamelCase							( self					):
          if self.task in ["default", "seq2seq-lm"]:
                UpperCAmelCase							=       OrderedDict(
                    [
                        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                    ]					)

                if self.use_past:
                      UpperCAmelCase							=       {0: """batch"""}
                      UpperCAmelCase							=       {0: """batch""", 1: """past_decoder_sequence + sequence"""}
                else:
                      UpperCAmelCase							=       {0: """batch""", 1: """decoder_sequence"""}
                      UpperCAmelCase							=       {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.
                UpperCAmelCase							=       OrderedDict(
                    [
                        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                    ]					)
                if self.use_past:
                      UpperCAmelCase			,					UpperCAmelCase							=       self.num_layers
                      for i in range(A					):
                            UpperCAmelCase							=       {0: """batch""", 2: """past_sequence + sequence"""}
                            UpperCAmelCase							=       {0: """batch""", 2: """past_sequence + sequence"""}
          else:
                UpperCAmelCase							=       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 				_UpperCamelCase							( self					):
          if self.task in ["default", "seq2seq-lm"]:
                UpperCAmelCase							=       super().outputs
          else:
                UpperCAmelCase							=       super(A	,self					).outputs
                if self.use_past:
                      UpperCAmelCase			,					UpperCAmelCase							=       self.num_layers
                      for i in range(A					):
                            UpperCAmelCase							=       {0: """batch""", 2: """past_sequence + sequence"""}
                            UpperCAmelCase							=       {0: """batch""", 2: """past_sequence + sequence"""}
          return common_outputs






    def 				_UpperCamelCase							( self	,A	,A = -1	,A = -1	,A = False	,A = None	,):
          UpperCAmelCase							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A	,A	,A	,A	,A					)

          # Generate decoder inputs
          UpperCAmelCase							=       seq_length if not self.use_past else 1
          UpperCAmelCase							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              A	,A	,A	,A	,A					)
          UpperCAmelCase							=       {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
          UpperCAmelCase							=       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
                UpperCAmelCase			,					UpperCAmelCase							=       common_inputs["""input_ids"""].shape
                UpperCAmelCase							=       common_inputs["""decoder_input_ids"""].shape[1]
                UpperCAmelCase			,					UpperCAmelCase							=       self.num_attention_heads
                UpperCAmelCase							=       (
                    batch,
                    num_encoder_attention_heads,
                    encoder_seq_length,
                    self._config.hidden_size // num_encoder_attention_heads,
                )
                UpperCAmelCase							=       decoder_seq_length + 3
                UpperCAmelCase							=       (
                    batch,
                    num_decoder_attention_heads,
                    decoder_past_length,
                    self._config.hidden_size // num_decoder_attention_heads,
                )

                UpperCAmelCase							=       torch.cat(
                    [common_inputs["""decoder_attention_mask"""], torch.ones(A	,A					)]	,dim=1					)

                UpperCAmelCase							=       []
                # If the number of encoder and decoder layers are present in the model configuration, both are considered
                UpperCAmelCase			,					UpperCAmelCase							=       self.num_layers
                UpperCAmelCase							=       min(A	,A					)
                UpperCAmelCase							=       max(A	,A					) - min_num_layers
                UpperCAmelCase							=       """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.
                UpperCAmelCase							=       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 				_UpperCamelCase							( self	,A	,A = -1	,A = -1	,A = False	,A = None	,):
          UpperCAmelCase							=       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
                UpperCAmelCase			,					UpperCAmelCase							=       common_inputs["""input_ids"""].shape
                # Not using the same length for past_key_values
                UpperCAmelCase							=       seqlen + 2
                UpperCAmelCase			,					UpperCAmelCase							=       self.num_layers
                UpperCAmelCase			,					UpperCAmelCase							=       self.num_attention_heads
                UpperCAmelCase							=       (
                    batch,
                    num_encoder_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // num_encoder_attention_heads,
                )

                UpperCAmelCase							=       common_inputs["""attention_mask"""].dtype
                UpperCAmelCase							=       torch.cat(
                    [common_inputs["""attention_mask"""], torch.ones(A	,A	,dtype=A					)]	,dim=1					)
                UpperCAmelCase							=       [
                    (torch.zeros(A					), torch.zeros(A					)) for _ in range(A					)
                ]
          return common_inputs






    def 				_UpperCamelCase							( self	,A	,A = -1	,A = -1	,A = False	,A = None	,):
          # Copied from OnnxConfig.generate_dummy_inputs
          # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          UpperCAmelCase							=       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
          UpperCAmelCase							=       tokenizer.num_special_tokens_to_add(A					)
          UpperCAmelCase							=       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
          UpperCAmelCase							=       [""" """.join([tokenizer.unk_token]					) * seq_length] * batch_size
          UpperCAmelCase							=       dict(tokenizer(A	,return_tensors=A					)					)
          return common_inputs






    def 				_UpperCamelCase							( self	,A	,A = -1	,A = -1	,A = False	,A = None	,):
          if self.task in ["default", "seq2seq-lm"]:
                UpperCAmelCase							=       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":
                UpperCAmelCase							=       self._generate_dummy_inputs_for_causal_lm(
                    A	,batch_size=A	,seq_length=A	,is_pair=A	,framework=A					)
          else:
                UpperCAmelCase							=       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 				_UpperCamelCase							( self	,A	,A	,A	,A					):
          if self.task in ["default", "seq2seq-lm"]:
                UpperCAmelCase							=       super()._flatten_past_key_values_(A	,A	,A	,A					)
          else:
                UpperCAmelCase							=       super(A	,self					)._flatten_past_key_values_(
                    A	,A	,A	,A					)



