






'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers.models.xglm.modeling_tf_xglm import (
			    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFXGLMForCausalLM,
			    TFXGLMModel,
			)

@require_tf
class _lowercase	:
							a				      =       XGLMConfig
							a				      =       {}
							a				      =       """gelu"""




							def __init__(							self:				str			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				List[Any]=14			,							UpperCamelCase__:				List[str]=7			,							UpperCamelCase__:				int=True			,							UpperCamelCase__:				int=True			,							UpperCamelCase__:				Any=True			,							UpperCamelCase__:				int=99			,							UpperCamelCase__:				str=32			,							UpperCamelCase__:				Optional[int]=2			,							UpperCamelCase__:				str=4			,							UpperCamelCase__:				Union[str, Any]=37			,							UpperCamelCase__:				Optional[int]="gelu"			,							UpperCamelCase__:				List[Any]=0.1			,							UpperCamelCase__:				int=0.1			,							UpperCamelCase__:				Union[str, Any]=512			,							UpperCamelCase__:				Optional[Any]=0.02			,							):
														lowerCamelCase__					:	Any         =       parent
														lowerCamelCase__					:	Union[str, Any]         =       batch_size
														lowerCamelCase__					:	Optional[int]         =       seq_length
														lowerCamelCase__					:	int         =       is_training
														lowerCamelCase__					:	str         =       use_input_mask
														lowerCamelCase__					:	Union[str, Any]         =       use_labels
														lowerCamelCase__					:	List[Any]         =       vocab_size
														lowerCamelCase__					:	List[str]         =       d_model
														lowerCamelCase__					:	Optional[Any]         =       num_hidden_layers
														lowerCamelCase__					:	Union[str, Any]         =       num_attention_heads
														lowerCamelCase__					:	str         =       ffn_dim
														lowerCamelCase__					:	Dict         =       activation_function
														lowerCamelCase__					:	Any         =       activation_dropout
														lowerCamelCase__					:	Union[str, Any]         =       attention_dropout
														lowerCamelCase__					:	Optional[int]         =       max_position_embeddings
														lowerCamelCase__					:	Any         =       initializer_range
														lowerCamelCase__					:	List[Any]         =       None
														lowerCamelCase__					:	Union[str, Any]         =       0
														lowerCamelCase__					:	Any         =       2
														lowerCamelCase__					:	Union[str, Any]         =       1




							def 		lowerCamelCase_				(							self:				List[str]     ):
														return XGLMConfig.from_pretrained("""facebook/xglm-564M"""     )




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	List[str]         =       tf.clip_by_value(
														    ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size     )			,							clip_value_min=0			,							clip_value_max=3     )

														lowerCamelCase__					:	str         =       None
														if self.use_input_mask:
																					lowerCamelCase__					:	Optional[Any]         =       random_attention_mask([self.batch_size, self.seq_length]     )

														lowerCamelCase__					:	List[str]         =       self.get_config()

														lowerCamelCase__					:	Optional[int]         =       floats_tensor([self.num_hidden_layers, self.num_attention_heads]			,							2     )

														return (
														    config,
														    input_ids,
														    input_mask,
														    head_mask,
														)




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														return XGLMConfig(
														    vocab_size=self.vocab_size			,							d_model=self.hidden_size			,							num_layers=self.num_hidden_layers			,							attention_heads=self.num_attention_heads			,							ffn_dim=self.ffn_dim			,							activation_function=self.activation_function			,							activation_dropout=self.activation_dropout			,							attention_dropout=self.attention_dropout			,							max_position_embeddings=self.max_position_embeddings			,							initializer_range=self.initializer_range			,							use_cache=UpperCamelCase__			,							bos_token_id=self.bos_token_id			,							eos_token_id=self.eos_token_id			,							pad_token_id=self.pad_token_id			,							return_dict=UpperCamelCase__			,							)




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	Any         =       self.prepare_config_and_inputs()

														(
														    (
														    lowerCamelCase__
														)  ,		(
														    lowerCamelCase__
														)  ,		(
														    lowerCamelCase__
														)  ,		(
														    lowerCamelCase__
														)  ,		
														)					:	int         =       config_and_inputs

														lowerCamelCase__					:	Optional[int]         =       {
														    """input_ids""": input_ids,
														    """head_mask""": head_mask,
														}

														return config, inputs_dict



@require_tf
class _lowercase	(				lowercase_				,      lowercase_				,      unittest.TestCase		):
							a				      =       (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
							a				      =       (TFXGLMForCausalLM,) if is_tf_available() else ()
							a				      =       (
							    {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
							)
							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	List[str]         =       TFXGLMModelTester(self     )
														lowerCamelCase__					:	Dict         =       ConfigTester(self			,							config_class=UpperCamelCase__			,							n_embd=37     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														self.config_tester.run_common_tests()




							@slow
							def 		lowerCamelCase_				(							self:				Optional[Any]     ):







														for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__					:	Any         =       TFXGLMModel.from_pretrained(UpperCamelCase__     )
																					self.assertIsNotNone(UpperCamelCase__     )




							@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""     )
							def 		lowerCamelCase_				(							self:				Any     ):
														super().test_resize_token_embeddings()



@require_tf
class _lowercase	(				unittest.TestCase		):




							@slow
							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				Optional[Any]=True     ):
														lowerCamelCase__					:	Union[str, Any]         =       TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""     )
														lowerCamelCase__					:	Dict         =       tf.convert_to_tensor([[2, 268, 9_865]]			,							dtype=tf.intaa     )  # The dog
														# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
														# fmt: off
														lowerCamelCase__					:	int         =       [2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
														# fmt: on
														lowerCamelCase__					:	int         =       model.generate(UpperCamelCase__			,							do_sample=UpperCamelCase__			,							num_beams=1     )
														if verify_outputs:
																					self.assertListEqual(output_ids[0].numpy().tolist()			,							UpperCamelCase__     )




							@slow
							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	List[Any]         =       XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""     )
														lowerCamelCase__					:	Tuple         =       TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""     )

														tf.random.set_seed(0     )
														lowerCamelCase__					:	str         =       tokenizer("""Today is a nice day and"""			,							return_tensors="""tf"""     )
														lowerCamelCase__					:	Dict         =       tokenized.input_ids
														# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
														with tf.device(""":/CPU:0"""     ):
																					lowerCamelCase__					:	Optional[int]         =       model.generate(UpperCamelCase__			,							do_sample=UpperCamelCase__			,							seed=[7, 0]     )
														lowerCamelCase__					:	Tuple         =       tokenizer.decode(output_ids[0]			,							skip_special_tokens=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[Any]         =       (
														    """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
														)
														self.assertEqual(UpperCamelCase__			,							UpperCamelCase__     )




							@slow
							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	Dict         =       TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""     )
														lowerCamelCase__					:	Dict         =       XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""     )

														lowerCamelCase__					:	Union[str, Any]         =       """left"""

														# use different length sentences to test batching
														lowerCamelCase__					:	int         =       [
														    """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
														    """left-padding, such as in batched generation. The output for the sequence below should be the same """
														    """regardless of whether left padding is applied or not. When""",
														    """Hello, my dog is a little""",
														]

														lowerCamelCase__					:	int         =       tokenizer(UpperCamelCase__			,							return_tensors="""tf"""			,							padding=UpperCamelCase__     )
														lowerCamelCase__					:	Optional[int]         =       inputs["""input_ids"""]

														lowerCamelCase__					:	List[Any]         =       model.generate(input_ids=UpperCamelCase__			,							attention_mask=inputs["""attention_mask"""]			,							max_new_tokens=12     )

														lowerCamelCase__					:	Dict         =       tokenizer(sentences[0]			,							return_tensors="""tf"""     ).input_ids
														lowerCamelCase__					:	Dict         =       model.generate(input_ids=UpperCamelCase__			,							max_new_tokens=12     )

														lowerCamelCase__					:	Dict         =       tokenizer(sentences[1]			,							return_tensors="""tf"""     ).input_ids
														lowerCamelCase__					:	Union[str, Any]         =       model.generate(input_ids=UpperCamelCase__			,							max_new_tokens=12     )

														lowerCamelCase__					:	Optional[Any]         =       tokenizer.batch_decode(UpperCamelCase__			,							skip_special_tokens=UpperCamelCase__     )
														lowerCamelCase__					:	Any         =       tokenizer.decode(output_non_padded[0]			,							skip_special_tokens=UpperCamelCase__     )
														lowerCamelCase__					:	Any         =       tokenizer.decode(output_padded[0]			,							skip_special_tokens=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[int]         =       [
														    """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
														    """left-padding, such as in batched generation. The output for the sequence below should be the same """
														    """regardless of whether left padding is applied or not. When left padding is applied, the sequence will be """
														    """a single""",
														    """Hello, my dog is a little bit of a shy one, but he is very friendly""",
														]
														self.assertListEqual(UpperCamelCase__			,							UpperCamelCase__     )
														self.assertListEqual(UpperCamelCase__			,							[non_padded_sentence, padded_sentence]     )





'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= [
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(A  , A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







       lowercase__			,lowercase__      			= emb.weight.shape
       lowercase__      			= nn.Linear(A  , A  , bias=A       )
       lowercase__      			= emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE      (A  , A="facebook/mbart-large-en-ro"  , A=False  , A=False       )       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__      			= torch.load(A  , map_location='''cpu'''       )['''model''']
       remove_ignore_keys_(A       )
       lowercase__      			= state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase__      			= MBartConfig.from_pretrained(A  , vocab_size=A       )
       if mbart_aa and finetuned:
              lowercase__      			= '''relu'''

       lowercase__      			= state_dict['''decoder.embed_tokens.weight''']
       lowercase__      			= MBartForConditionalGeneration(A       )
       model.model.load_state_dict(A       )

       if finetuned:
              lowercase__      			= make_linear_from_emb(model.model.shared       )

       return model


if __name__ == "__main__":
 lowerCamelCase							:   Any  						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a 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.')
 parser.add_argument(
     '--hf_config',
     default='facebook/mbart-large-cc25',
     type=str,
     help='Which huggingface architecture to use: mbart-large',
 )
 parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
 parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
 lowerCamelCase							:   Any  						=	parser.parse_args()
 lowerCamelCase							:   List[str]  						=	convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)




