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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__lowerCamelCase							= logging.get_logger(__name__)

__lowerCamelCase							= {
    """gpt2""": """https://huggingface.co/gpt2/resolve/main/config.json""",
    """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/config.json""",
    """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/config.json""",
    """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/config.json""",
    """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/config.json""",
}






class 						UpperCAmelCase	(		A_				):
   A__      :	Optional[int]		  =  "gpt2"
   A__      :	Any		  =  ["past_key_values"]
   A__      :	Dict		  =  {
       "hidden_size": "n_embd",
       "max_position_embeddings": "n_positions",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }



   def __init__(self	:      Dict					,		snake_case__	:      Union[str, Any]=5_02_57					,		snake_case__	:      int=10_24					,		snake_case__	:      List[str]=7_68					,		snake_case__	:      Optional[int]=12					,		snake_case__	:      int=12					,		snake_case__	:      Optional[Any]=None					,		snake_case__	:      Union[str, Any]="gelu_new"					,		snake_case__	:      int=0.1					,		snake_case__	:      Optional[int]=0.1					,		snake_case__	:      Optional[Any]=0.1					,		snake_case__	:      Optional[int]=1e-5					,		snake_case__	:      List[str]=0.02					,		snake_case__	:      Optional[int]="cls_index"					,		snake_case__	:      int=True					,		snake_case__	:      int=None					,		snake_case__	:      Union[str, Any]=True					,		snake_case__	:      Union[str, Any]=0.1					,		snake_case__	:      List[Any]=True					,		snake_case__	:      str=True					,		snake_case__	:      Any=5_02_56					,		snake_case__	:      Any=5_02_56					,		snake_case__	:      Any=False					,		snake_case__	:      Dict=False					,		**snake_case__	:      Dict					,		)	->			Optional[int]:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   vocab_size
          snake_case :     List[str]           =   n_positions
          snake_case :     str           =   n_embd
          snake_case :     str           =   n_layer
          snake_case :     List[Any]           =   n_head
          snake_case :     List[str]           =   n_inner
          snake_case :     Dict           =   activation_function
          snake_case :     List[Any]           =   resid_pdrop
          snake_case :     Union[str, Any]           =   embd_pdrop
          snake_case :     List[Any]           =   attn_pdrop
          snake_case :     Optional[int]           =   layer_norm_epsilon
          snake_case :     Dict           =   initializer_range
          snake_case :     List[str]           =   summary_type
          snake_case :     str           =   summary_use_proj
          snake_case :     Optional[Any]           =   summary_activation
          snake_case :     List[str]           =   summary_first_dropout
          snake_case :     Optional[int]           =   summary_proj_to_labels
          snake_case :     Any           =   scale_attn_weights
          snake_case :     Tuple           =   use_cache
          snake_case :     str           =   scale_attn_by_inverse_layer_idx
          snake_case :     Union[str, Any]           =   reorder_and_upcast_attn

          snake_case :     Tuple           =   bos_token_id
          snake_case :     Union[str, Any]           =   eos_token_id

          super().__init__(bos_token_id=snake_case__					,		eos_token_id=snake_case__					,		**snake_case__				)






class 						UpperCAmelCase	(		A_				):



   def __init__(self	:      str					,		snake_case__	:      PretrainedConfig					,		snake_case__	:      str = "default"					,		snake_case__	:      List[PatchingSpec] = None					,		snake_case__	:      bool = False					,		)	->			List[str]:


          '''simple docstring'''


          super().__init__(snake_case__					,		task=snake_case__					,		patching_specs=snake_case__					,		use_past=snake_case__				)
          if not getattr(self._config					,		"pad_token_id"					,		snake_case__				):
                 # TODO: how to do that better?
                 snake_case :     Union[str, Any]           =   0



   @property
   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			Mapping[str, Mapping[int, str]]:


          '''simple docstring'''


          snake_case :     Dict           =   OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}				)
          if self.use_past:
                 self.fill_with_past_key_values_(snake_case__					,		direction="inputs"				)
                 snake_case :     List[Any]           =   {0: "batch", 1: "past_sequence + sequence"}
          else:
                 snake_case :     Dict           =   {0: "batch", 1: "sequence"}

          return common_inputs



   @property
   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]				)	->			int:


          '''simple docstring'''


          return self._config.n_layer



   @property
   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			int:


          '''simple docstring'''


          return self._config.n_head



   def        _SCREAMING_SNAKE_CASE     (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]:


          '''simple docstring'''


          snake_case :     Tuple           =   super(snake_case__					,		self				).generate_dummy_inputs(
              snake_case__					,		batch_size=snake_case__					,		seq_length=snake_case__					,		is_pair=snake_case__					,		framework=snake_case__				)

          # We need to order the input in the way they appears in the forward()
          snake_case :     Dict           =   OrderedDict({"input_ids": common_inputs["input_ids"]}				)

          # Need to add the past_keys
          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 :     Union[str, Any]           =   common_inputs["input_ids"].shape
                        # Not using the same length for past_key_values
                        snake_case :     Dict           =   seqlen + 2
                        snake_case :     Dict           =   (
                            batch,
                            self.num_attention_heads,
                            past_key_values_length,
                            self._config.hidden_size // self.num_attention_heads,
                        )
                        snake_case :     int           =   [
                            (torch.zeros(snake_case__				), torch.zeros(snake_case__				)) for _ in range(self.num_layers				)
                        ]

          snake_case :     List[str]           =   common_inputs["attention_mask"]
          if self.use_past:
                 snake_case :     str           =   ordered_inputs["attention_mask"].dtype
                 snake_case :     Any           =   torch.cat(
                     [ordered_inputs["attention_mask"], torch.ones(snake_case__					,		snake_case__					,		dtype=snake_case__				)]					,		dim=1				)

          return ordered_inputs



   @property
   def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			int:


          '''simple docstring'''


          return 13







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils






class 						UpperCAmelCase	(		unittest.TestCase				):



   @patch("diffusers.utils.hub_utils.get_full_repo_name"				)
   def        _SCREAMING_SNAKE_CASE     (self	:      Any					,		snake_case__	:      Mock				)	->			None:


          '''simple docstring'''


          snake_case :     str           =   "full_repo_name"
          with TemporaryDirectory() as tmpdir:
                 # Dummy args values
                 snake_case :     List[str]           =   Mock()
                 snake_case :     List[str]           =   tmpdir
                 snake_case :     Optional[int]           =   0
                 snake_case :     int           =   "hub_token"
                 snake_case :     Union[str, Any]           =   "dataset_name"
                 snake_case :     Tuple           =   0.01
                 snake_case :     int           =   10_00_00
                 snake_case :     Optional[Any]           =   1_00_00
                 snake_case :     Optional[Any]           =   0.01
                 snake_case :     Dict           =   0.02
                 snake_case :     List[Any]           =   0.03
                 snake_case :     str           =   0.0005
                 snake_case :     List[str]           =   0.000001
                 snake_case :     Tuple           =   1
                 snake_case :     Optional[int]           =   10
                 snake_case :     List[str]           =   0.001
                 snake_case :     List[Any]           =   0.1
                 snake_case :     List[str]           =   0.2
                 snake_case :     Tuple           =   True

                 # Model card mush be rendered and saved
                 diffusers.utils.hub_utils.create_model_card(snake_case__					,		model_name="model_name"				)
                 self.assertTrue((Path(snake_case__				) / "README.md").is_file()				)





