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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=None  ,			):
    '''simple docstring'''







    if attention_mask is None:
        __UpperCamelCase     :List[str]			  =		input_ids.ne(config.pad_token_id	)
    if decoder_attention_mask is None:
        __UpperCamelCase     :Tuple			  =		decoder_input_ids.ne(config.pad_token_id	)
    if head_mask is None:
        __UpperCamelCase     :Dict			  =		torch.ones(config.encoder_layers  ,			config.encoder_attention_heads  ,			device=SCREAMING_SNAKE_CASE	)
    if decoder_head_mask is None:
        __UpperCamelCase     :int			  =		torch.ones(config.decoder_layers  ,			config.decoder_attention_heads  ,			device=SCREAMING_SNAKE_CASE	)
    if cross_attn_head_mask is None:
        __UpperCamelCase     :Union[str, Any]			  =		torch.ones(config.decoder_layers  ,			config.decoder_attention_heads  ,			device=SCREAMING_SNAKE_CASE	)
    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 	lowerCamelCase_     :






    '''simple docstring'''






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







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :Tuple			  =		ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
        __UpperCamelCase     :Any			  =		self.eos_token_id  # Eos Token
        __UpperCamelCase     :Tuple			  =		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
        __UpperCamelCase     :Union[str, Any]			  =		input_ids.clamp(self.pad_token_id + 1)
        __UpperCamelCase     :List[str]			  =		decoder_input_ids.clamp(self.pad_token_id + 1)

        __UpperCamelCase     :Any			  =		self.get_config()
        __UpperCamelCase     :List[Any]			  =		prepare_mam_aaa_inputs_dict(__lowercase , __lowercase , __lowercase)
        return config, inputs_dict







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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :Tuple			  =		MaMaaaModel(config=__lowercase).get_decoder().to(__lowercase).eval()
        __UpperCamelCase     :str			  =		inputs_dict['''input_ids''']
        __UpperCamelCase     :Union[str, Any]			  =		inputs_dict['''attention_mask''']
        __UpperCamelCase     :List[str]			  =		inputs_dict['''head_mask''']

        # first forward pass
        __UpperCamelCase     :Dict			  =		model(__lowercase , attention_mask=__lowercase , head_mask=__lowercase , use_cache=__lowercase)

        __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		outputs.to_tuple()

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

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

        __UpperCamelCase     :Union[str, Any]			  =		model(__lowercase , attention_mask=__lowercase)['''last_hidden_state''']
        __UpperCamelCase     :str			  =		model(__lowercase , attention_mask=__lowercase , past_key_values=__lowercase)[
            '''last_hidden_state'''
        ]

        # select random slice
        __UpperCamelCase     :List[str]			  =		ids_tensor((1,) , output_from_past.shape[-1]).item()
        __UpperCamelCase     :Optional[int]			  =		output_from_no_past[:, -3:, random_slice_idx].detach()
        __UpperCamelCase     :Optional[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 , __lowercase , __lowercase)       ->	int:
        __UpperCamelCase     :Dict			  =		MaMaaaModel(config=__lowercase).to(__lowercase).eval()
        __UpperCamelCase     :Tuple			  =		model(**__lowercase)

        __UpperCamelCase     :List[Any]			  =		outputs.encoder_last_hidden_state
        __UpperCamelCase     :List[str]			  =		outputs.last_hidden_state

        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase     :List[str]			  =		model.get_encoder()
            encoder.save_pretrained(__lowercase)
            __UpperCamelCase     :Union[str, Any]			  =		MaMaaaEncoder.from_pretrained(__lowercase).to(__lowercase)

        __UpperCamelCase     :Tuple			  =		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:
            __UpperCamelCase     :Any			  =		model.get_decoder()
            decoder.save_pretrained(__lowercase)
            __UpperCamelCase     :str			  =		MaMaaaDecoder.from_pretrained(__lowercase).to(__lowercase)

        __UpperCamelCase     :Any			  =		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 	lowerCamelCase_     (   UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      (
        (
            MaMaaaModel,
            MaMaaaForConditionalGeneration,
        )
        if is_torch_available()
        else ()
    )
    a__	:						str						=      (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
    a__	:						str						=      (
        {
            """conversational""": MaMaaaForConditionalGeneration,
            """feature-extraction""": MaMaaaModel,
            """summarization""": MaMaaaForConditionalGeneration,
            """text2text-generation""": MaMaaaForConditionalGeneration,
            """translation""": MaMaaaForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    a__	:						int						=      True
    a__	:						int						=      True
    a__	:						Union[str, Any]						=      False
    a__	:						int						=      False







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase)       ->	Union[str, Any]:
        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)       ->	Dict:
        __UpperCamelCase     :Optional[int]			  =		MaMaaaModelTester(self)
        __UpperCamelCase     :Optional[int]			  =		ConfigTester(self , config_class=__lowercase)







    def 			UpperCamelCase__   (	self)       ->	str:
        self.config_tester.run_common_tests()







    def 			UpperCamelCase__   (	self)       ->	int:
        __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            __UpperCamelCase     :str			  =		model_class(__lowercase)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(__lowercase)
                __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		model_class.from_pretrained(__lowercase , output_loading_info=__lowercase)
            self.assertEqual(info['''missing_keys'''] , [])







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :Optional[int]			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowercase)







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :Any			  =		self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_encoder_decoder_model_standalone(*__lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase  ,       __UpperCamelCase     :Optional[int]			  =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
            __UpperCamelCase     :Optional[Any]			  =		model_class(__lowercase)
            model.to(__lowercase)
            model.eval()

            __UpperCamelCase     :Union[str, Any]			  =		copy.deepcopy(self._prepare_for_class(__lowercase , __lowercase))

            if not self.is_encoder_decoder:
                __UpperCamelCase     :Dict			  =		inputs['''input_ids''']
                del inputs["input_ids"]
            else:
                __UpperCamelCase     :str			  =		inputs['''input_ids''']
                __UpperCamelCase     :Dict			  =		inputs.get('''decoder_input_ids''' , __lowercase)
                del inputs["input_ids"]
                inputs.pop('''decoder_input_ids''' , __lowercase)

            __UpperCamelCase     :Union[str, Any]			  =		model.get_input_embeddings()
            if not self.is_encoder_decoder:
                __UpperCamelCase     :List[Any]			  =		wte(__lowercase)
            else:
                __UpperCamelCase     :int			  =		wte(__lowercase)
                __UpperCamelCase     :List[str]			  =		wte(__lowercase)

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







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		self.model_tester.prepare_config_and_inputs()
        __UpperCamelCase     :Dict			  =		input_dict['''input_ids''']
        __UpperCamelCase     :Any			  =		input_ids.ne(1).to(__lowercase)
        __UpperCamelCase     :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 lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    return torch.tensor(SCREAMING_SNAKE_CASE  ,			dtype=torch.long  ,			device=SCREAMING_SNAKE_CASE	)


__lowercase							 =					1e-4


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






    '''simple docstring'''






    @cached_property
    def 			UpperCamelCase__   (	self)       ->	int:
        return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M''')







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :Union[str, Any]			  =		MaMaaaModel.from_pretrained('''facebook/m2m100_418M''').to(__lowercase)
        __UpperCamelCase     :Dict			  =		_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]])
        __UpperCamelCase     :int			  =		_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]])
        __UpperCamelCase     :int			  =		prepare_mam_aaa_inputs_dict(model.config , __lowercase , __lowercase)
        with torch.no_grad():
            __UpperCamelCase     :List[Any]			  =		model(**__lowercase)[0]
        __UpperCamelCase     :Optional[Any]			  =		torch.Size((1, 11, 1_024))
        self.assertEqual(output.shape , __lowercase)
        # change to expected output here
        __UpperCamelCase     :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)       ->	List[str]:
        __UpperCamelCase     :Optional[int]			  =		MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M''').to(__lowercase)

        # change to intended input
        __UpperCamelCase     :List[Any]			  =		_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]])
        __UpperCamelCase     :str			  =		_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]])
        __UpperCamelCase     :List[Any]			  =		prepare_mam_aaa_inputs_dict(model.config , __lowercase , __lowercase)
        with torch.no_grad():
            __UpperCamelCase     :Optional[int]			  =		model(**__lowercase)[0]
        __UpperCamelCase     :List[Any]			  =		torch.Size((1, 11, model.config.vocab_size))
        self.assertEqual(output.shape , __lowercase)
        # change to expected output here
        __UpperCamelCase     :int			  =		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[Any]:
        __UpperCamelCase     :Tuple			  =		MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M''').to(__lowercase)
        __UpperCamelCase     :Optional[int]			  =		MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M''' , src_lang='''fr''' , tgt_lang='''en''')

        __UpperCamelCase     :Optional[int]			  =		[
            '''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
        __UpperCamelCase     :List[str]			  =		tokenizer(__lowercase , padding=__lowercase , return_tensors='''pt''')

        __UpperCamelCase     :Dict			  =		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''') , )

        __UpperCamelCase     :Any			  =		[
            '''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.''',
        ]

        __UpperCamelCase     :str			  =		tokenizer.batch_decode(
            hypotheses_batch.tolist() , clean_up_tokenization_spaces=__lowercase , skip_special_tokens=__lowercase)
        assert generated == expected_en






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		AlbertConfig.from_json_file(SCREAMING_SNAKE_CASE	)
    print(f"""Building PyTorch model from configuration: {config}"""	)
    __UpperCamelCase     :List[str]			  =		AlbertForPreTraining(SCREAMING_SNAKE_CASE	)

    # Load weights from tf checkpoint
    load_tf_weights_in_albert(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
    torch.save(model.state_dict()  ,			SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    __lowercase							 =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--albert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained ALBERT model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __lowercase							 =					parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





