





'''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


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							     =			{
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "marian"
     snake_case_						      =   ["past_key_values"]
     snake_case_						      =   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



     def __init__(   self							:			Any   ,       __snake_case							:			Any=5_81_01   ,       __snake_case							:			Union[str, Any]=None   ,       __snake_case							:			Optional[Any]=10_24   ,       __snake_case							:			Optional[Any]=12   ,       __snake_case							:			List[str]=40_96   ,       __snake_case							:			Optional[Any]=16   ,       __snake_case							:			List[Any]=12   ,       __snake_case							:			Optional[int]=40_96   ,       __snake_case							:			Any=16   ,       __snake_case							:			Tuple=0.0   ,       __snake_case							:			Dict=0.0   ,       __snake_case							:			int=True   ,       __snake_case							:			Optional[Any]=True   ,       __snake_case							:			List[str]="gelu"   ,       __snake_case							:			Dict=10_24   ,       __snake_case							:			Tuple=0.1   ,       __snake_case							:			str=0.0   ,       __snake_case							:			int=0.0   ,       __snake_case							:			str=0.02   ,       __snake_case							:			Union[str, Any]=5_81_00   ,       __snake_case							:			Dict=False   ,       __snake_case							:			Any=5_81_00   ,       __snake_case							:			Tuple=0   ,       __snake_case							:			Optional[Any]=0   ,       __snake_case							:			Union[str, Any]=True   ,       **__snake_case							:			Tuple   ,       )->	List[Any]:
        snake_case 						=   vocab_size
        snake_case 						=   decoder_vocab_size or 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
        snake_case 						=   share_encoder_decoder_embeddings
        super().__init__(
            pad_token_id=__snake_case   ,       eos_token_id=__snake_case   ,       is_encoder_decoder=__snake_case   ,       decoder_start_token_id=__snake_case   ,       forced_eos_token_id=__snake_case   ,       **__snake_case   ,       )




class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
     def      lowerCAmelCase			(   self							:			Any						)->	Mapping[str, Mapping[int, str]]:
        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_(__snake_case   ,       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(__snake_case						):
                 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
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def      lowerCAmelCase			(   self							:			List[Any]						)->	Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
           snake_case 						=   super().outputs
        else:
           snake_case 						=   super(__snake_case   ,       self						).outputs
           if self.use_past:
              snake_case						,    snake_case 						=   self.num_layers
              for i in range(__snake_case						):
                 snake_case 						=   {0: """batch""", 2: """past_sequence + sequence"""}
                 snake_case 						=   {0: """batch""", 2: """past_sequence + sequence"""}
        return common_outputs



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			PreTrainedTokenizer   ,       __snake_case							:			int = -1   ,       __snake_case							:			int = -1   ,       __snake_case							:			bool = False   ,       __snake_case							:			Optional[TensorType] = None   ,       )->	Mapping[str, Any]:
        snake_case 						=   self._generate_dummy_inputs_for_encoder_and_decoder(
            __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case						)

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

        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(__snake_case   ,       __snake_case						)]   ,       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(__snake_case   ,       __snake_case						)
           snake_case 						=   max(__snake_case   ,       __snake_case						) - min_num_layers
           snake_case 						=   """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			PreTrainedTokenizer   ,       __snake_case							:			int = -1   ,       __snake_case							:			int = -1   ,       __snake_case							:			bool = False   ,       __snake_case							:			Optional[TensorType] = None   ,       )->	Mapping[str, Any]:
        snake_case 						=   self._generate_dummy_inputs_for_encoder_and_decoder(
            __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case						)

        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(__snake_case   ,       __snake_case   ,       dtype=__snake_case						)]   ,       dim=1						)
           snake_case 						=   [
               (torch.zeros(__snake_case						), torch.zeros(__snake_case						)) for _ in range(__snake_case						)
           ]
        return common_inputs



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			PreTrainedTokenizer   ,       __snake_case							:			int = -1   ,       __snake_case							:			int = -1   ,       __snake_case							:			bool = False   ,       __snake_case							:			Optional[TensorType] = None   ,       )->	Mapping[str, Any]:
        # 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
        snake_case 						=   compute_effective_axis_dimension(
            __snake_case   ,       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(__snake_case						)
        snake_case 						=   compute_effective_axis_dimension(
            __snake_case   ,       fixed_dimension=OnnxConfig.default_fixed_sequence   ,       num_token_to_add=__snake_case						)

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



     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			PreTrainedTokenizer   ,       __snake_case							:			int = -1   ,       __snake_case							:			int = -1   ,       __snake_case							:			bool = False   ,       __snake_case							:			Optional[TensorType] = None   ,       )->	Mapping[str, Any]:
        if self.task in ["default", "seq2seq-lm"]:
           snake_case 						=   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               __snake_case   ,       batch_size=__snake_case   ,       seq_length=__snake_case   ,       is_pair=__snake_case   ,       framework=__snake_case						)

        else:
           snake_case 						=   self._generate_dummy_inputs_for_causal_lm(
               __snake_case   ,       batch_size=__snake_case   ,       seq_length=__snake_case   ,       is_pair=__snake_case   ,       framework=__snake_case						)

        return common_inputs



     def      lowerCAmelCase			(   self							:			Union[str, Any]   ,       __snake_case							:			Any   ,       __snake_case							:			str   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Tuple						)->	int:
        if self.task in ["default", "seq2seq-lm"]:
           snake_case 						=   super()._flatten_past_key_values_(__snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case						)
        else:
           snake_case 						=   super(__snake_case   ,       self						)._flatten_past_key_values_(
               __snake_case   ,       __snake_case   ,       __snake_case   ,       __snake_case						)



     @property
     def      lowerCAmelCase			(   self							:			Dict						)->	float:
        return 1e-4








'''simple docstring'''




import os
from collections.abc import Iterator





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "."		)		->  Iterator[str]:
   for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase		):
      snake_case 						=   [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
      for filename in filenames:
         if filename == "__init__.py":
            continue
         if os.path.splitext(__lowerCAmelCase		)[1] in (".py", ".ipynb"):
            yield os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		).lstrip("""./"""		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]		)		->  Any:
   return F'''{i * "  "}*''' if i else "\n##"





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							str		)		->  str:
   snake_case 						=   old_path.split(os.sep		)
   for i, new_part in enumerate(new_path.split(os.sep		)		):
      if (i + 1 > len(__lowerCAmelCase		) or old_parts[i] != new_part) and new_part:
         print(F'''{md_prefix(__lowerCAmelCase		)} {new_part.replace("_"      ,    " "		).title()}'''		)
   return new_path





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str = "."		)		->  None:
   snake_case 						=   """"""
   for filepath in sorted(good_file_paths(__lowerCAmelCase		)		):
      snake_case						,    snake_case 						=   os.path.split(__lowerCAmelCase		)
      if filepath != old_path:
         snake_case 						=   print_path(__lowerCAmelCase      ,    __lowerCAmelCase		)
      snake_case 						=   (filepath.count(os.sep		) + 1) if filepath else 0
      snake_case 						=   F'''{filepath}/{filename}'''.replace(""" """      ,    """%20"""		)
      snake_case 						=   os.path.splitext(filename.replace("""_"""      ,    """ """		).title()		)[0]
      print(F'''{md_prefix(__lowerCAmelCase		)} [{filename}]({url})'''		)


if __name__ == "__main__":
 print_directory_md(".")

