"""simple docstring"""






import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           GPTNeoXForCausalLM,
           GPTNeoXForQuestionAnswering,
           GPTNeoXForSequenceClassification,
           GPTNeoXForTokenClassification,
           GPTNeoXModel,
       )





class 				_A		:

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase=13	, __lowerCAmelCase=7	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=99	, __lowerCAmelCase=64	, __lowerCAmelCase=5	, __lowerCAmelCase=4	, __lowerCAmelCase=37	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.1	, __lowerCAmelCase=512	, __lowerCAmelCase=16	, __lowerCAmelCase=2	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=3	, __lowerCAmelCase=4	, __lowerCAmelCase=None	, ):







            """simple docstring"""



            lowercase														=			parent
            lowercase														=			batch_size
            lowercase														=			seq_length
            lowercase														=			is_training
            lowercase														=			use_input_mask
            lowercase														=			use_token_type_ids
            lowercase														=			use_labels
            lowercase														=			vocab_size
            lowercase														=			hidden_size
            lowercase														=			num_hidden_layers
            lowercase														=			num_attention_heads
            lowercase														=			intermediate_size
            lowercase														=			hidden_act
            lowercase														=			hidden_dropout_prob
            lowercase														=			attention_probs_dropout_prob
            lowercase														=			max_position_embeddings
            lowercase														=			type_vocab_size
            lowercase														=			type_sequence_label_size
            lowercase														=			initializer_range
            lowercase														=			num_labels
            lowercase														=			num_choices
            lowercase														=			scope
            lowercase														=			vocab_size - 1

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size		)

            lowercase														=			None
            if self.use_input_mask:
                  lowercase														=			random_attention_mask([self.batch_size, self.seq_length]		)

            lowercase														=			None
            if self.use_labels:
                  lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.num_labels		)

            lowercase														=			self.get_config()

            return config, input_ids, input_mask, token_labels

      def        A__ (		self		):







            """simple docstring"""



            return GPTNeoXConfig(
                vocab_size=self.vocab_size	, hidden_size=self.hidden_size	, num_hidden_layers=self.num_hidden_layers	, num_attention_heads=self.num_attention_heads	, intermediate_size=self.intermediate_size	, hidden_act=self.hidden_act	, hidden_dropout_prob=self.hidden_dropout_prob	, attention_probs_dropout_prob=self.attention_probs_dropout_prob	, max_position_embeddings=self.max_position_embeddings	, type_vocab_size=self.type_vocab_size	, is_decoder=__lowerCAmelCase	, initializer_range=self.initializer_range	, pad_token_id=self.pad_token_id	, )

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase							,							lowercase							,							lowercase														=			self.prepare_config_and_inputs()

            lowercase														=			True

            return config, input_ids, input_mask, token_labels

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			GPTNeoXModel(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase		)
            lowercase														=			model(__lowerCAmelCase		)
            self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, self.seq_length, self.hidden_size)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			True
            lowercase														=			GPTNeoXModel(__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase		)
            self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, self.seq_length, self.hidden_size)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			GPTNeoXForCausalLM(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, labels=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.vocab_size)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.num_labels
            lowercase														=			GPTNeoXForQuestionAnswering(__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase		)
            self.parent.assertEqual(result.start_logits.shape	, (self.batch_size, self.seq_length)		)
            self.parent.assertEqual(result.end_logits.shape	, (self.batch_size, self.seq_length)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.num_labels
            lowercase														=			GPTNeoXForSequenceClassification(__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			ids_tensor([self.batch_size]	, self.type_sequence_label_size		)
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, labels=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.num_labels)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.num_labels
            lowercase														=			GPTNeoXForTokenClassification(__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, labels=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.num_labels)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			True
            lowercase														=			GPTNeoXForCausalLM(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()

            # first forward pass
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, use_cache=__lowerCAmelCase		)
            lowercase														=			outputs.past_key_values

            # create hypothetical multiple next token and extent to next_input_ids
            lowercase														=			ids_tensor((self.batch_size, 3)	, config.vocab_size		)
            lowercase														=			ids_tensor((self.batch_size, 3)	, vocab_size=2		)

            # append to next input_ids and
            lowercase														=			torch.cat([input_ids, next_tokens]	, dim=-1		)
            lowercase														=			torch.cat([input_mask, next_mask]	, dim=-1		)

            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, output_hidden_states=__lowerCAmelCase		)
            lowercase														=			output_from_no_past["""hidden_states"""][0]
            lowercase														=			model(
                __lowerCAmelCase	, attention_mask=__lowerCAmelCase	, past_key_values=__lowerCAmelCase	, output_hidden_states=__lowerCAmelCase	, )["""hidden_states"""][0]

            # select random slice
            lowercase														=			ids_tensor((1,)	, output_from_past.shape[-1]		).item()
            lowercase														=			output_from_no_past[:, -3:, random_slice_idx].detach()
            lowercase														=			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(__lowerCAmelCase	, __lowerCAmelCase	, atol=1E-3		)		)







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.prepare_config_and_inputs()
            lowercase							,							lowercase							,							lowercase							,							lowercase														=			config_and_inputs
            lowercase														=			{"""input_ids""": input_ids, """attention_mask""": input_mask}
            return config, inputs_dict






@require_torch
class 				_A		( lowerCAmelCase						,					lowerCAmelCase						,					lowerCAmelCase						,					unittest.TestCase			):
      snake_case__    :     Union[str, Any]													=			(
          (
              GPTNeoXModel,
              GPTNeoXForCausalLM,
              GPTNeoXForQuestionAnswering,
              GPTNeoXForSequenceClassification,
              GPTNeoXForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      snake_case__    :     str													=			(GPTNeoXForCausalLM,) if is_torch_available() else ()
      snake_case__    :     int													=			(
          {
              'feature-extraction': GPTNeoXModel,
              'question-answering': GPTNeoXForQuestionAnswering,
              'text-classification': GPTNeoXForSequenceClassification,
              'text-generation': GPTNeoXForCausalLM,
              'token-classification': GPTNeoXForTokenClassification,
              'zero-shot': GPTNeoXForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      snake_case__    :     str													=			False
      snake_case__    :     str													=			False
      snake_case__    :     Optional[int]													=			False
      snake_case__    :     List[str]													=			False

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			GPTNeoXModelTester(self		)
            lowercase														=			ConfigTester(self	, config_class=__lowerCAmelCase	, hidden_size=64	, num_attention_heads=8		)

      def        A__ (		self		):







            """simple docstring"""



            self.config_tester.run_common_tests()

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase							,							lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase							,							lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs_for_decoder()
            self.model_tester.create_and_check_model_as_decoder(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase							,							lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs_for_decoder()

            lowercase														=			None

            self.model_tester.create_and_check_model_as_decoder(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase							,							lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_causal_lm(*__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase		)

      @unittest.skip(reason="""Feed forward chunking is not implemented"""		)
      def        A__ (		self		):







            """simple docstring"""



            pass







      @parameterized.expand([("""linear""",), ("""dynamic""",)]		)
      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs_for_common()
            lowercase														=			ids_tensor([1, 10]	, config.vocab_size		)
            lowercase														=			ids_tensor([1, int(config.max_position_embeddings * 1.5		)]	, config.vocab_size		)

            set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
            lowercase														=			GPTNeoXModel(__lowerCAmelCase		)
            original_model.to(__lowerCAmelCase		)
            original_model.eval()
            lowercase														=			original_model(__lowerCAmelCase		).last_hidden_state
            lowercase														=			original_model(__lowerCAmelCase		).last_hidden_state

            set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
            lowercase														=			{"""type""": scaling_type, """factor""": 1_0.0}
            lowercase														=			GPTNeoXModel(__lowerCAmelCase		)
            scaled_model.to(__lowerCAmelCase		)
            scaled_model.eval()
            lowercase														=			scaled_model(__lowerCAmelCase		).last_hidden_state
            lowercase														=			scaled_model(__lowerCAmelCase		).last_hidden_state

            # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
            # maximum sequence length, so the outputs for the short input should match.
            if scaling_type == "dynamic":
                  self.assertTrue(torch.allclose(__lowerCAmelCase	, __lowerCAmelCase	, atol=1E-5		)		)
            else:
                  self.assertFalse(torch.allclose(__lowerCAmelCase	, __lowerCAmelCase	, atol=1E-5		)		)

            # The output should be different for long inputs
            self.assertFalse(torch.allclose(__lowerCAmelCase	, __lowerCAmelCase	, atol=1E-5		)		)






@require_torch
class 				_A		( unittest.TestCase			):

      @slow
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""		)
            for checkpointing in [True, False]:
                  lowercase														=			GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""		)

                  if checkpointing:
                        model.gradient_checkpointing_enable()
                  else:
                        model.gradient_checkpointing_disable()
                  model.to(__lowerCAmelCase		)

                  lowercase														=			tokenizer("""My favorite food is"""	, return_tensors="""pt"""		).to(__lowerCAmelCase		)
                  # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
                  # See: https://github.com/huggingface/transformers/pull/24193
                  lowercase														=			"""My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

                  lowercase														=			model.generate(**__lowerCAmelCase	, do_sample=__lowerCAmelCase	, max_new_tokens=20		)
                  lowercase														=			tokenizer.batch_decode(__lowerCAmelCase		)[0]

                  self.assertEqual(__lowerCAmelCase	, __lowerCAmelCase		)




"""simple docstring"""






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

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


__lowerCAmelCase					: Tuple        =logging.get_logger(__name__)

__lowerCAmelCase					: Tuple        ={
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}





class 				_A		( lowerCAmelCase			):
      snake_case__    :     int													=			'blenderbot-small'
      snake_case__    :     Optional[Any]													=			['past_key_values']
      snake_case__    :     Optional[Any]													=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

      def __init__(		self	, __lowerCAmelCase=5_0265	, __lowerCAmelCase=512	, __lowerCAmelCase=8	, __lowerCAmelCase=2048	, __lowerCAmelCase=16	, __lowerCAmelCase=8	, __lowerCAmelCase=2048	, __lowerCAmelCase=16	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=512	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=1	, __lowerCAmelCase=False	, __lowerCAmelCase=0	, __lowerCAmelCase=1	, __lowerCAmelCase=2	, __lowerCAmelCase=2	, **__lowerCAmelCase	, ):







            """simple docstring"""



            lowercase														=			vocab_size
            lowercase														=			max_position_embeddings
            lowercase														=			d_model
            lowercase														=			encoder_ffn_dim
            lowercase														=			encoder_layers
            lowercase														=			encoder_attention_heads
            lowercase														=			decoder_ffn_dim
            lowercase														=			decoder_layers
            lowercase														=			decoder_attention_heads
            lowercase														=			dropout
            lowercase														=			attention_dropout
            lowercase														=			activation_dropout
            lowercase														=			activation_function
            lowercase														=			init_std
            lowercase														=			encoder_layerdrop
            lowercase														=			decoder_layerdrop
            lowercase														=			use_cache
            lowercase														=			encoder_layers
            lowercase														=			scale_embedding  # scale factor will be sqrt(d_model) if True

            super().__init__(
                pad_token_id=__lowerCAmelCase	, bos_token_id=__lowerCAmelCase	, eos_token_id=__lowerCAmelCase	, is_encoder_decoder=__lowerCAmelCase	, decoder_start_token_id=__lowerCAmelCase	, forced_eos_token_id=__lowerCAmelCase	, **__lowerCAmelCase	, )






class 				_A		( lowerCAmelCase			):

      @property
      def        A__ (		self		):







            """simple docstring"""



            if self.task in ["default", "seq2seq-lm"]:
                  lowercase														=			OrderedDict(
                      [
                          ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                          ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                      ]		)

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

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







            """simple docstring"""



            if self.task in ["default", "seq2seq-lm"]:
                  lowercase														=			super().outputs
            else:
                  lowercase														=			super(__lowerCAmelCase	, self		).outputs
                  if self.use_past:
                        lowercase							,							lowercase														=			self.num_layers
                        for i in range(__lowerCAmelCase		):
                              lowercase														=			{0: """batch""", 2: """past_sequence + sequence"""}
                              lowercase														=			{0: """batch""", 2: """past_sequence + sequence"""}
            return common_outputs

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            lowercase														=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

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

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

                  lowercase														=			torch.cat(
                      [common_inputs["""decoder_attention_mask"""], torch.ones(__lowerCAmelCase	, __lowerCAmelCase		)]	, dim=1		)

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

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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            lowercase														=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)

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

                  lowercase														=			common_inputs["""attention_mask"""].dtype
                  lowercase														=			torch.cat(
                      [common_inputs["""attention_mask"""], torch.ones(__lowerCAmelCase	, __lowerCAmelCase	, dtype=__lowerCAmelCase		)]	, dim=1		)
                  lowercase														=			[
                      (torch.zeros(__lowerCAmelCase		), torch.zeros(__lowerCAmelCase		)) for _ in range(__lowerCAmelCase		)
                  ]
            return common_inputs

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            lowercase														=			compute_effective_axis_dimension(
                __lowerCAmelCase	, 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
            lowercase														=			tokenizer.num_special_tokens_to_add(__lowerCAmelCase		)
            lowercase														=			compute_effective_axis_dimension(
                __lowerCAmelCase	, fixed_dimension=OnnxConfig.default_fixed_sequence	, num_token_to_add=__lowerCAmelCase		)

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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            if self.task in ["default", "seq2seq-lm"]:
                  lowercase														=			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                      __lowerCAmelCase	, batch_size=__lowerCAmelCase	, seq_length=__lowerCAmelCase	, is_pair=__lowerCAmelCase	, framework=__lowerCAmelCase		)

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

            return common_inputs







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            if self.task in ["default", "seq2seq-lm"]:
                  lowercase														=			super()._flatten_past_key_values_(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
            else:
                  lowercase														=			super(__lowerCAmelCase	, self		)._flatten_past_key_values_(
                      __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)



