





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


A__:  Union[str, Any]       					=					logging.get_logger(__name__)

A__:  Any       					=					{
    """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    _a  (  snake_case__):

      """simple docstring"""


      UpperCamelCase__       						=   "mbart"
      UpperCamelCase__       						=   ["past_key_values"]
      UpperCamelCase__       						=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






      def __init__(						self:	Optional[int]					,  __lowerCamelCase:	Union[str, Any]=5_0265					,  __lowerCamelCase:	Union[str, Any]=1024					,  __lowerCamelCase:	Any=12					,  __lowerCamelCase:	Tuple=4096					,  __lowerCamelCase:	Tuple=16					,  __lowerCamelCase:	Dict=12					,  __lowerCamelCase:	int=4096					,  __lowerCamelCase:	List[str]=16					,  __lowerCamelCase:	List[Any]=0.0					,  __lowerCamelCase:	List[Any]=0.0					,  __lowerCamelCase:	str=True					,  __lowerCamelCase:	int=True					,  __lowerCamelCase:	List[Any]="gelu"					,  __lowerCamelCase:	Dict=1024					,  __lowerCamelCase:	Dict=0.1					,  __lowerCamelCase:	Optional[int]=0.0					,  __lowerCamelCase:	int=0.0					,  __lowerCamelCase:	List[str]=0.02					,  __lowerCamelCase:	Tuple=0.0					,  __lowerCamelCase:	List[str]=False					,  __lowerCamelCase:	Dict=1					,  __lowerCamelCase:	Dict=0					,  __lowerCamelCase:	Any=2					,  __lowerCamelCase:	List[Any]=2					,  **__lowerCamelCase:	str					,  ):
          '''simple docstring'''


          UpperCamelCase__:					Tuple												=      vocab_size
          UpperCamelCase__:					Tuple												=      max_position_embeddings
          UpperCamelCase__:					Optional[int]												=      d_model
          UpperCamelCase__:					Tuple												=      encoder_ffn_dim
          UpperCamelCase__:					Dict												=      encoder_layers
          UpperCamelCase__:					List[Any]												=      encoder_attention_heads
          UpperCamelCase__:					str												=      decoder_ffn_dim
          UpperCamelCase__:					List[str]												=      decoder_layers
          UpperCamelCase__:					Dict												=      decoder_attention_heads
          UpperCamelCase__:					List[str]												=      dropout
          UpperCamelCase__:					str												=      attention_dropout
          UpperCamelCase__:					Tuple												=      activation_dropout
          UpperCamelCase__:					Optional[int]												=      activation_function
          UpperCamelCase__:					Tuple												=      init_std
          UpperCamelCase__:					int												=      encoder_layerdrop
          UpperCamelCase__:					str												=      decoder_layerdrop
          UpperCamelCase__:					int												=      classifier_dropout
          UpperCamelCase__:					str												=      use_cache
          UpperCamelCase__:					Union[str, Any]												=      encoder_layers
          UpperCamelCase__:					Any												=      scale_embedding  # scale factor will be sqrt(d_model) if True
          super().__init__(
              pad_token_id=__lowerCamelCase					,  bos_token_id=__lowerCamelCase					,  eos_token_id=__lowerCamelCase					,  is_encoder_decoder=__lowerCamelCase					,  forced_eos_token_id=__lowerCamelCase					,  **__lowerCamelCase					,  )




class    _a  (  snake_case__):

      """simple docstring"""


      @property
      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


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

              if self.use_past:
                  UpperCamelCase__:					Tuple												=      {0: "batch"}
                  UpperCamelCase__:					int												=      {0: "batch", 1: "past_decoder_sequence + sequence"}
              else:
                  UpperCamelCase__:					int												=      {0: "batch", 1: "decoder_sequence"}
                  UpperCamelCase__:					Optional[int]												=      {0: "batch", 1: "decoder_sequence"}

              if self.use_past:
                  self.fill_with_past_key_values_(__lowerCamelCase					,  direction="inputs"			)
          elif self.task == "causal-lm":
              # TODO: figure this case out.
              UpperCamelCase__:					List[Any]												=      OrderedDict(
                  [
                      ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                      ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                  ]			)
              if self.use_past:
                  UpperCamelCase__:					Optional[int]												=      self.num_layers
                  for i in range(__lowerCamelCase			):
                      UpperCamelCase__:					str												=      {0: "batch", 2: "past_sequence + sequence"}
                      UpperCamelCase__:					Union[str, Any]												=      {0: "batch", 2: "past_sequence + sequence"}
          else:
              UpperCamelCase__:					Dict												=      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:	int			):
          '''simple docstring'''


          if self.task in ["default", "seq2seq-lm"]:
              UpperCamelCase__:					Dict												=      super().outputs
          else:
              UpperCamelCase__:					str												=      super(__lowerCamelCase					,  self			).outputs
              if self.use_past:
                  UpperCamelCase__:					Tuple												=      self.num_layers
                  for i in range(__lowerCamelCase			):
                      UpperCamelCase__:					List[Any]												=      {0: "batch", 2: "past_sequence + sequence"}
                      UpperCamelCase__:					Tuple												=      {0: "batch", 2: "past_sequence + sequence"}
          return common_outputs






      def 	UpperCAmelCase_   (						self:	Dict					,  __lowerCamelCase:	PreTrainedTokenizer					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	bool = False					,  __lowerCamelCase:	Optional[TensorType] = None					,  ):
          '''simple docstring'''


          UpperCamelCase__:					Optional[Any]												=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)

          # Generate decoder inputs
          UpperCamelCase__:					Union[str, Any]												=      seq_length if not self.use_past else 1
          UpperCamelCase__:					Tuple												=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)
          UpperCamelCase__:					Dict												=      {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
          UpperCamelCase__:					Tuple												=      dict(**__lowerCamelCase					,  **__lowerCamelCase			)

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

              UpperCamelCase__:					Tuple												=      torch.cat(
                  [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase					,  __lowerCamelCase			)]					,  dim=1			)

              UpperCamelCase__:					Dict												=      []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              UpperCamelCase__:					Tuple												=      self.num_layers
              UpperCamelCase__:					Tuple												=      min(__lowerCamelCase					,  __lowerCamelCase			)
              UpperCamelCase__:					Any												=      max(__lowerCamelCase					,  __lowerCamelCase			) - min_num_layers
              UpperCamelCase__:					Optional[Any]												=      "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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






      def 	UpperCAmelCase_   (						self:	List[Any]					,  __lowerCamelCase:	PreTrainedTokenizer					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	bool = False					,  __lowerCamelCase:	Optional[TensorType] = None					,  ):
          '''simple docstring'''


          UpperCamelCase__:					Dict												=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)

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

              UpperCamelCase__:					Optional[int]												=      common_inputs["attention_mask"].dtype
              UpperCamelCase__:					Tuple												=      torch.cat(
                  [common_inputs["attention_mask"], torch.ones(__lowerCamelCase					,  __lowerCamelCase					,  dtype=__lowerCamelCase			)]					,  dim=1			)
              UpperCamelCase__:					List[Any]												=      [
                  (torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			)) for _ in range(__lowerCamelCase			)
              ]
          return common_inputs






      def 	UpperCAmelCase_   (						self:	Optional[Any]					,  __lowerCamelCase:	PreTrainedTokenizer					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	bool = False					,  __lowerCamelCase:	Optional[TensorType] = None					,  ):
          '''simple docstring'''


          UpperCamelCase__:					Dict												=      compute_effective_axis_dimension(
              __lowerCamelCase					,  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__:					int												=      tokenizer.num_special_tokens_to_add(__lowerCamelCase			)
          UpperCamelCase__:					List[Any]												=      compute_effective_axis_dimension(
              __lowerCamelCase					,  fixed_dimension=OnnxConfig.default_fixed_sequence					,  num_token_to_add=__lowerCamelCase			)

          # Generate dummy inputs according to compute batch and sequence
          UpperCamelCase__:					List[Any]												=      [" ".join([tokenizer.unk_token]			) * seq_length] * batch_size
          UpperCamelCase__:					Union[str, Any]												=      dict(tokenizer(__lowerCamelCase					,  return_tensors=__lowerCamelCase			)			)
          return common_inputs






      def 	UpperCAmelCase_   (						self:	Dict					,  __lowerCamelCase:	PreTrainedTokenizer					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	int = -1					,  __lowerCamelCase:	bool = False					,  __lowerCamelCase:	Optional[TensorType] = None					,  ):
          '''simple docstring'''


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

          elif self.task == "causal-lm":
              UpperCamelCase__:					List[str]												=      self._generate_dummy_inputs_for_causal_lm(
                  __lowerCamelCase					,  batch_size=__lowerCamelCase					,  seq_length=__lowerCamelCase					,  is_pair=__lowerCamelCase					,  framework=__lowerCamelCase			)
          else:
              UpperCamelCase__:					List[str]												=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  __lowerCamelCase					,  batch_size=__lowerCamelCase					,  seq_length=__lowerCamelCase					,  is_pair=__lowerCamelCase					,  framework=__lowerCamelCase			)

          return common_inputs






      def 	UpperCAmelCase_   (						self:	int					,  __lowerCamelCase:	Optional[Any]					,  __lowerCamelCase:	List[Any]					,  __lowerCamelCase:	Optional[Any]					,  __lowerCamelCase:	str			):
          '''simple docstring'''


          if self.task in ["default", "seq2seq-lm"]:
              UpperCamelCase__:					Dict												=      super()._flatten_past_key_values_(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)
          else:
              UpperCamelCase__:					Optional[int]												=      super(__lowerCamelCase					,  self			)._flatten_past_key_values_(
                  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)








import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor



def 							lowerCAmelCase_				(    ):
    UpperCamelCase__:					Optional[Any]												=      set()
    # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
    for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
        UpperCamelCase__:					Tuple												=      getattr(transformers.models.auto  ,A_  ,A_)
        if module is None:
            continue
        # all mappings in a single auto modeling file
        UpperCamelCase__:					Union[str, Any]												=      [
            x
            for x in dir(A_)
            if x.endswith("_MAPPING_NAMES")
            and (x.startswith("MODEL_") or x.startswith("TF_MODEL_") or x.startswith("FLAX_MODEL_"))
        ]
        for name in mapping_names:
            UpperCamelCase__:					Union[str, Any]												=      getattr(A_  ,A_)
            if mapping is not None:
                for v in mapping.values():
                    if isinstance(A_  ,(list, tuple)):
                        model_names.update(A_)
                    elif isinstance(A_  ,A_):
                        model_names.add(A_)

    return sorted(A_)



def 							lowerCAmelCase_				(    ):
    # All model names defined in auto mappings
    UpperCamelCase__:					Any												=      set(get_all_model_names())

    with open("tests/utils/tiny_model_summary.json") as fp:
        UpperCamelCase__:					Union[str, Any]												=      json.load(A_)
    UpperCamelCase__:					Union[str, Any]												=      set()
    for model_base_name in tiny_model_info:
        tiny_models_names.update(tiny_model_info[model_base_name]["model_classes"])

    # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
    UpperCamelCase__:					Any												=      model_names.difference(A_)
    for model_name in copy.copy(A_):
        if not model_name.startswith("TF") and F"TF{model_name}" in not_on_hub:
            tiny_models_names.remove(A_)
        elif model_name.startswith("TF") and model_name[2:] in not_on_hub:
            tiny_models_names.remove(A_)

    return sorted(A_)



def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					int												=      COMPOSITE_MODELS.values()

    # All tiny model base names on Hub
    UpperCamelCase__:					Any												=      get_all_model_names()
    UpperCamelCase__:					Tuple												=      hf_api.list_models(
        filter=ModelFilter(
            author="hf-internal-testing"  ,))
    UpperCamelCase__:					Optional[Any]												=      set()
    for x in models:
        UpperCamelCase__:					List[str]												=      x.modelId
        UpperCamelCase__							,				UpperCamelCase__:					Any												=      model.split("/")
        if not model.startswith("tiny-random-"):
            continue
        UpperCamelCase__:					Tuple												=      model.replace("tiny-random-"  ,"")
        if not model[0].isupper():
            continue
        if model not in model_names and model not in special_models:
            continue
        _models.add(A_)

    UpperCamelCase__:					List[Any]												=      sorted(_models)
    # All tiny model names on Hub
    UpperCamelCase__:					List[str]												=      {}
    for model in models:
        UpperCamelCase__:					str												=      F"hf-internal-testing/tiny-random-{model}"
        UpperCamelCase__:					Union[str, Any]												=      model.split("-")[0]
        try:
            UpperCamelCase__:					Any												=      hf_api.repo_info(A_)
            UpperCamelCase__:					int												=      {
                "tokenizer_classes": set(),
                "processor_classes": set(),
                "model_classes": set(),
                "sha": repo_info.sha,
            }
        except Exception:
            continue
        try:
            time.sleep(1)
            UpperCamelCase__:					Any												=      AutoTokenizer.from_pretrained(A_)
            content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					str												=      AutoTokenizer.from_pretrained(A_  ,use_fast=A_)
            content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					List[str]												=      AutoImageProcessor.from_pretrained(A_)
            content["processor_classes"].add(img_p.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					Union[str, Any]												=      AutoFeatureExtractor.from_pretrained(A_)
            if not isinstance(A_  ,A_):
                content["processor_classes"].add(feat_p.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					List[str]												=      getattr(A_  ,A_)
            UpperCamelCase__:					Union[str, Any]												=      model_class.from_pretrained(A_)
            content["model_classes"].add(m.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					str												=      getattr(A_  ,F"TF{model}")
            UpperCamelCase__:					Optional[Any]												=      model_class.from_pretrained(A_)
            content["model_classes"].add(m.__class__.__name__)
        except Exception:
            pass

        UpperCamelCase__:					Optional[int]												=      sorted(content["tokenizer_classes"])
        UpperCamelCase__:					List[str]												=      sorted(content["processor_classes"])
        UpperCamelCase__:					Optional[int]												=      sorted(content["model_classes"])

        UpperCamelCase__:					int												=      content
        with open(os.path.join(A_  ,"hub_tiny_model_summary.json")  ,"w") as fp:
            json.dump(A_  ,A_  ,ensure_ascii=A_  ,indent=4)


if __name__ == "__main__":
   A__:  Optional[Any]       					=					argparse.ArgumentParser()
   parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
   A__:  Dict       					=					parser.parse_args()

   # This has to be `spawn` to avoid hanging forever!
   multiprocessing.set_start_method('''spawn''')

   A__:  List[Any]       					=					'''tiny_models'''
   A__:  Dict       					=					True
   A__:  Dict       					=					None
   A__:  Optional[Any]       					=					get_tiny_model_names_from_repo()
   A__:  int       					=					True
   A__:  int       					=					True
   A__:  int       					=					'''hf-internal-testing'''

   create_tiny_models(
       output_path,
       all,
       model_types,
       models_to_skip,
       no_check,
       upload,
       organization,
       token=os.environ.get('''TOKEN''', None),
       num_workers=args.num_workers,
   )

