






from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__lowerCamelCase      :    Any   		=   """facebook/wmt19-en-de"""
__lowerCamelCase      :    Optional[Any]   		=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCamelCase      :    Optional[Any]   		=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__lowerCamelCase      :    List[str]   		=   FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")

# Test
__lowerCamelCase      :    Tuple   		=   tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__lowerCamelCase      :    Union[str, Any]   		=   tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
__lowerCamelCase      :    Dict   		=   """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-de











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





					"""simple docstring"""






					def __init__(      self  :						List[str]							,	__A  :						Any							,	__A  :						List[str]=1_3							,	__A  :						List[Any]=7							,	__A  :						Union[str, Any]=True							,	__A  :						Union[str, Any]=False							,	__A  :						str=9_9							,	__A  :						Optional[Any]=1_6							,	__A  :						Optional[Any]=2							,	__A  :						Any=4							,	__A  :						List[Any]=4							,	__A  :						int="relu"							,	__A  :						Optional[int]=0.1							,	__A  :						Tuple=0.1							,	__A  :						Optional[int]=0.0							,	__A  :						Optional[Any]=0.0							,	__A  :						List[Any]=2_0							,	__A  :						Optional[Any]=2							,	__A  :						int=1							,	__A  :						Union[str, Any]=0							,	):
						snake_case__				:   Optional[Any]     =						parent
						snake_case__				:   List[str]     =						batch_size
						snake_case__				:   Union[str, Any]     =						seq_length
						snake_case__				:   Optional[Any]     =						is_training
						snake_case__				:   List[str]     =						use_labels
						snake_case__				:   Tuple     =						vocab_size
						snake_case__				:   Optional[Any]     =						hidden_size
						snake_case__				:   Union[str, Any]     =						num_hidden_layers
						snake_case__				:   List[Any]     =						num_attention_heads
						snake_case__				:   Tuple     =						intermediate_size
						snake_case__				:   str     =						hidden_act
						snake_case__				:   Optional[Any]     =						hidden_dropout_prob
						snake_case__				:   int     =						attention_probs_dropout_prob
						snake_case__				:   int     =						encoder_layerdrop
						snake_case__				:   Tuple     =						decoder_layerdrop
						snake_case__				:   List[str]     =						max_position_embeddings
						snake_case__				:   Tuple     =						eos_token_id
						snake_case__				:   Dict     =						pad_token_id
						snake_case__				:   str     =						bos_token_id



					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   List[Any]     =						ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size )
						snake_case__				:   Union[str, Any]     =						self.eos_token_id  # Eos Token
						snake_case__				:   str     =						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
						snake_case__				:   int     =						input_ids.clamp(self.pad_token_id + 1 )
						snake_case__				:   Optional[Any]     =						decoder_input_ids.clamp(self.pad_token_id + 1 )

						snake_case__				:   Union[str, Any]     =						self.get_config()
						snake_case__				:   Union[str, Any]     =						prepare_mam_aaa_inputs_dict(__A							,	__A							,	__A )
						return config, inputs_dict



					def  _lowercase  (      self  :						Dict ):
						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  _lowercase  (      self  :						List[str] ):
						snake_case__,		snake_case__				:   Any     =						self.prepare_config_and_inputs()
						return config, inputs_dict



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						int							,	__A  :						Dict ):
						snake_case__				:   Union[str, Any]     =						MaMaaaModel(config=__A ).get_decoder().to(__A ).eval()
						snake_case__				:   List[Any]     =						inputs_dict["input_ids"]
						snake_case__				:   Optional[Any]     =						inputs_dict["attention_mask"]
						snake_case__				:   Union[str, Any]     =						inputs_dict["head_mask"]

						# first forward pass
						snake_case__				:   Dict     =						model(__A							,	attention_mask=__A							,	head_mask=__A							,	use_cache=__A )

						snake_case__,		snake_case__				:   Dict     =						outputs.to_tuple()

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

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

						snake_case__				:   Tuple     =						model(__A							,	attention_mask=__A )["last_hidden_state"]
						snake_case__				:   Tuple     =						model(__A							,	attention_mask=__A							,	past_key_values=__A )[
						    "last_hidden_state"
						]

						# select random slice
						snake_case__				:   Optional[Any]     =						ids_tensor((1,)							,	output_from_past.shape[-1] ).item()
						snake_case__				:   Optional[Any]     =						output_from_no_past[:, -3:, random_slice_idx].detach()
						snake_case__				:   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(__A							,	__A							,	atol=1e-2 ) )






					def  _lowercase  (      self  :						str							,	__A  :						Dict							,	__A  :						Optional[Any] ):
						snake_case__				:   Union[str, Any]     =						MaMaaaModel(config=__A ).to(__A ).eval()
						snake_case__				:   Union[str, Any]     =						model(**__A )

						snake_case__				:   Tuple     =						outputs.encoder_last_hidden_state
						snake_case__				:   Union[str, Any]     =						outputs.last_hidden_state

						with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__				:   Dict     =						model.get_encoder()
							encoder.save_pretrained(__A )
							snake_case__				:   Any     =						MaMaaaEncoder.from_pretrained(__A ).to(__A )

						snake_case__				:   List[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:
							snake_case__				:   Dict     =						model.get_decoder()
							decoder.save_pretrained(__A )
							snake_case__				:   Optional[Any]     =						MaMaaaDecoder.from_pretrained(__A ).to(__A )

						snake_case__				:   List[str]     =						decoder(
						    input_ids=inputs_dict["decoder_input_ids"]							,	attention_mask=inputs_dict["decoder_attention_mask"]							,	encoder_hidden_states=__A							,	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 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	(
					    (
					        MaMaaaModel,
					        MaMaaaForConditionalGeneration,
					    )
					    if is_torch_available()
					    else ()
					)
					a_			=	(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
					a_			=	(
					    {
					        "conversational": MaMaaaForConditionalGeneration,
					        "feature-extraction": MaMaaaModel,
					        "summarization": MaMaaaForConditionalGeneration,
					        "text2text-generation": MaMaaaForConditionalGeneration,
					        "translation": MaMaaaForConditionalGeneration,
					    }
					    if is_torch_available()
					    else {}
					)
					a_			=	True
					a_			=	True
					a_			=	False
					a_			=	False



					def  _lowercase  (      self  :						int							,	__A  :						Tuple							,	__A  :						Any							,	__A  :						Optional[Any]							,	__A  :						Optional[Any]							,	__A  :						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  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Any     =						MaMaaaModelTester(self )
						snake_case__				:   Dict     =						ConfigTester(self							,	config_class=__A )



					def  _lowercase  (      self  :						Optional[Any] ):
						self.config_tester.run_common_tests()



					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__,		snake_case__				:   int     =						self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
							snake_case__				:   int     =						model_class(__A )

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



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   Dict     =						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(*__A )



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Tuple     =						self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_encoder_decoder_model_standalone(*__A )



					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__,		snake_case__				:   Dict     =						self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
							snake_case__				:   str     =						model_class(__A )
							model.to(__A )
							model.eval()

							snake_case__				:   str     =						copy.deepcopy(self._prepare_for_class(__A							,	__A ) )

							if not self.is_encoder_decoder:
								snake_case__				:   Optional[Any]     =						inputs["input_ids"]
								del inputs["input_ids"]
							else:
								snake_case__				:   Union[str, Any]     =						inputs["input_ids"]
								snake_case__				:   List[str]     =						inputs.get("decoder_input_ids"							,	__A )
								del inputs["input_ids"]
								inputs.pop("decoder_input_ids"							,	__A )

							snake_case__				:   Tuple     =						model.get_input_embeddings()
							if not self.is_encoder_decoder:
								snake_case__				:   List[Any]     =						wte(__A )
							else:
								snake_case__				:   Any     =						wte(__A )
								snake_case__				:   Optional[int]     =						wte(__A )

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






					def  _lowercase  (      self  :						Optional[Any] ):
						snake_case__,		snake_case__				:   Union[str, Any]     =						self.model_tester.prepare_config_and_inputs()
						snake_case__				:   Any     =						input_dict["input_ids"]
						snake_case__				:   int     =						input_ids.ne(1 ).to(__A )
						snake_case__				:   List[Any]     =						MaMaaaForConditionalGeneration(__A ).eval().to(__A )
						if torch_device == "cuda":
							model.half()
						model.generate(__A							,	attention_mask=__A )
						model.generate(num_beams=4							,	do_sample=__A							,	early_stopping=__A							,	num_return_sequences=3 )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	return torch.tensor(snake_case_  ,						dtype=torch.long  ,						device=snake_case_     )


__lowerCamelCase      :    Optional[Any]   		=   1e-4





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





					"""simple docstring"""






					@cached_property
					def  _lowercase  (      self  :						str ):
						return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" )



					def  _lowercase  (      self  :						Optional[int] ):
						snake_case__				:   List[str]     =						MaMaaaModel.from_pretrained("facebook/m2m100_418M" ).to(__A )
						snake_case__				:   Optional[Any]     =						_long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]] )
						snake_case__				:   str     =						_long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]] )
						snake_case__				:   int     =						prepare_mam_aaa_inputs_dict(model.config							,	__A							,	__A )
						with torch.no_grad():
							snake_case__				:   str     =						model(**__A )[0]
						snake_case__				:   Tuple     =						torch.Size((1, 1_1, 1_0_2_4) )
						self.assertEqual(output.shape							,	__A )
						# change to expected output here
						snake_case__				:   Optional[Any]     =						torch.tensor(
						    [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]]							,	device=__A )
						self.assertTrue(torch.allclose(output[:, :3, :3]							,	__A							,	atol=__A ) )



					def  _lowercase  (      self  :						Union[str, Any] ):
						snake_case__				:   Union[str, Any]     =						MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(__A )

						# change to intended input
						snake_case__				:   Union[str, Any]     =						_long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]] )
						snake_case__				:   List[str]     =						_long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]] )
						snake_case__				:   int     =						prepare_mam_aaa_inputs_dict(model.config							,	__A							,	__A )
						with torch.no_grad():
							snake_case__				:   Union[str, Any]     =						model(**__A )[0]
						snake_case__				:   Tuple     =						torch.Size((1, 1_1, model.config.vocab_size) )
						self.assertEqual(output.shape							,	__A )
						# change to expected output here
						snake_case__				:   List[str]     =						torch.tensor(
						    [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]]							,	device=__A )
						self.assertTrue(torch.allclose(output[:, :3, :3]							,	__A							,	atol=__A ) )






					def  _lowercase  (      self  :						Optional[Any] ):
						snake_case__				:   List[Any]     =						MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(__A )
						snake_case__				:   List[str]     =						MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"							,	src_lang="fr"							,	tgt_lang="en" )

						snake_case__				:   List[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
						snake_case__				:   str     =						tokenizer(__A							,	padding=__A							,	return_tensors="pt" )

						snake_case__				:   Tuple     =						model.generate(
						    input_ids=dct["input_ids"].to(__A )							,	attention_mask=dct["attention_mask"].to(__A )							,	num_beams=5							,	forced_bos_token_id=tokenizer.get_lang_id("en" )							,	)

						snake_case__				:   List[str]     =						[
						    "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.",
						]

						snake_case__				:   Dict     =						tokenizer.batch_decode(
						    hypotheses_batch.tolist()							,	clean_up_tokenization_spaces=__A							,	skip_special_tokens=__A )
						assert generated == expected_en



