"""simple docstring"""







from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	bool						)  ->							torch.Tensor:
   # The first operation in a checkpoint can't be in-place, but it's
   # nice to have in-place addition during inference. Thus...
   if not inplace:
      __lowerCamelCase    		=					ma + ma
   else:
      ma += ma

   return ma



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	List[int]						)  ->							torch.Tensor:
   __lowerCamelCase    		=					-1 * len(lowercase_						)
   __lowerCamelCase    		=					list(range(len(tensor.shape[:zero_index]						)						)						)
   return tensor.permute(first_inds + [zero_index + i for i in inds]						)



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	int						)  ->							torch.Tensor:
   return t.reshape(t.shape[:-no_dims] + (-1,)						)



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	int      , lowercase_     :	float = 1e-4						)  ->							torch.Tensor:
   __lowerCamelCase    		=					mask.expand(*value.shape						)
   return torch.sum(mask * value      , dim=lowercase_						) / (eps + torch.sum(lowercase_      , dim=lowercase_						))



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.types.Number = 2.3_1_2_5      , lowercase_     :	torch.types.Number = 2_1.6_8_7_5      , lowercase_     :	int = 64						)  ->							torch.Tensor:
   __lowerCamelCase    		=					torch.linspace(lowercase_      , lowercase_      , no_bins - 1      , device=pts.device						)
   __lowerCamelCase    		=					torch.sqrt(torch.sum((pts.unsqueeze(-2						) - pts.unsqueeze(-3						)) ** 2      , dim=-1						)						)
   return torch.bucketize(lowercase_      , lowercase_						)



def       A__    (	lowercase_     :	Callable[[list], Any]      , lowercase_     :	List[dict]						)  ->							dict:
   __lowerCamelCase    		=					dicts[0]
   __lowerCamelCase    		=					{}
   for k, v in first.items():
      __lowerCamelCase    		=					[d[k] for d in dicts]
      if isinstance(lowercase_      , lowercase_						):
         __lowerCamelCase    		=					dict_multimap(lowercase_      , lowercase_						)
      else:
         __lowerCamelCase    		=					fn(lowercase_						)

   return new_dict



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor						)  ->							torch.Tensor:
   __lowerCamelCase    		=					v_bins.view(((1,) * len(x.shape						)) + (len(lowercase_						),)						)
   __lowerCamelCase    		=					x[..., None] - reshaped_bins
   __lowerCamelCase    		=					torch.argmin(torch.abs(lowercase_						)      , dim=-1						)
   return nn.functional.one_hot(lowercase_      , num_classes=len(lowercase_						)						).float()



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	int = 0      , lowercase_     :	int = 0						)  ->							torch.Tensor:
   __lowerCamelCase    		=					[]
   for i, s in enumerate(data.shape[:no_batch_dims]						):
      __lowerCamelCase    		=					torch.arange(lowercase_						)
      __lowerCamelCase    		=					r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape						) - i - 1)))						)
      ranges.append(lowercase_						)

   __lowerCamelCase    		=					[slice(lowercase_						) for _ in range(len(data.shape						) - no_batch_dims						)]
   __lowerCamelCase    		=					inds
   ranges.extend(lowercase_						)
   # Matt note: Editing this to get around the behaviour of using a list as an array index changing
   # in recent Numpy versions
   return data[tuple(lowercase_						)]


_SCREAMING_SNAKE_CASE	:		Dict   =   TypeVar('''T''')



def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	Dict[Any, Union[dict, list, tuple, T]]      , lowercase_     :	Type[T]						)  ->							Dict[Any, Union[dict, list, tuple, Any]]:
   __lowerCamelCase    		=					{}
   for k, v in dic.items():
      if isinstance(lowercase_      , lowercase_						):
         __lowerCamelCase    		=					dict_map(lowercase_      , lowercase_      , lowercase_						)
      else:
         __lowerCamelCase    		=					tree_map(lowercase_      , lowercase_      , lowercase_						)

   return new_dict



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	T      , lowercase_     :	Type[T]						)  ->							Any:
   ...



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	dict      , lowercase_     :	Type[T]						)  ->							dict:
   ...



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	list      , lowercase_     :	Type[T]						)  ->							list:
   ...



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	tuple      , lowercase_     :	Type[T]						)  ->							tuple:
   ...



def       A__    (	lowercase_     :	Optional[Any]      , lowercase_     :	Optional[int]      , lowercase_     :	List[str]						)  ->							Any:
   if isinstance(lowercase_      , lowercase_						):
      return dict_map(lowercase_      , lowercase_      , lowercase_						)
   elif isinstance(lowercase_      , lowercase_						):
      return [tree_map(lowercase_      , lowercase_      , lowercase_						) for x in tree]
   elif isinstance(lowercase_      , lowercase_						):
      return tuple(tree_map(lowercase_      , lowercase_      , lowercase_						) for x in tree						)
   elif isinstance(lowercase_      , lowercase_						):
      return fn(lowercase_						)
   else:
      print(type(lowercase_						)						)
      raise ValueError('Not supported'						)


_SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   partial(tree_map, leaf_type=torch.Tensor)







'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.openai.modeling_tf_openai import (
      TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFOpenAIGPTDoubleHeadsModel,
      TFOpenAIGPTForSequenceClassification,
      TFOpenAIGPTLMHeadModel,
      TFOpenAIGPTModel,
  )




class       __UpperCAmelCase							:





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	)						->	Any:
           A_      =  parent
           A_      =  13
           A_      =  7
           A_      =  True
           A_      =  True
           A_      =  True
           A_      =  True
           A_      =  True
           A_      =  99
           A_      =  32
           A_      =  2
           A_      =  4
           A_      =  37
           A_      =  '''gelu'''
           A_      =  0.1
           A_      =  0.1
           A_      =  512
           A_      =  16
           A_      =  2
           A_      =  0.02
           A_      =  3
           A_      =  4
           A_      =  None
           A_      =  self.vocab_size - 1

       def    __A			( self					)						->	Union[str, Any]:
           A_      =  ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size					)

           A_      =  None
           if self.use_input_mask:
               A_      =  random_attention_mask([self.batch_size, self.seq_length]					)

           A_      =  None
           if self.use_token_type_ids:
               A_      =  ids_tensor([self.batch_size, self.seq_length]       ,	self.type_vocab_size					)

           A_      =  None
           if self.use_mc_token_ids:
               A_      =  ids_tensor([self.batch_size, self.num_choices]       ,	self.seq_length					)

           A_      =  None
           A_      =  None
           A_      =  None
           if self.use_labels:
               A_      =  ids_tensor([self.batch_size]       ,	self.type_sequence_label_size					)
               A_      =  ids_tensor([self.batch_size, self.seq_length]       ,	self.num_labels					)
               A_      =  ids_tensor([self.batch_size]       ,	self.num_choices					)

           A_      =  OpenAIGPTConfig(
               vocab_size=self.vocab_size       ,	n_embd=self.hidden_size       ,	n_layer=self.num_hidden_layers       ,	n_head=self.num_attention_heads       ,	n_positions=self.max_position_embeddings       ,	pad_token_id=self.pad_token_id       ,	)

           A_      =  ids_tensor([self.num_hidden_layers, self.num_attention_heads]       ,	2					)

           return (
               config,
               input_ids,
               input_mask,
               head_mask,
               token_type_ids,
               mc_token_ids,
               sequence_labels,
               token_labels,
               choice_labels,
           )

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	*_SCREAMING_SNAKE_CASE					)						->	Optional[int]:
           A_      =  TFOpenAIGPTModel(config=_SCREAMING_SNAKE_CASE					)
           A_      =  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           A_      =  model(_SCREAMING_SNAKE_CASE					)

           A_      =  [input_ids, input_mask]
           A_      =  model(_SCREAMING_SNAKE_CASE					)

           A_      =  model(_SCREAMING_SNAKE_CASE					)

           self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	*_SCREAMING_SNAKE_CASE					)						->	Dict:
           A_      =  TFOpenAIGPTLMHeadModel(config=_SCREAMING_SNAKE_CASE					)
           A_      =  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
           A_      =  model(_SCREAMING_SNAKE_CASE					)
           self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.vocab_size)					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	*_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           A_      =  TFOpenAIGPTDoubleHeadsModel(config=_SCREAMING_SNAKE_CASE					)

           A_      =  tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE       ,	1					)       ,	(1, self.num_choices, 1)					)
           A_      =  tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE       ,	1					)       ,	(1, self.num_choices, 1)					)
           A_      =  tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE       ,	1					)       ,	(1, self.num_choices, 1)					)

           A_      =  {
               '''input_ids''': multiple_choice_inputs_ids,
               '''mc_token_ids''': mc_token_ids,
               '''attention_mask''': multiple_choice_input_mask,
               '''token_type_ids''': multiple_choice_token_type_ids,
           }
           A_      =  model(_SCREAMING_SNAKE_CASE					)
           self.parent.assertEqual(
               result.logits.shape       ,	(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)					)
           self.parent.assertEqual(result.mc_logits.shape       ,	(self.batch_size, self.num_choices)					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	*_SCREAMING_SNAKE_CASE					)						->	Dict:
           A_      =  self.num_labels
           A_      =  ids_tensor([self.batch_size]       ,	self.type_sequence_label_size					)
           A_      =  {
               '''input_ids''': input_ids,
               '''attention_mask''': input_mask,
               '''token_type_ids''': token_type_ids,
               '''labels''': sequence_labels,
           }
           A_      =  TFOpenAIGPTForSequenceClassification(_SCREAMING_SNAKE_CASE					)
           A_      =  model(_SCREAMING_SNAKE_CASE					)
           self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)					)

       def    __A			( self					)						->	Union[str, Any]:
           A_      =  self.prepare_config_and_inputs()

           (
               (
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,(
               A_
           )  ,
           )      =  config_and_inputs

           A_      =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
           return config, inputs_dict




@require_tf
class       __UpperCAmelCase							(							_UpperCamelCase		, _UpperCamelCase		, unittest.TestCase	):





       '''simple docstring'''



       __lowercase   :   Dict  					=							(
           (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
           if is_tf_available()
           else ()
       )
       __lowercase   :   List[str]  					=							(
           (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
       )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
       __lowercase   :   List[str]  					=							(
           {
               'feature-extraction': TFOpenAIGPTModel,
               'text-classification': TFOpenAIGPTForSequenceClassification,
               'text-generation': TFOpenAIGPTLMHeadModel,
               'zero-shot': TFOpenAIGPTForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       __lowercase   :   Dict  					=							False
       __lowercase   :   Optional[int]  					=							False

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Union[str, Any]:
           if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
               # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
               # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
               # tiny config could not be created.
               return True

           return False

       def    __A			( self					)						->	Any:
           A_      =  TFOpenAIGPTModelTester(self					)
           A_      =  ConfigTester(self       ,	config_class=_SCREAMING_SNAKE_CASE       ,	n_embd=37					)

       def    __A			( self					)						->	List[Any]:
           self.config_tester.run_common_tests()

       def    __A			( self					)						->	Dict:
           A_      =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_model(*_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Any:
           A_      =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_lm_head(*_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Tuple:
           A_      =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_double_head(*_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	List[str]:
           A_      =  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_SCREAMING_SNAKE_CASE					)

       @slow
       def    __A			( self					)						->	Union[str, Any]:
           for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A_      =  TFOpenAIGPTModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)




@require_tf
class       __UpperCAmelCase							(							unittest.TestCase	):





       '''simple docstring'''



       @slow
       def    __A			( self					)						->	List[str]:
           A_      =  TFOpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''					)
           A_      =  tf.convert_to_tensor([[481, 4735, 544]]       ,	dtype=tf.intaa					)  # the president is
           A_      =  [
               481,
               4735,
               544,
               246,
               963,
               870,
               762,
               239,
               244,
               4_0477,
               244,
               249,
               719,
               881,
               487,
               544,
               240,
               244,
               603,
               481,
           ]  # the president is a very good man. " \n " i\'m sure he is, " said the

           A_      =  model.generate(_SCREAMING_SNAKE_CASE       ,	do_sample=_SCREAMING_SNAKE_CASE					)
           self.assertListEqual(output_ids[0].numpy().tolist()       ,	_SCREAMING_SNAKE_CASE					)






