import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

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 MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
      from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder

def 			lowercase_				(			_lowerCamelCase							:       List[str]		,	_lowerCamelCase							:       Any		,	_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       str=None		,	_lowerCamelCase							:       Union[str, Any]=None		,	_lowerCamelCase							:       Tuple=None		,	_lowerCamelCase							:       Dict=None		,	_lowerCamelCase							:       Union[str, Any]=None		,	):
    if attention_mask is None:
        lowercase__ :   Dict    					=				input_ids.ne(config.pad_token_id)
    if decoder_attention_mask is None:
        lowercase__ :   Any    					=				decoder_input_ids.ne(config.pad_token_id)
    if head_mask is None:
        lowercase__ :   Optional[int]    					=				torch.ones(config.encoder_layers		,	config.encoder_attention_heads		,	device=_lowerCamelCase)
    if decoder_head_mask is None:
        lowercase__ :   Dict    					=				torch.ones(config.decoder_layers		,	config.decoder_attention_heads		,	device=_lowerCamelCase)
    if cross_attn_head_mask is None:
        lowercase__ :   List[str]    					=				torch.ones(config.decoder_layers		,	config.decoder_attention_heads		,	device=_lowerCamelCase)
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }



class   snake_case_       :






   def __init__(      self				:     Union[str, Any]	,						lowercase_				:     Any	,						lowercase_				:     Optional[int]=13	,						lowercase_				:     Any=7	,						lowercase_				:     Tuple=True	,						lowercase_				:     Union[str, Any]=False	,						lowercase_				:     Any=99	,						lowercase_				:     Tuple=16	,						lowercase_				:     int=2	,						lowercase_				:     List[str]=4	,						lowercase_				:     List[str]=4	,						lowercase_				:     Any="relu"	,						lowercase_				:     Optional[Any]=0.1	,						lowercase_				:     Optional[int]=0.1	,						lowercase_				:     int=0.0	,						lowercase_				:     Dict=0.0	,						lowercase_				:     List[str]=20	,						lowercase_				:     Dict=2	,						lowercase_				:     int=1	,						lowercase_				:     Dict=0	,						)					->						str:
       lowercase__ :   List[Any]    					=				parent
       lowercase__ :   int    					=				batch_size
       lowercase__ :   Optional[Any]    					=				seq_length
       lowercase__ :   Dict    					=				is_training
       lowercase__ :   List[str]    					=				use_labels
       lowercase__ :   Dict    					=				vocab_size
       lowercase__ :   List[Any]    					=				hidden_size
       lowercase__ :   Optional[int]    					=				num_hidden_layers
       lowercase__ :   Any    					=				num_attention_heads
       lowercase__ :   str    					=				intermediate_size
       lowercase__ :   Optional[Any]    					=				hidden_act
       lowercase__ :   str    					=				hidden_dropout_prob
       lowercase__ :   Union[str, Any]    					=				attention_probs_dropout_prob
       lowercase__ :   Optional[Any]    					=				encoder_layerdrop
       lowercase__ :   Optional[int]    					=				decoder_layerdrop
       lowercase__ :   Dict    					=				max_position_embeddings
       lowercase__ :   List[Any]    					=				eos_token_id
       lowercase__ :   Dict    					=				pad_token_id
       lowercase__ :   Union[str, Any]    					=				bos_token_id






   def       __UpperCamelCase     (      self				:     Optional[Any]  )					->						int:
       lowercase__ :   Optional[Any]    					=				ids_tensor([self.batch_size, self.seq_length]	,						self.vocab_size  )
       lowercase__ :   Optional[int]    					=				self.eos_token_id  # Eos Token
       lowercase__ :   int    					=				ids_tensor([self.batch_size, self.seq_length]	,						self.vocab_size  )

       # we need to clamp the input ids here to avoid having pad token in between
       # this is because for M2M100 the position_ids are prepared such that
       # all pad tokens have pos id = 2 and rest are between 2..seq_length
       # and the seq_length here is seq_length - num_pad_tokens
       # but when using past, there is no way of knowing if the past input ids had
       # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
       # position_ids being off by num_pad_tokens in past input
       lowercase__ :   Union[str, Any]    					=				input_ids.clamp(self.pad_token_id + 1  )
       lowercase__ :   Any    					=				decoder_input_ids.clamp(self.pad_token_id + 1  )

       lowercase__ :   Tuple    					=				self.get_config()
       lowercase__ :   Tuple    					=				prepare_mam_aaa_inputs_dict(lowercase_	,						lowercase_	,						lowercase_  )
       return config, inputs_dict






   def       __UpperCamelCase     (      self				:     Dict  )					->						Optional[int]:
       return MaMaaaConfig(
           vocab_size=self.vocab_size	,						d_model=self.hidden_size	,						encoder_layers=self.num_hidden_layers	,						decoder_layers=self.num_hidden_layers	,						encoder_attention_heads=self.num_attention_heads	,						decoder_attention_heads=self.num_attention_heads	,						encoder_ffn_dim=self.intermediate_size	,						decoder_ffn_dim=self.intermediate_size	,						dropout=self.hidden_dropout_prob	,						attention_dropout=self.attention_probs_dropout_prob	,						encoder_layerdrop=self.encoder_layerdrop	,						decoder_layerdrop=self.decoder_layerdrop	,						max_position_embeddings=self.max_position_embeddings	,						eos_token_id=self.eos_token_id	,						bos_token_id=self.bos_token_id	,						pad_token_id=self.pad_token_id	,						)






   def       __UpperCamelCase     (      self				:     List[Any]  )					->						Any:
       lowercase__					,   lowercase__ :   Tuple    					=				self.prepare_config_and_inputs()
       return config, inputs_dict






   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     List[Any]	,						lowercase_				:     int  )					->						Union[str, Any]:
       lowercase__ :   str    					=				MaMaaaModel(config=lowercase_  ).get_decoder().to(lowercase_  ).eval()
       lowercase__ :   Tuple    					=				inputs_dict["input_ids"]
       lowercase__ :   List[Any]    					=				inputs_dict["attention_mask"]
       lowercase__ :   Any    					=				inputs_dict["head_mask"]

       # first forward pass
       lowercase__ :   Optional[int]    					=				model(lowercase_	,						attention_mask=lowercase_	,						head_mask=lowercase_	,						use_cache=lowercase_  )

       lowercase__					,   lowercase__ :   List[Any]    					=				outputs.to_tuple()

       # create hypothetical multiple next token and extent to next_input_ids
       lowercase__ :   List[Any]    					=				ids_tensor((self.batch_size, 3)	,						config.vocab_size  )
       lowercase__ :   Any    					=				ids_tensor((self.batch_size, 3)	,						2  )

       # append to next input_ids and
       lowercase__ :   List[str]    					=				torch.cat([input_ids, next_tokens]	,						dim=-1  )
       lowercase__ :   Tuple    					=				torch.cat([attention_mask, next_attn_mask]	,						dim=-1  )

       lowercase__ :   Tuple    					=				model(lowercase_	,						attention_mask=lowercase_  )["last_hidden_state"]
       lowercase__ :   List[Any]    					=				model(lowercase_	,						attention_mask=lowercase_	,						past_key_values=lowercase_  )[
           "last_hidden_state"
       ]

       # select random slice
       lowercase__ :   Union[str, Any]    					=				ids_tensor((1,)	,						output_from_past.shape[-1]  ).item()
       lowercase__ :   int    					=				output_from_no_past[:, -3:, random_slice_idx].detach()
       lowercase__ :   Union[str, Any]    					=				output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(lowercase_	,						lowercase_	,						atol=1E-2  )  )







   def       __UpperCamelCase     (      self				:     Optional[int]	,						lowercase_				:     Optional[int]	,						lowercase_				:     List[Any]  )					->						Dict:
       lowercase__ :   Optional[Any]    					=				MaMaaaModel(config=lowercase_  ).to(lowercase_  ).eval()
       lowercase__ :   str    					=				model(**lowercase_  )

       lowercase__ :   Union[str, Any]    					=				outputs.encoder_last_hidden_state
       lowercase__ :   Dict    					=				outputs.last_hidden_state

       with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase__ :   List[str]    					=				model.get_encoder()
           encoder.save_pretrained(lowercase_  )
           lowercase__ :   int    					=				MaMaaaEncoder.from_pretrained(lowercase_  ).to(lowercase_  )

       lowercase__ :   str    					=				encoder(inputs_dict["input_ids"]	,						attention_mask=inputs_dict["attention_mask"]  )[
           0
       ]

       self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3  )

       with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase__ :   Optional[int]    					=				model.get_decoder()
           decoder.save_pretrained(lowercase_  )
           lowercase__ :   int    					=				MaMaaaDecoder.from_pretrained(lowercase_  ).to(lowercase_  )

       lowercase__ :   List[str]    					=				decoder(
           input_ids=inputs_dict["decoder_input_ids"]	,						attention_mask=inputs_dict["decoder_attention_mask"]	,						encoder_hidden_states=lowercase_	,						encoder_attention_mask=inputs_dict["attention_mask"]	,						)[0]

       self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3  )



@require_torch
class   snake_case_       (		__A  ,__A  ,__A  ,unittest.TestCase       ):
   __A	:		Optional[Any]         =				(
       (
           MaMaaaModel,
           MaMaaaForConditionalGeneration,
       )
       if is_torch_available()
       else ()
   )
   __A	:		Optional[Any]         =				(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
   __A	:		Dict         =				(
       {
           "conversational": MaMaaaForConditionalGeneration,
           "feature-extraction": MaMaaaModel,
           "summarization": MaMaaaForConditionalGeneration,
           "text2text-generation": MaMaaaForConditionalGeneration,
           "translation": MaMaaaForConditionalGeneration,
       }
       if is_torch_available()
       else {}
   )
   __A	:		str         =				True
   __A	:		List[str]         =				True
   __A	:		List[Any]         =				False
   __A	:		List[Any]         =				False






   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     List[Any]	,						lowercase_				:     List[str]	,						lowercase_				:     List[str]	,						lowercase_				:     Tuple	,						lowercase_				:     int  )					->						str:
       if pipeline_test_casse_name == "TranslationPipelineTests":
           # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
           # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
           return True

       return False






   def       __UpperCamelCase     (      self				:     List[Any]  )					->						Dict:
       lowercase__ :   List[Any]    					=				MaMaaaModelTester(self  )
       lowercase__ :   str    					=				ConfigTester(self	,						config_class=lowercase_  )






   def       __UpperCamelCase     (      self				:     List[str]  )					->						Optional[Any]:
       self.config_tester.run_common_tests()






   def       __UpperCamelCase     (      self				:     List[str]  )					->						Optional[int]:
       lowercase__					,   lowercase__ :   str    					=				self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
           lowercase__ :   int    					=				model_class(lowercase_  )

           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(lowercase_  )
               lowercase__					,   lowercase__ :   int    					=				model_class.from_pretrained(lowercase_	,						output_loading_info=lowercase_  )
           self.assertEqual(info["missing_keys"]	,						[]  )






   def       __UpperCamelCase     (      self				:     Dict  )					->						str:
       lowercase__ :   List[str]    					=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase_  )






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Optional[Any]:
       lowercase__ :   Any    					=				self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_encoder_decoder_model_standalone(*lowercase_  )






   def       __UpperCamelCase     (      self				:     Optional[Any]  )					->						Optional[Any]:
       lowercase__					,   lowercase__ :   Tuple    					=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
           lowercase__ :   Union[str, Any]    					=				model_class(lowercase_  )
           model.to(lowercase_  )
           model.eval()

           lowercase__ :   List[Any]    					=				copy.deepcopy(self._prepare_for_class(lowercase_	,						lowercase_  )  )

           if not self.is_encoder_decoder:
               lowercase__ :   Optional[int]    					=				inputs["input_ids"]
               del inputs["input_ids"]
           else:
               lowercase__ :   str    					=				inputs["input_ids"]
               lowercase__ :   Optional[Any]    					=				inputs.get("decoder_input_ids"	,						lowercase_  )
               del inputs["input_ids"]
               inputs.pop("decoder_input_ids"	,						lowercase_  )

           lowercase__ :   Optional[int]    					=				model.get_input_embeddings()
           if not self.is_encoder_decoder:
               lowercase__ :   int    					=				wte(lowercase_  )
           else:
               lowercase__ :   str    					=				wte(lowercase_  )
               lowercase__ :   str    					=				wte(lowercase_  )

           with torch.no_grad():
               model(**lowercase_  )[0]







   def       __UpperCamelCase     (      self				:     Tuple  )					->						str:
       lowercase__					,   lowercase__ :   Tuple    					=				self.model_tester.prepare_config_and_inputs()
       lowercase__ :   Any    					=				input_dict["input_ids"]
       lowercase__ :   List[Any]    					=				input_ids.ne(1  ).to(lowercase_  )
       lowercase__ :   Optional[int]    					=				MaMaaaForConditionalGeneration(lowercase_  ).eval().to(lowercase_  )
       if torch_device == "cuda":
           model.half()
       model.generate(lowercase_	,						attention_mask=lowercase_  )
       model.generate(num_beams=4	,						do_sample=lowercase_	,						early_stopping=lowercase_	,						num_return_sequences=3  )



def 			lowercase_				(			_lowerCamelCase							:       Any):
    return torch.tensor(_lowerCamelCase		,	dtype=torch.long		,	device=_lowerCamelCase)


UpperCamelCase  							=						1E-4



@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class   snake_case_       (		unittest.TestCase       ):






   @cached_property
   def       __UpperCamelCase     (      self				:     Tuple  )					->						Tuple:
       return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"  )






   def       __UpperCamelCase     (      self				:     str  )					->						Optional[int]:
       lowercase__ :   Optional[Any]    					=				MaMaaaModel.from_pretrained("facebook/m2m100_418M"  ).to(lowercase_  )
       lowercase__ :   int    					=				_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]  )
       lowercase__ :   Optional[Any]    					=				_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]  )
       lowercase__ :   int    					=				prepare_mam_aaa_inputs_dict(model.config	,						lowercase_	,						lowercase_  )
       with torch.no_grad():
           lowercase__ :   Any    					=				model(**lowercase_  )[0]
       lowercase__ :   Any    					=				torch.Size((1, 11, 10_24)  )
       self.assertEqual(output.shape	,						lowercase_  )
       # change to expected output here
       lowercase__ :   Tuple    					=				torch.tensor(
           [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]]	,						device=lowercase_  )
       self.assertTrue(torch.allclose(output[:, :3, :3]	,						lowercase_	,						atol=lowercase_  )  )






   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						List[str]:
       lowercase__ :   Optional[Any]    					=				MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"  ).to(lowercase_  )

       # change to intended input
       lowercase__ :   Optional[Any]    					=				_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]  )
       lowercase__ :   Any    					=				_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]  )
       lowercase__ :   Optional[int]    					=				prepare_mam_aaa_inputs_dict(model.config	,						lowercase_	,						lowercase_  )
       with torch.no_grad():
           lowercase__ :   List[str]    					=				model(**lowercase_  )[0]
       lowercase__ :   Optional[Any]    					=				torch.Size((1, 11, model.config.vocab_size)  )
       self.assertEqual(output.shape	,						lowercase_  )
       # change to expected output here
       lowercase__ :   List[Any]    					=				torch.tensor(
           [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]]	,						device=lowercase_  )
       self.assertTrue(torch.allclose(output[:, :3, :3]	,						lowercase_	,						atol=lowercase_  )  )







   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Tuple:
       lowercase__ :   List[Any]    					=				MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"  ).to(lowercase_  )
       lowercase__ :   int    					=				MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"	,						src_lang="fr"	,						tgt_lang="en"  )

       lowercase__ :   Union[str, Any]    					=				[
           "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
           "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
           "Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
           " Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
           " l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
       ]

       # The below article tests that we don't add any hypotheses outside of the top n_beams
       lowercase__ :   Optional[int]    					=				tokenizer(lowercase_	,						padding=lowercase_	,						return_tensors="pt"  )

       lowercase__ :   str    					=				model.generate(
           input_ids=dct["input_ids"].to(lowercase_  )	,						attention_mask=dct["attention_mask"].to(lowercase_  )	,						num_beams=5	,						forced_bos_token_id=tokenizer.get_lang_id("en"  )	,						)

       lowercase__ :   Optional[int]    					=				[
           "The NSA case highlights the total absence of intelligence debate",
           "I think there are two levels of response from the French government.",
           "When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
           " Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
           " communications in France.",
       ]

       lowercase__ :   List[str]    					=				tokenizer.batch_decode(
           hypotheses_batch.tolist()	,						clean_up_tokenization_spaces=lowercase_	,						skip_special_tokens=lowercase_  )
       assert generated == expected_en






"""simple docstring"""


import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Optional[int]:
		# Construct model
		if gpta_config_file == "":
				__lowerCAmelCase:       Optional[int]          =			GPTaConfig()
		else:
				__lowerCAmelCase:       List[str]          =			GPTaConfig.from_json_file(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Optional[int]          =			GPTaModel(__SCREAMING_SNAKE_CASE )

		# Load weights from numpy
		load_tf_weights_in_gpta(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )

		# Save pytorch-model
		__lowerCAmelCase:       str          =			pytorch_dump_folder_path + "/" + WEIGHTS_NAME
		__lowerCAmelCase:       List[Any]          =			pytorch_dump_folder_path + "/" + CONFIG_NAME
		print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
		torch.save(model.state_dict()					, __SCREAMING_SNAKE_CASE )
		print(F"Save configuration file to {pytorch_config_dump_path}" )
		with open(__SCREAMING_SNAKE_CASE					, "w"					, encoding="utf-8" ) as f:
				f.write(config.to_json_string() )


if __name__ == "__main__":
			__A						   =			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			parser.add_argument(
			    "--gpt2_config_file",
			    default="",
			    type=str,
			    help=(
			        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
			        "This specifies the model architecture."
			    ),
			)
			__A						   =			parser.parse_args()
			convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


