






"""simple docstring"""





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  A__       :			List[Any]=						"""platform"""

  import jax
  import jax.numpy as jnp

  from transformers import MarianTokenizer
  from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right







def 	lowerCAmelCase_(			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			,						SCREAMING_SNAKE_CASE=None			,						)       ->						Union[str, Any]:
    """simple docstring"""

    if attention_mask is None:
        UpperCamelCase__								=     np.where(input_ids != config.pad_token_id			,						1			,						0       )
    if decoder_attention_mask is None:
        UpperCamelCase__								=     np.where(decoder_input_ids != config.pad_token_id			,						1			,						0       )
    if head_mask is None:
        UpperCamelCase__								=     np.ones((config.encoder_layers, config.encoder_attention_heads)       )
    if decoder_head_mask is None:
        UpperCamelCase__								=     np.ones((config.decoder_layers, config.decoder_attention_heads)       )
    if cross_attn_head_mask is None:
        UpperCamelCase__								=     np.ones((config.decoder_layers, config.decoder_attention_heads)       )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
    }
class  __lowerCamelCase     :

   def __init__(       self			,					snake_case_			,					snake_case_=13			,					snake_case_=7			,					snake_case_=True			,					snake_case_=False			,					snake_case_=99			,					snake_case_=16			,					snake_case_=2			,					snake_case_=4			,					snake_case_=4			,					snake_case_="gelu"			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=32			,					snake_case_=2			,					snake_case_=1			,					snake_case_=0			,					snake_case_=0.02			,					)		->							Tuple:
       UpperCamelCase__								=     parent
       UpperCamelCase__								=     batch_size
       UpperCamelCase__								=     seq_length
       UpperCamelCase__								=     is_training
       UpperCamelCase__								=     use_labels
       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     num_attention_heads
       UpperCamelCase__								=     intermediate_size
       UpperCamelCase__								=     hidden_act
       UpperCamelCase__								=     hidden_dropout_prob
       UpperCamelCase__								=     attention_probs_dropout_prob
       UpperCamelCase__								=     max_position_embeddings
       UpperCamelCase__								=     eos_token_id
       UpperCamelCase__								=     pad_token_id
       UpperCamelCase__								=     bos_token_id
       UpperCamelCase__								=     initializer_range

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Union[str, Any]:
       UpperCamelCase__								=     np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,					self.vocab_size   )			,					3			,					self.vocab_size   )
       UpperCamelCase__								=     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,					dtype=np.intaa   ))			,					-1   )

       UpperCamelCase__								=     shift_tokens_right(snake_case_			,					1			,					2   )

       UpperCamelCase__								=     MarianConfig(
           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			,					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			,					initializer_range=self.initializer_range			,					use_cache=snake_case_			,					)
       UpperCamelCase__								=     prepare_marian_inputs_dict(snake_case_			,					snake_case_			,					snake_case_   )
       return config, inputs_dict

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

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_			,					snake_case_   )		->							Dict:
       UpperCamelCase__								=     20
       UpperCamelCase__								=     model_class_name(snake_case_   )

       UpperCamelCase__								=     model.encode(inputs_dict['input_ids']   )

       UpperCamelCase__							,    UpperCamelCase__								=     (
           inputs_dict['decoder_input_ids'],
           inputs_dict['decoder_attention_mask'],
       )

       UpperCamelCase__								=     model.init_cache(decoder_input_ids.shape[0]			,					snake_case_			,					snake_case_   )
       UpperCamelCase__								=     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,					dtype='i4'   )

       UpperCamelCase__								=     jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]			,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,					)
       UpperCamelCase__								=     model.decode(
           decoder_input_ids[:, :-1]			,					snake_case_			,					decoder_attention_mask=snake_case_			,					past_key_values=snake_case_			,					decoder_position_ids=snake_case_			,					)

       UpperCamelCase__								=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,					dtype='i4'   )
       UpperCamelCase__								=     model.decode(
           decoder_input_ids[:, -1:]			,					snake_case_			,					decoder_attention_mask=snake_case_			,					past_key_values=outputs_cache.past_key_values			,					decoder_position_ids=snake_case_			,					)

       UpperCamelCase__								=     model.decode(snake_case_			,					snake_case_   )

       UpperCamelCase__								=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
       self.parent.assertTrue(diff < 1E-3			,					msg=F'Max diff is {diff}'   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_			,					snake_case_   )		->							List[str]:
       UpperCamelCase__								=     20
       UpperCamelCase__								=     model_class_name(snake_case_   )

       UpperCamelCase__								=     model.encode(inputs_dict['input_ids']   )

       UpperCamelCase__							,    UpperCamelCase__								=     (
           inputs_dict['decoder_input_ids'],
           inputs_dict['decoder_attention_mask'],
       )

       UpperCamelCase__								=     jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
           ]			,					axis=-1			,					)

       UpperCamelCase__								=     model.init_cache(decoder_input_ids.shape[0]			,					snake_case_			,					snake_case_   )
       UpperCamelCase__								=     jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]			,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,					)

       UpperCamelCase__								=     model.decode(
           decoder_input_ids[:, :-1]			,					snake_case_			,					decoder_attention_mask=snake_case_			,					past_key_values=snake_case_			,					decoder_position_ids=snake_case_			,					)
       UpperCamelCase__								=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,					dtype='i4'   )
       UpperCamelCase__								=     model.decode(
           decoder_input_ids[:, -1:]			,					snake_case_			,					past_key_values=outputs_cache.past_key_values			,					decoder_attention_mask=snake_case_			,					decoder_position_ids=snake_case_			,					)

       UpperCamelCase__								=     model.decode(snake_case_			,					snake_case_			,					decoder_attention_mask=snake_case_   )

       UpperCamelCase__								=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
       self.parent.assertTrue(diff < 1E-3			,					msg=F'Max diff is {diff}'   )







@require_flax
class  __lowerCamelCase     (	_a				,						unittest.TestCase				,						_a	):
   a   :					str			    =True
   a   :					Tuple			    =(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
   a   :					List[Any]			    =(FlaxMarianMTModel,) if is_flax_available() else ()

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Any:
       UpperCamelCase__								=     FlaxMarianModelTester(self   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       UpperCamelCase__							,    UpperCamelCase__								=     self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(snake_case_			,					snake_case_			,					snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       UpperCamelCase__							,    UpperCamelCase__								=     self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward_with_attn_mask(snake_case_			,					snake_case_			,					snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[str]:
       UpperCamelCase__							,    UpperCamelCase__								=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__   ):
               UpperCamelCase__								=     self._prepare_for_class(snake_case_			,					snake_case_   )
               UpperCamelCase__								=     model_class(snake_case_   )

               @jax.jit
               def encode_jitted(snake_case_			,					snake_case_=None			,					**snake_case_   ):
                   return model.encode(input_ids=snake_case_			,					attention_mask=snake_case_   )

               with self.subTest('JIT Enabled'   ):
                   UpperCamelCase__								=     encode_jitted(**snake_case_   ).to_tuple()

               with self.subTest('JIT Disabled'   ):
                   with jax.disable_jit():
                       UpperCamelCase__								=     encode_jitted(**snake_case_   ).to_tuple()

               self.assertEqual(len(snake_case_   )			,					len(snake_case_   )   )
               for jitted_output, output in zip(snake_case_			,					snake_case_   ):
                   self.assertEqual(jitted_output.shape			,					output.shape   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       UpperCamelCase__							,    UpperCamelCase__								=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__   ):
               UpperCamelCase__								=     model_class(snake_case_   )
               UpperCamelCase__								=     model.encode(inputs_dict['input_ids']			,					inputs_dict['attention_mask']   )

               UpperCamelCase__								=     {
                   'decoder_input_ids': inputs_dict['decoder_input_ids'],
                   'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                   'encoder_outputs': encoder_outputs,
               }

               @jax.jit
               def decode_jitted(snake_case_			,					snake_case_			,					snake_case_   ):
                   return model.decode(
                       decoder_input_ids=snake_case_			,					decoder_attention_mask=snake_case_			,					encoder_outputs=snake_case_			,					)

               with self.subTest('JIT Enabled'   ):
                   UpperCamelCase__								=     decode_jitted(**snake_case_   ).to_tuple()

               with self.subTest('JIT Disabled'   ):
                   with jax.disable_jit():
                       UpperCamelCase__								=     decode_jitted(**snake_case_   ).to_tuple()

               self.assertEqual(len(snake_case_   )			,					len(snake_case_   )   )
               for jitted_output, output in zip(snake_case_			,					snake_case_   ):
                   self.assertEqual(jitted_output.shape			,					output.shape   )







   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       for model_class_name in self.all_model_classes:
           UpperCamelCase__								=     model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'   )
           # FlaxMarianForSequenceClassification expects eos token in input_ids
           UpperCamelCase__								=     np.ones((1, 1)   ) * model.config.eos_token_id
           UpperCamelCase__								=     model(snake_case_   )
           self.assertIsNotNone(snake_case_   )







@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase     (	unittest.TestCase	):
   a   :					List[str]			    =None
   a   :					Any			    =None

   @classmethod
   def     SCREAMING_SNAKE_CASE__			(       cls   )		->							None:
       UpperCamelCase__								=     F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
       return cls

   @cached_property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							int:
       return MarianTokenizer.from_pretrained(self.model_name   )

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							int:
       return self.tokenizer.eos_token_id

   @cached_property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       UpperCamelCase__								=     FlaxMarianMTModel.from_pretrained(self.model_name   )
       self.assertEqual(model.config.decoder_start_token_id			,					model.config.pad_token_id   )
       return model

   def     SCREAMING_SNAKE_CASE__			(       self			,					**snake_case_   )		->							Optional[Any]:
       UpperCamelCase__								=     self.translate_src_text(**snake_case_   )
       self.assertListEqual(self.expected_text			,					snake_case_   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					**snake_case_   )		->							str:
       UpperCamelCase__								=     self.tokenizer(self.src_text			,					padding=snake_case_			,					return_tensors='np'			,					**snake_case_   )
       UpperCamelCase__								=     self.model.generate(
           model_inputs.input_ids			,					attention_mask=model_inputs.attention_mask			,					num_beams=2			,					max_length=128			,					).sequences
       UpperCamelCase__								=     self.tokenizer.batch_decode(snake_case_			,					skip_special_tokens=snake_case_   )
       return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase     (	_a	):
   a   :					List[Any]			    ="""en"""
   a   :					Optional[Any]			    ="""fr"""
   a   :					List[Any]			    =[
       """I am a small frog.""",
       """Now I can forget the 100 words of german that I know.""",
   ]
   a   :					Any			    =[
       """Je suis une petite grenouille.""",
       """Maintenant, je peux oublier les 100 mots d'allemand que je connais.""",
   ]

   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[Any]:
       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase     (	_a	):
   a   :					Tuple			    ="""fr"""
   a   :					Optional[Any]			    ="""en"""
   a   :					Union[str, Any]			    =[
       """Donnez moi le micro.""",
       """Tom et Mary étaient assis à une table.""",  # Accents
   ]
   a   :					Dict			    =[
       """Give me the microphone.""",
       """Tom and Mary were sitting at a table.""",
   ]

   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							str:
       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase     (	_a	):
   a   :					List[str]			    ="""mt"""
   a   :					Any			    ="""en"""
   a   :					Union[str, Any]			    =["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
   a   :					Optional[Any]			    =["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]

   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[Any]:
       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase     (	_a	):
   a   :					str			    ="""en"""
   a   :					str			    ="""de"""
   a   :					List[Any]			    =[
       """I am a small frog.""",
       """Now I can forget the 100 words of german that I know.""",
       """Tom asked his teacher for advice.""",
       """That's how I would do it.""",
       """Tom really admired Mary's courage.""",
       """Turn around and close your eyes.""",
   ]
   a   :					List[str]			    =[
       """Ich bin ein kleiner Frosch.""",
       """Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.""",
       """Tom bat seinen Lehrer um Rat.""",
       """So würde ich das machen.""",
       """Tom bewunderte Marias Mut wirklich.""",
       """Drehen Sie sich um und schließen Sie die Augen.""",
   ]

   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[int]:
       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase     (	_a	):
   a   :					List[str]			    ="""en"""
   a   :					Optional[Any]			    ="""zh"""
   a   :					Dict			    =["""My name is Wolfgang and I live in Berlin"""]
   a   :					Optional[Any]			    =["""我叫沃尔夫冈 我住在柏林"""]

   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Any:
       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase     (	_a	):
   a   :					int			    ="""ru"""
   a   :					int			    ="""fr"""
   a   :					List[Any]			    =["""Он показал мне рукопись своей новой пьесы."""]
   a   :					str			    =["""Il m'a montré le manuscrit de sa nouvelle pièce."""]

   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Any:
       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase     (	_a	):
   a   :					Any			    ="""en"""
   a   :					Tuple			    ="""ROMANCE"""
   a   :					Optional[Any]			    =[
       """>>fr<< Don't spend so much time watching TV.""",
       """>>pt<< Your message has been sent.""",
       """>>es<< He's two years older than me.""",
   ]
   a   :					List[str]			    =[
       """Ne passez pas autant de temps à regarder la télé.""",
       """A sua mensagem foi enviada.""",
       """Es dos años más viejo que yo.""",
   ]

   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[str]:
       self._assert_generated_batch_equal_expected()












"""simple docstring"""





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE=0       )       ->						str:
    """simple docstring"""

    if name is None:
        UpperCamelCase__								=     None
    else:
        UpperCamelCase__								=     '.' * max(0			,						spaces - 2       ) + '# {:' + str(50 - spaces       ) + 's}'
        UpperCamelCase__								=     fmt.format(SCREAMING_SNAKE_CASE       )

    # Print and recurse (if needed).
    if isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       ):
        if msg is not None:
            print(SCREAMING_SNAKE_CASE       )
        for k in val.keys():
            recursive_print(SCREAMING_SNAKE_CASE			,						val[k]			,						spaces + 2       )
    elif isinstance(SCREAMING_SNAKE_CASE			,						torch.Tensor       ):
        print(SCREAMING_SNAKE_CASE			,						':'			,						val.size()       )
    else:
        print(SCREAMING_SNAKE_CASE			,						':'			,						SCREAMING_SNAKE_CASE       )







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Tuple:
    """simple docstring"""

    UpperCamelCase__								=     param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    UpperCamelCase__								=     (num_heads, num_splits, hidden_size) + input_shape[1:]
    UpperCamelCase__								=     param.view(*SCREAMING_SNAKE_CASE       )
    UpperCamelCase__								=     param.transpose(0			,						1       ).contiguous()
    UpperCamelCase__								=     param.view(*SCREAMING_SNAKE_CASE       )
    return param







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Any:
    """simple docstring"""

    UpperCamelCase__								=     config.n_positions
    UpperCamelCase__								=     config.n_layer
    UpperCamelCase__								=     config.vocab_size
    UpperCamelCase__								=     config.n_head
    UpperCamelCase__								=     config.n_embd // config.n_head

    UpperCamelCase__								=     sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
    UpperCamelCase__								=     {
        'transformer.wte.weight': word_embeddings,
        'transformer.wpe.weight': sd_megatron['model.language_model.embedding.position_embeddings.weight'],
        'transformer.ln_f.weight': sd_megatron['model.language_model.encoder.final_layernorm.weight'],
        'transformer.ln_f.bias': sd_megatron['model.language_model.encoder.final_layernorm.bias'],
    }

    UpperCamelCase__								=     'model.language_model.encoder.layers.'
    for i in range(SCREAMING_SNAKE_CASE       ):
        UpperCamelCase__								=     torch.tril(torch.ones((n_positions, n_positions)			,						dtype=torch.bool       )       )
        UpperCamelCase__								=     causal_mask.view(1			,						1			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     causal_mask
        UpperCamelCase__								=     torch.tensor(-1E4			,						dtype=torch.bfloataa       )

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.input_layernorm.weight']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.input_layernorm.bias']

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.query_key_value.weight']
        UpperCamelCase__								=     fix_query_key_value_ordering(SCREAMING_SNAKE_CASE			,						3			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     vala.transpose(0			,						1       ).contiguous()

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.query_key_value.bias']
        UpperCamelCase__								=     fix_query_key_value_ordering(SCREAMING_SNAKE_CASE			,						3			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     vala

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.dense.weight'].transpose(
            0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.dense.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.post_attention_layernorm.weight']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.post_attention_layernorm.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
            0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.bias']

    # For LM head, transformers' wants the matrix to weight embeddings.
    UpperCamelCase__								=     word_embeddings

    return sd_hf







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						int:
    """simple docstring"""

    UpperCamelCase__								=     6_40_00
    UpperCamelCase__								=     config_megatron['encoder_seq_length']
    UpperCamelCase__								=     config_megatron['hidden_size']
    UpperCamelCase__								=     config_megatron['num_layers']
    UpperCamelCase__								=     config_megatron['num_attention_heads']
    UpperCamelCase__								=     config_megatron['ffn_hidden_size']
    UpperCamelCase__								=     'gelu'
    UpperCamelCase__								=     0.1
    UpperCamelCase__								=     0.1
    UpperCamelCase__								=     0.1
    UpperCamelCase__								=     config_megatron['layernorm_epsilon']  # 1e-5
    UpperCamelCase__								=     config_megatron['init_method_std']  # 0.02
    UpperCamelCase__								=     config_megatron['apply_query_key_layer_scaling']  # True
    UpperCamelCase__								=     True
    UpperCamelCase__								=     True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 40_96:
        UpperCamelCase__								=     1  # <|endoftext|>
        UpperCamelCase__								=     1  # <|endoftext|>
        UpperCamelCase__								=     0  # <unk>
    else:
        UpperCamelCase__								=     2  # <s>
        UpperCamelCase__								=     3  # <|endoftext|>
        UpperCamelCase__								=     0  # <pad>

    return config_hf







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						str:
    """simple docstring"""

    print(SCREAMING_SNAKE_CASE       )

    UpperCamelCase__								=     args.checkpoint_path
    UpperCamelCase__								=     args.save_path
    if isfile(SCREAMING_SNAKE_CASE       ):
        raise FileNotFoundError(F'ERROR! could not find file {checkpoint_path}'       )

    # Load the model.
    UpperCamelCase__								=     torch.load(SCREAMING_SNAKE_CASE			,						map_location='cpu'       )

    # Load the config.
    UpperCamelCase__								=     checkpoint['hyper_parameters']['cfg']
    UpperCamelCase__								=     GPTaConfig()
    UpperCamelCase__								=     copy_config(config_hf=SCREAMING_SNAKE_CASE			,						config_megatron=SCREAMING_SNAKE_CASE       )
    UpperCamelCase__								=     ['GPT2LMHeadModel']

    UpperCamelCase__								=     checkpoint['state_dict']

    # Convert.
    print('Converting'       )
    UpperCamelCase__								=     convert_megatron_checkpoint(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )

    UpperCamelCase__								=     'GPTSw3Tokenizer'

    # Store the config to file.
    print('Saving config'       )
    config_hf.save_pretrained(SCREAMING_SNAKE_CASE       )

    # Store the state_dict to file.
    UpperCamelCase__								=     os.path.join(SCREAMING_SNAKE_CASE			,						'pytorch_model.bin'       )
    print(F'Saving checkpoint to "{output_checkpoint_file}"'       )
    torch.save(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  A__       :			Optional[Any]=						argparse.ArgumentParser()
  parser.add_argument(
      """--checkpoint_path""",
      type=str,
      required=True,
      help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
  )
  parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
  parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
  A__       :			Any=						parser.parse_args()
  main(_args)




