

import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
							import os

							import jax.numpy as jnp
							from jax import jit

							from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
							from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

							lowercase__	:Optional[Any]										=					"0.12"  # assumed parallelism: 8


if is_torch_available():
							import torch



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__=None	):
					'''simple docstring'''
					if rng is None:
										lowercase					=		random.Random()

					lowercase					=		1
					for dim in shape:
										total_dims *= dim

					lowercase					=		[]
					for _ in range(lowerCAmelCase__	):
										values.append(rng.randint(0				,    vocab_size - 1	)	)

					lowercase					=		np.array(lowerCAmelCase__				,    dtype=jnp.intaa	).reshape(lowerCAmelCase__	)

					return output



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=None	):
					'''simple docstring'''
					lowercase					=		ids_tensor(lowerCAmelCase__				,    vocab_size=2				,    rng=lowerCAmelCase__	)
					# make sure that at least one token is attended to for each batch
					lowercase					=		1
					return attn_mask







@require_flax
class        lowercase	:
							lowercase_ : Any       =None
							lowercase_ : List[str]       =()




							def 				A__   (       self):
												lowercase			,		lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()

												# cut to half length & take max batch_size 3
												lowercase					=		2
												lowercase					=		inputs['''input_ids'''].shape[-1] // 2
												lowercase					=		inputs['''input_ids'''][:max_batch_size, :sequence_length]

												lowercase					=		jnp.ones_like(A__)
												lowercase					=		attention_mask[:max_batch_size, :sequence_length]

												# generate max 5 tokens
												lowercase					=		input_ids.shape[-1] + 5
												if config.eos_token_id is not None and config.pad_token_id is None:
																	# hack to allow generate for models such as GPT2 as is done in `generate()`
																	lowercase					=		config.eos_token_id
												return config, input_ids, attention_mask, max_length




							@is_pt_flax_cross_test
							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		0

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
																	lowercase					=		getattr(A__     ,A__)
																	lowercase					=		pt_model_class(A__).eval()
																	lowercase					=		load_flax_weights_in_pytorch_model(A__     ,flax_model.params)

																	lowercase					=		flax_model.generate(A__).sequences
																	lowercase					=		pt_model.generate(torch.tensor(A__     ,dtype=torch.long))

																	if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																						lowercase					=		flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																	self.assertListEqual(pt_generation_outputs.numpy().tolist()     ,flax_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		True
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		2

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		2
												lowercase					=		2

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[0]     ,input_ids.shape[0] * config.num_return_sequences)




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		True
												lowercase					=		max_length
												lowercase					=		0.8
												lowercase					=		1_0
												lowercase					=		0.3
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		max_length
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		max_length
												lowercase					=		2
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

												# pad attention mask on the left
												lowercase					=		attention_mask.at[(0, 0)].set(0)

												lowercase					=		False
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__     ,attention_mask=A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

												# pad attention mask on the left
												lowercase					=		attention_mask.at[(0, 0)].set(0)

												lowercase					=		True
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__     ,attention_mask=A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())





							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

												# pad attention mask on the left
												lowercase					=		attention_mask.at[(0, 0)].set(0)

												lowercase					=		2
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__     ,attention_mask=A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())







@require_flax
class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self):
												lowercase					=		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''')
												lowercase					=		FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''')

												lowercase					=		'''Hello world'''
												lowercase					=		tokenizer(A__     ,return_tensors='''np''').input_ids

												# typos are quickly detected (the correct argument is `do_sample`)
												with self.assertRaisesRegex(A__     ,'''do_samples'''):
																	model.generate(A__     ,do_samples=A__)

												# arbitrary arguments that will not be used anywhere are also not accepted
												with self.assertRaisesRegex(A__     ,'''foo'''):
																	lowercase					=		{'''foo''': '''bar'''}
																	model.generate(A__     ,**A__)






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		[
					    '''encoder.version''',
					    '''decoder.version''',
					    '''model.encoder.version''',
					    '''model.decoder.version''',
					    '''decoder.output_projection.weight''',
					    '''_float_tensor''',
					    '''encoder.embed_positions._float_tensor''',
					    '''decoder.embed_positions._float_tensor''',
					]
					for k in ignore_keys:
										state_dict.pop(lowerCAmelCase__				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		emb.weight.shape
					lowercase					=		nn.Linear(lowerCAmelCase__				,    lowerCAmelCase__				,    bias=lowerCAmelCase__	)
					lowercase					=		emb.weight.data
					return lin_layer



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		torch.load(lowerCAmelCase__				,    map_location='''cpu'''	)
					lowercase					=		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
					lowercase					=		mam_aaa['''model''']
					remove_ignore_keys_(lowerCAmelCase__	)
					lowercase					=		state_dict['''encoder.embed_tokens.weight'''].shape[0]

					lowercase					=		MaMaaaConfig(
					    vocab_size=lowerCAmelCase__				,    max_position_embeddings=1024				,    encoder_layers=args.encoder_layers				,    decoder_layers=args.decoder_layers				,    encoder_attention_heads=args.encoder_attention_heads				,    decoder_attention_heads=args.decoder_attention_heads				,    encoder_ffn_dim=args.encoder_ffn_embed_dim				,    decoder_ffn_dim=args.decoder_ffn_embed_dim				,    d_model=args.encoder_embed_dim				,    encoder_layerdrop=args.encoder_layerdrop				,    decoder_layerdrop=args.decoder_layerdrop				,    dropout=args.dropout				,    attention_dropout=args.attention_dropout				,    activation_dropout=args.activation_dropout				,    activation_function='''relu'''				,    )

					lowercase					=		state_dict['''decoder.embed_tokens.weight''']
					lowercase					=		MaMaaaForConditionalGeneration(lowerCAmelCase__	)
					model.model.load_state_dict(lowerCAmelCase__				,    strict=lowerCAmelCase__	)
					lowercase					=		make_linear_from_emb(model.model.shared	)

					return model


if __name__ == "__main__":
							lowercase__	:Optional[int]										=					argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
							parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
							lowercase__	:Tuple										=					parser.parse_args()
							lowercase__	:int										=					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)




