

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    OpenAIGPTConfig,
					    OpenAIGPTDoubleHeadsModel,
					    OpenAIGPTForSequenceClassification,
					    OpenAIGPTLMHeadModel,
					    OpenAIGPTModel,
					)

class 			a					:






						"""simple docstring"""




						def __init__(    self  ,   A_  ,   A_=13  ,   A_=7  ,   A_=True  ,   A_=True  ,   A_=True  ,   A_=99  ,   A_=32  ,   A_=5  ,   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  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      parent
										_UpperCAmelCase    :						Optional[Any]							=      batch_size
										_UpperCAmelCase    :						Optional[int]							=      seq_length
										_UpperCAmelCase    :						Tuple							=      is_training
										_UpperCAmelCase    :						Union[str, Any]							=      use_token_type_ids
										_UpperCAmelCase    :						int							=      use_labels
										_UpperCAmelCase    :						Tuple							=      vocab_size
										_UpperCAmelCase    :						int							=      hidden_size
										_UpperCAmelCase    :						str							=      num_hidden_layers
										_UpperCAmelCase    :						str							=      num_attention_heads
										_UpperCAmelCase    :						Any							=      intermediate_size
										_UpperCAmelCase    :						Union[str, Any]							=      hidden_act
										_UpperCAmelCase    :						Optional[Any]							=      hidden_dropout_prob
										_UpperCAmelCase    :						Dict							=      attention_probs_dropout_prob
										_UpperCAmelCase    :						int							=      max_position_embeddings
										_UpperCAmelCase    :						List[str]							=      type_vocab_size
										_UpperCAmelCase    :						Optional[Any]							=      type_sequence_label_size
										_UpperCAmelCase    :						Tuple							=      initializer_range
										_UpperCAmelCase    :						List[str]							=      num_labels
										_UpperCAmelCase    :						str							=      num_choices
										_UpperCAmelCase    :						Optional[int]							=      scope
										_UpperCAmelCase    :						Union[str, Any]							=      self.vocab_size - 1


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      ids_tensor([self.batch_size, self.seq_length]  ,   self.vocab_size		)

										_UpperCAmelCase    :						List[Any]							=      None
										if self.use_token_type_ids:
														_UpperCAmelCase    :						Union[str, Any]							=      ids_tensor([self.batch_size, self.seq_length]  ,   self.type_vocab_size		)

										_UpperCAmelCase    :						Union[str, Any]							=      None
										_UpperCAmelCase    :						int							=      None
										_UpperCAmelCase    :						Optional[int]							=      None
										if self.use_labels:
														_UpperCAmelCase    :						int							=      ids_tensor([self.batch_size]  ,   self.type_sequence_label_size		)
														_UpperCAmelCase    :						str							=      ids_tensor([self.batch_size, self.seq_length]  ,   self.num_labels		)
														_UpperCAmelCase    :						Optional[int]							=      ids_tensor([self.batch_size]  ,   self.num_choices		)

										_UpperCAmelCase    :						Dict							=      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  ,   )

										_UpperCAmelCase    :						Union[str, Any]							=      ids_tensor([self.num_hidden_layers, self.num_attention_heads]  ,   2		)

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


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   *A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      OpenAIGPTModel(config=A_		)
										model.to(A_		)
										model.eval()

										_UpperCAmelCase    :						Dict							=      model(A_  ,   token_type_ids=A_  ,   head_mask=A_		)
										_UpperCAmelCase    :						str							=      model(A_  ,   token_type_ids=A_		)
										_UpperCAmelCase    :						Any							=      model(A_		)

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


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   *A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      OpenAIGPTLMHeadModel(A_		)
										model.to(A_		)
										model.eval()

										_UpperCAmelCase    :						Optional[Any]							=      model(A_  ,   token_type_ids=A_  ,   labels=A_		)
										self.parent.assertEqual(result.loss.shape  ,   ()		)
										self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.seq_length, self.vocab_size)		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   *A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      OpenAIGPTDoubleHeadsModel(A_		)
										model.to(A_		)
										model.eval()

										_UpperCAmelCase    :						int							=      model(A_  ,   token_type_ids=A_  ,   labels=A_		)
										self.parent.assertEqual(result.loss.shape  ,   ()		)
										self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.seq_length, self.vocab_size)		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   *A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      self.num_labels
										_UpperCAmelCase    :						Dict							=      OpenAIGPTForSequenceClassification(A_		)
										model.to(A_		)
										model.eval()

										_UpperCAmelCase    :						Union[str, Any]							=      ids_tensor([self.batch_size]  ,   self.type_sequence_label_size		)
										_UpperCAmelCase    :						int							=      model(A_  ,   token_type_ids=A_  ,   labels=A_		)
										self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.num_labels)		)





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.prepare_config_and_inputs()
										(
										    _UpperCAmelCase							
										)    :						Any							=      config_and_inputs
										_UpperCAmelCase    :						Dict							=      {
										    "input_ids": input_ids,
										    "token_type_ids": token_type_ids,
										    "head_mask": head_mask,
										}

										return config, inputs_dict







@require_torch
class 			a					(		UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	unittest.TestCase			):






						"""simple docstring"""




						_lowercase        =     (
						    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
						    if is_torch_available()
						    else ()
						)
						_lowercase        =     (
						    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
						)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
						_lowercase        =     (
						    {
						        "feature-extraction": OpenAIGPTModel,
						        "text-classification": OpenAIGPTForSequenceClassification,
						        "text-generation": OpenAIGPTLMHeadModel,
						        "zero-shot": OpenAIGPTForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										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     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_=False		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      super()._prepare_for_class(A_  ,   A_  ,   return_labels=A_		)

										if return_labels:
														if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																		_UpperCAmelCase    :						Optional[Any]							=      torch.zeros(
																		    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)  ,   dtype=torch.long  ,   device=A_  ,   )
																		_UpperCAmelCase    :						List[str]							=      inputs_dict["labels"]
																		_UpperCAmelCase    :						Tuple							=      inputs_dict["labels"]
																		_UpperCAmelCase    :						int							=      torch.zeros(
																		    (self.model_tester.batch_size, self.model_tester.num_choices)  ,   dtype=torch.long  ,   device=A_  ,   )
																		_UpperCAmelCase    :						Tuple							=      torch.zeros(
																		    self.model_tester.batch_size  ,   dtype=torch.long  ,   device=A_		)
										return inputs_dict


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      OpenAIGPTModelTester(self		)
										_UpperCAmelCase    :						Union[str, Any]							=      ConfigTester(self  ,   config_class=A_  ,   n_embd=37		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_model(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head_model(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_double_lm_head_model(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*A_		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						Dict							=      OpenAIGPTModel.from_pretrained(A_		)
														self.assertIsNotNone(A_		)







@require_torch
class 			a					(		unittest.TestCase			):






						"""simple docstring"""




						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      OpenAIGPTLMHeadModel.from_pretrained("openai-gpt"		)
										model.to(A_		)
										_UpperCAmelCase    :						Tuple							=      torch.tensor([[481, 4735, 544]]  ,   dtype=torch.long  ,   device=A_		)  # the president is
										_UpperCAmelCase    :						Optional[Any]							=      [
										    481,
										    4735,
										    544,
										    246,
										    963,
										    870,
										    762,
										    239,
										    244,
										    40477,
										    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

										_UpperCAmelCase    :						Optional[int]							=      model.generate(A_  ,   do_sample=A_		)
										self.assertListEqual(output_ids[0].tolist()  ,   A_		)





import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = [
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    Tuple:
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								_UpperCAmelCase    :						Optional[int]							=      k.replace(lowerCAmelCase					, lowerCAmelCase	)

				if k.startswith("encoder"	):
								_UpperCAmelCase    :						Tuple							=      k.replace(".attn"					, ".self_attn"	)
								_UpperCAmelCase    :						str							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Optional[Any]							=      k.replace("norm2"					, "final_layer_norm"	)
				elif k.startswith("decoder"	):
								_UpperCAmelCase    :						Union[str, Any]							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Tuple							=      k.replace("norm2"					, "encoder_attn_layer_norm"	)
								_UpperCAmelCase    :						Any							=      k.replace("norm3"					, "final_layer_norm"	)
				return k

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						int							=      [
				    "model.encoder.layernorm_embedding.weight",
				    "model.encoder.layernorm_embedding.bias",
				    "model.decoder.layernorm_embedding.weight",
				    "model.decoder.layernorm_embedding.bias",
				]
				for k in keys:
								_UpperCAmelCase    :						Dict							=      sd.pop(lowerCAmelCase	)
								_UpperCAmelCase    :						str							=      k.replace("layernorm_embedding"					, "layer_norm"	)
								assert new_k not in sd
								_UpperCAmelCase    :						Dict							=      v


SCREAMING_SNAKE_CASE_            = ['START']

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						Union[str, Any]							=      torch.load(lowerCAmelCase					, map_location="cpu"	)
				_UpperCAmelCase    :						List[str]							=      model["model"]
				_UpperCAmelCase    :						List[str]							=      BlenderbotConfig.from_json_file(lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      BlenderbotForConditionalGeneration(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      m.model.state_dict().keys()
				_UpperCAmelCase    :						List[Any]							=      []
				_UpperCAmelCase    :						List[str]							=      {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

								_UpperCAmelCase    :						Tuple							=      rename_state_dict_key(lowerCAmelCase	)
								if new_k not in valid_keys:
												failures.append([k, new_k]	)
								else:
												_UpperCAmelCase    :						Tuple							=      v
				if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
								rename_layernorm_keys(lowerCAmelCase	)
				m.model.load_state_dict(lowerCAmelCase					, strict=lowerCAmelCase	)
				m.half()
				m.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
					parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
					parser.add_argument(
					    '--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


