



import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


A_		:  Optional[int]										=  namedtuple('''ExampleNamedTuple''', '''a b c''')



class 		_lowercase		(							unittest.TestCase						):

   def 		A				(   self      :							List[str]				)							->					Any:


    """simple docstring"""






    a							=		torch.randn(5				, 2				)
    a							=		torch.device("cuda"				) if torch.cuda.is_available() else torch.device("cpu"				)

    a							=		send_to_device(__lowerCAmelCase				, __lowerCAmelCase				)
    self.assertTrue(torch.equal(resulta.cpu()				, __lowerCAmelCase				)				)

    a							=		send_to_device((tensor, [tensor, tensor], 1)				, __lowerCAmelCase				)
    self.assertIsInstance(__lowerCAmelCase				, __lowerCAmelCase				)
    self.assertTrue(torch.equal(resulta[0].cpu()				, __lowerCAmelCase				)				)
    self.assertIsInstance(resulta[1]				, __lowerCAmelCase				)
    self.assertTrue(torch.equal(resulta[1][0].cpu()				, __lowerCAmelCase				)				)
    self.assertTrue(torch.equal(resulta[1][1].cpu()				, __lowerCAmelCase				)				)
    self.assertEqual(resulta[2]				, 1				)

    a							=		send_to_device({"a": tensor, "b": [tensor, tensor], "c": 1}				, __lowerCAmelCase				)
    self.assertIsInstance(__lowerCAmelCase				, __lowerCAmelCase				)
    self.assertTrue(torch.equal(resulta["a"].cpu()				, __lowerCAmelCase				)				)
    self.assertIsInstance(resulta["b"]				, __lowerCAmelCase				)
    self.assertTrue(torch.equal(resulta["b"][0].cpu()				, __lowerCAmelCase				)				)
    self.assertTrue(torch.equal(resulta["b"][1].cpu()				, __lowerCAmelCase				)				)
    self.assertEqual(resulta["c"]				, 1				)

    a							=		send_to_device(ExampleNamedTuple(a=__lowerCAmelCase				, b=[tensor, tensor]				, c=1				)				, __lowerCAmelCase				)
    self.assertIsInstance(__lowerCAmelCase				, __lowerCAmelCase				)
    self.assertTrue(torch.equal(resulta.a.cpu()				, __lowerCAmelCase				)				)
    self.assertIsInstance(resulta.b				, __lowerCAmelCase				)
    self.assertTrue(torch.equal(resulta.b[0].cpu()				, __lowerCAmelCase				)				)
    self.assertTrue(torch.equal(resulta.b[1].cpu()				, __lowerCAmelCase				)				)
    self.assertEqual(resulta.c				, 1				)

    a							=		send_to_device(UserDict({"a": tensor, "b": [tensor, tensor], "c": 1}				)				, __lowerCAmelCase				)
    self.assertIsInstance(__lowerCAmelCase				, __lowerCAmelCase				)
    self.assertTrue(torch.equal(resulta["a"].cpu()				, __lowerCAmelCase				)				)
    self.assertIsInstance(resulta["b"]				, __lowerCAmelCase				)
    self.assertTrue(torch.equal(resulta["b"][0].cpu()				, __lowerCAmelCase				)				)
    self.assertTrue(torch.equal(resulta["b"][1].cpu()				, __lowerCAmelCase				)				)
    self.assertEqual(resulta["c"]				, 1				)

   def 		A				(   self      :							List[Any]				)							->					Optional[Any]:


    """simple docstring"""






    with self.assertRaises(__lowerCAmelCase				) as cm:
     a							=		recursively_apply(torch.tensor				, (torch.tensor(1				), 1)				, error_on_other_type=__lowerCAmelCase				)
    self.assertEqual(
        str(cm.exception				)				, "Unsupported types (<class 'int'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed."				, )

   def 		A				(   self      :							List[str]				)							->					Optional[int]:


    """simple docstring"""






    a							=		torch.tensor([1, 2, 3, 4, 5]				)
    self.assertEqual(listify(__lowerCAmelCase				)				, [1, 2, 3, 4, 5]				)

    a							=		torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]				)
    self.assertEqual(listify(__lowerCAmelCase				)				, [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]				)

    a							=		torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]				)
    self.assertEqual(
        listify(__lowerCAmelCase				)				, [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]				)

   def 		A				(   self      :							Tuple				)							->					int:


    """simple docstring"""






    with patch_environment(aa=1				, BB=2				):
     self.assertEqual(os.environ.get("AA"				)				, "1"				)
     self.assertEqual(os.environ.get("BB"				)				, "2"				)

    self.assertNotIn("AA"				, os.environ				)
    self.assertNotIn("BB"				, os.environ				)

   def 		A				(   self      :							Tuple				)							->					List[Any]:


    """simple docstring"""






    a							=		RegressionModel()
    a							=		model.forward
    a							=		convert_outputs_to_fpaa(model.forward				)
    a							=		extract_model_from_parallel(__lowerCAmelCase				, keep_fpaa_wrapper=__lowerCAmelCase				)
    a							=		pickle.dumps(__lowerCAmelCase				)

   @require_cuda
   def 		A				(   self      :							Optional[Any]				)							->					List[Any]:


    """simple docstring"""






    a							=		RegressionModel()
    a							=		model.forward
    a							=		torch.cuda.amp.autocast(dtype=torch.floataa				)(model.forward				)
    a							=		convert_outputs_to_fpaa(model.forward				)
    a							=		extract_model_from_parallel(__lowerCAmelCase				, keep_fpaa_wrapper=__lowerCAmelCase				)
    a							=		pickle.dumps(__lowerCAmelCase				)

   @require_cuda
   @require_torch_min_version(version="2.0"				)
   def 		A				(   self      :							Optional[Any]				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		RegressionModel()
    a							=		model.forward
    a							=		torch.cuda.amp.autocast(dtype=torch.floataa				)(model.forward				)
    a							=		convert_outputs_to_fpaa(model.forward				)
    a							=		torch.compile(model.forward				, backend="inductor"				)
    a							=		torch.randn(4				, 10				).cuda()
    a							=		model(__lowerCAmelCase				)

   def 		A				(   self      :							int				)							->					int:


    """simple docstring"""






    a							=		RegressionModel()
    # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
    a							=		torch.nn.parallel.DataParallel(__lowerCAmelCase				)
    a							=		extract_model_from_parallel(__lowerCAmelCase				)

    self.assertEqual(__lowerCAmelCase				, __lowerCAmelCase				)

   @require_torch_min_version(version="2.0"				)
   def 		A				(   self      :							Dict				)							->					List[str]:


    """simple docstring"""






    a							=		RegressionModel()
    a							=		torch.compile(__lowerCAmelCase				)

    # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
    a							=		torch.nn.parallel.DataParallel(__lowerCAmelCase				)
    a							=		torch.compile(__lowerCAmelCase				)
    a							=		extract_model_from_parallel(__lowerCAmelCase				)

    self.assertEqual(compiled_model._orig_mod				, compiled_model_unwrapped._orig_mod				)




   def 		A				(   self      :							Union[str, Any]				)							->					Tuple:


    """simple docstring"""






    self.assertEqual(find_device([1, "a", torch.tensor([1, 2, 3]				)]				)				, torch.device("cpu"				)				)
    self.assertEqual(find_device({"a": 1, "b": torch.tensor([1, 2, 3]				)}				)				, torch.device("cpu"				)				)
    self.assertIsNone(find_device([1, "a"]				)				)



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_snake_case		  =   logging.get_logger(__name__)

_snake_case		  =   {
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:Optional[int]						 =      'mbart'
      a		:List[Any]						 =      ['past_key_values']
      a		:Optional[Any]						 =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







      def __init__( self					,					_UpperCAmelCase=50_265					,					_UpperCAmelCase=1_024					,					_UpperCAmelCase=12					,					_UpperCAmelCase=4_096					,					_UpperCAmelCase=16					,					_UpperCAmelCase=12					,					_UpperCAmelCase=4_096					,					_UpperCAmelCase=16					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=True					,					_UpperCAmelCase=True					,					_UpperCAmelCase="gelu"					,					_UpperCAmelCase=1_024					,					_UpperCAmelCase=0.1					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.02					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=False					,					_UpperCAmelCase=1					,					_UpperCAmelCase=0					,					_UpperCAmelCase=2					,					_UpperCAmelCase=2					,					**_UpperCAmelCase					,					):
            lowerCAmelCase_						=      vocab_size
            lowerCAmelCase_						=      max_position_embeddings
            lowerCAmelCase_						=      d_model
            lowerCAmelCase_						=      encoder_ffn_dim
            lowerCAmelCase_						=      encoder_layers
            lowerCAmelCase_						=      encoder_attention_heads
            lowerCAmelCase_						=      decoder_ffn_dim
            lowerCAmelCase_						=      decoder_layers
            lowerCAmelCase_						=      decoder_attention_heads
            lowerCAmelCase_						=      dropout
            lowerCAmelCase_						=      attention_dropout
            lowerCAmelCase_						=      activation_dropout
            lowerCAmelCase_						=      activation_function
            lowerCAmelCase_						=      init_std
            lowerCAmelCase_						=      encoder_layerdrop
            lowerCAmelCase_						=      decoder_layerdrop
            lowerCAmelCase_						=      classifier_dropout
            lowerCAmelCase_						=      use_cache
            lowerCAmelCase_						=      encoder_layers
            lowerCAmelCase_						=      scale_embedding  # scale factor will be sqrt(d_model) if True
            super().__init__(
                pad_token_id=_UpperCAmelCase					,					bos_token_id=_UpperCAmelCase					,					eos_token_id=_UpperCAmelCase					,					is_encoder_decoder=_UpperCAmelCase					,					forced_eos_token_id=_UpperCAmelCase					,					**_UpperCAmelCase					,					)


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      @property
      def lowercase__		( self):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      OrderedDict(
                      [
                          ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                          ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                      ])

                  if self.use_past:
                        lowerCAmelCase_						=      {0: '''batch'''}
                        lowerCAmelCase_						=      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
                  else:
                        lowerCAmelCase_						=      {0: '''batch''', 1: '''decoder_sequence'''}
                        lowerCAmelCase_						=      {0: '''batch''', 1: '''decoder_sequence'''}

                  if self.use_past:
                        self.fill_with_past_key_values_(_UpperCAmelCase					,					direction='''inputs''')
            elif self.task == "causal-lm":
                  # TODO: figure this case out.
                  lowerCAmelCase_						=      OrderedDict(
                      [
                          ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                          ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                      ])
                  if self.use_past:
                        lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                        for i in range(_UpperCAmelCase):
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
            else:
                  lowerCAmelCase_						=      OrderedDict(
                      [
                          ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                          ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                          ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                          ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                      ])

            return common_inputs







      @property
      def lowercase__		( self):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      super().outputs
            else:
                  lowerCAmelCase_						=      super(_UpperCAmelCase					,					self).outputs
                  if self.use_past:
                        lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                        for i in range(_UpperCAmelCase):
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
            return common_outputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)

            # Generate decoder inputs
            lowerCAmelCase_						=      seq_length if not self.use_past else 1
            lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            lowerCAmelCase_						=      {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
            lowerCAmelCase_						=      dict(**_UpperCAmelCase					,					**_UpperCAmelCase)

            if self.use_past:
                  if not is_torch_available():
                        raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
                  else:
                        import torch
                  lowerCAmelCase_			,		lowerCAmelCase_						=      common_inputs['''input_ids'''].shape
                  lowerCAmelCase_						=      common_inputs['''decoder_input_ids'''].shape[1]
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_attention_heads
                  lowerCAmelCase_						=      (
                      batch,
                      num_encoder_attention_heads,
                      encoder_seq_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )
                  lowerCAmelCase_						=      decoder_seq_length + 3
                  lowerCAmelCase_						=      (
                      batch,
                      num_decoder_attention_heads,
                      decoder_past_length,
                      self._config.hidden_size // num_decoder_attention_heads,
                  )

                  lowerCAmelCase_						=      torch.cat(
                      [common_inputs['''decoder_attention_mask'''], torch.ones(_UpperCAmelCase					,					_UpperCAmelCase)]					,					dim=1)

                  lowerCAmelCase_						=      []
                  # If the number of encoder and decoder layers are present in the model configuration, both are considered
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                  lowerCAmelCase_						=      min(_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      max(_UpperCAmelCase					,					_UpperCAmelCase) - min_num_layers
                  lowerCAmelCase_						=      '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

                  for _ in range(_UpperCAmelCase):
                        common_inputs["past_key_values"].append(
                            (
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                                torch.zeros(_UpperCAmelCase),
                            ))
                  # TODO: test this.
                  lowerCAmelCase_						=      encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
                  for _ in range(_UpperCAmelCase					,					_UpperCAmelCase):
                        common_inputs["past_key_values"].append((torch.zeros(_UpperCAmelCase), torch.zeros(_UpperCAmelCase)))
            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)

            if self.use_past:
                  if not is_torch_available():
                        raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
                  else:
                        import torch
                  lowerCAmelCase_			,		lowerCAmelCase_						=      common_inputs['''input_ids'''].shape
                  # Not using the same length for past_key_values
                  lowerCAmelCase_						=      seqlen + 2
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_attention_heads
                  lowerCAmelCase_						=      (
                      batch,
                      num_encoder_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )

                  lowerCAmelCase_						=      common_inputs['''attention_mask'''].dtype
                  lowerCAmelCase_						=      torch.cat(
                      [common_inputs['''attention_mask'''], torch.ones(_UpperCAmelCase					,					_UpperCAmelCase					,					dtype=_UpperCAmelCase)]					,					dim=1)
                  lowerCAmelCase_						=      [
                      (torch.zeros(_UpperCAmelCase), torch.zeros(_UpperCAmelCase)) for _ in range(_UpperCAmelCase)
                  ]
            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            # Copied from OnnxConfig.generate_dummy_inputs
            # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            lowerCAmelCase_						=      compute_effective_axis_dimension(
                _UpperCAmelCase					,					fixed_dimension=OnnxConfig.default_fixed_batch					,					num_token_to_add=0)

            # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
            lowerCAmelCase_						=      tokenizer.num_special_tokens_to_add(_UpperCAmelCase)
            lowerCAmelCase_						=      compute_effective_axis_dimension(
                _UpperCAmelCase					,					fixed_dimension=OnnxConfig.default_fixed_sequence					,					num_token_to_add=_UpperCAmelCase)

            # Generate dummy inputs according to compute batch and sequence
            lowerCAmelCase_						=      [''' '''.join([tokenizer.unk_token]) * seq_length] * batch_size
            lowerCAmelCase_						=      dict(tokenizer(_UpperCAmelCase					,					return_tensors=_UpperCAmelCase))
            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                      _UpperCAmelCase					,					batch_size=_UpperCAmelCase					,					seq_length=_UpperCAmelCase					,					is_pair=_UpperCAmelCase					,					framework=_UpperCAmelCase)

            elif self.task == "causal-lm":
                  lowerCAmelCase_						=      self._generate_dummy_inputs_for_causal_lm(
                      _UpperCAmelCase					,					batch_size=_UpperCAmelCase					,					seq_length=_UpperCAmelCase					,					is_pair=_UpperCAmelCase					,					framework=_UpperCAmelCase)
            else:
                  lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                      _UpperCAmelCase					,					batch_size=_UpperCAmelCase					,					seq_length=_UpperCAmelCase					,					is_pair=_UpperCAmelCase					,					framework=_UpperCAmelCase)

            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      super()._flatten_past_key_values_(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            else:
                  lowerCAmelCase_						=      super(_UpperCAmelCase					,					self)._flatten_past_key_values_(
                      _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)



