



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__						:	str					=logging.get_logger(__name__)

lowerCAmelCase__						:	Dict					={
    '''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   UpperCAmelCase_    (  UpperCamelCase_			):

						'''simple docstring'''




						UpperCamelCase__     :					Union[str, Any]							    =    "blenderbot-small"
						UpperCamelCase__     :					Optional[Any]							    =    ["past_key_values"]
						UpperCamelCase__     :					str							    =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






						def __init__(       self ,				_A=50_265 ,				_A=512 ,				_A=8 ,				_A=2_048 ,				_A=16 ,				_A=8 ,				_A=2_048 ,				_A=16 ,				_A=0.0 ,				_A=0.0 ,				_A=True ,				_A=True ,				_A="gelu" ,				_A=512 ,				_A=0.1 ,				_A=0.0 ,				_A=0.0 ,				_A=0.0_2 ,				_A=1 ,				_A=False ,				_A=0 ,				_A=1 ,				_A=2 ,				_A=2 ,				**_A ,				):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   vocab_size
													__SCREAMING_SNAKE_CASE										=   max_position_embeddings
													__SCREAMING_SNAKE_CASE										=   d_model
													__SCREAMING_SNAKE_CASE										=   encoder_ffn_dim
													__SCREAMING_SNAKE_CASE										=   encoder_layers
													__SCREAMING_SNAKE_CASE										=   encoder_attention_heads
													__SCREAMING_SNAKE_CASE										=   decoder_ffn_dim
													__SCREAMING_SNAKE_CASE										=   decoder_layers
													__SCREAMING_SNAKE_CASE										=   decoder_attention_heads
													__SCREAMING_SNAKE_CASE										=   dropout
													__SCREAMING_SNAKE_CASE										=   attention_dropout
													__SCREAMING_SNAKE_CASE										=   activation_dropout
													__SCREAMING_SNAKE_CASE										=   activation_function
													__SCREAMING_SNAKE_CASE										=   init_std
													__SCREAMING_SNAKE_CASE										=   encoder_layerdrop
													__SCREAMING_SNAKE_CASE										=   decoder_layerdrop
													__SCREAMING_SNAKE_CASE										=   use_cache
													__SCREAMING_SNAKE_CASE										=   encoder_layers
													__SCREAMING_SNAKE_CASE										=   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   UpperCAmelCase_    (  UpperCamelCase_			):

						'''simple docstring'''




						@property
						def 			_A      (       self						):







													'''simple docstring'''

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

																				if self.use_past:
																											__SCREAMING_SNAKE_CASE										=   {0: 'batch'}
																											__SCREAMING_SNAKE_CASE										=   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
																				else:
																											__SCREAMING_SNAKE_CASE										=   {0: 'batch', 1: 'decoder_sequence'}
																											__SCREAMING_SNAKE_CASE										=   {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.
																				__SCREAMING_SNAKE_CASE										=   OrderedDict(
																				    [
																				        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																				        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																				    ]						)
																				if self.use_past:
																											__SCREAMING_SNAKE_CASE				,			__SCREAMING_SNAKE_CASE										=   self.num_layers
																											for i in range(_lowerCAmelCase						):
																																		__SCREAMING_SNAKE_CASE										=   {0: 'batch', 2: 'past_sequence + sequence'}
																																		__SCREAMING_SNAKE_CASE										=   {0: 'batch', 2: 'past_sequence + sequence'}
													else:
																				__SCREAMING_SNAKE_CASE										=   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"]:
																				__SCREAMING_SNAKE_CASE										=   super().outputs
													else:
																				__SCREAMING_SNAKE_CASE										=   super(_lowerCAmelCase ,				self						).outputs
																				if self.use_past:
																											__SCREAMING_SNAKE_CASE				,			__SCREAMING_SNAKE_CASE										=   self.num_layers
																											for i in range(_lowerCAmelCase						):
																																		__SCREAMING_SNAKE_CASE										=   {0: 'batch', 2: 'past_sequence + sequence'}
																																		__SCREAMING_SNAKE_CASE										=   {0: 'batch', 2: 'past_sequence + sequence'}
													return common_outputs






						def 			_A      (       self ,				_A ,				_A = -1 ,				_A = -1 ,				_A = False ,				_A = None ,				):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    _lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)

													# Generate decoder inputs
													__SCREAMING_SNAKE_CASE										=   seq_length if not self.use_past else 1
													__SCREAMING_SNAKE_CASE										=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    _lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)
													__SCREAMING_SNAKE_CASE										=   {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
													__SCREAMING_SNAKE_CASE										=   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
																				__SCREAMING_SNAKE_CASE				,			__SCREAMING_SNAKE_CASE										=   common_inputs['input_ids'].shape
																				__SCREAMING_SNAKE_CASE										=   common_inputs['decoder_input_ids'].shape[1]
																				__SCREAMING_SNAKE_CASE				,			__SCREAMING_SNAKE_CASE										=   self.num_attention_heads
																				__SCREAMING_SNAKE_CASE										=   (
																				    batch,
																				    num_encoder_attention_heads,
																				    encoder_seq_length,
																				    self._config.hidden_size // num_encoder_attention_heads,
																				)
																				__SCREAMING_SNAKE_CASE										=   decoder_seq_length + 3
																				__SCREAMING_SNAKE_CASE										=   (
																				    batch,
																				    num_decoder_attention_heads,
																				    decoder_past_length,
																				    self._config.hidden_size // num_decoder_attention_heads,
																				)

																				__SCREAMING_SNAKE_CASE										=   torch.cat(
																				    [common_inputs['decoder_attention_mask'], torch.ones(_lowerCAmelCase ,				_lowerCAmelCase						)] ,				dim=1						)

																				__SCREAMING_SNAKE_CASE										=   []
																				# If the number of encoder and decoder layers are present in the model configuration, both are considered
																				__SCREAMING_SNAKE_CASE				,			__SCREAMING_SNAKE_CASE										=   self.num_layers
																				__SCREAMING_SNAKE_CASE										=   min(_lowerCAmelCase ,				_lowerCAmelCase						)
																				__SCREAMING_SNAKE_CASE										=   max(_lowerCAmelCase ,				_lowerCAmelCase						) - min_num_layers
																				__SCREAMING_SNAKE_CASE										=   '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.
																				__SCREAMING_SNAKE_CASE										=   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 ,				_A ,				_A = -1 ,				_A = -1 ,				_A = False ,				_A = None ,				):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   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
																				__SCREAMING_SNAKE_CASE				,			__SCREAMING_SNAKE_CASE										=   common_inputs['input_ids'].shape
																				# Not using the same length for past_key_values
																				__SCREAMING_SNAKE_CASE										=   seqlen + 2
																				__SCREAMING_SNAKE_CASE				,			__SCREAMING_SNAKE_CASE										=   self.num_layers
																				__SCREAMING_SNAKE_CASE				,			__SCREAMING_SNAKE_CASE										=   self.num_attention_heads
																				__SCREAMING_SNAKE_CASE										=   (
																				    batch,
																				    num_encoder_attention_heads,
																				    past_key_values_length,
																				    self._config.hidden_size // num_encoder_attention_heads,
																				)

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






						def 			_A      (       self ,				_A ,				_A = -1 ,				_A = -1 ,				_A = False ,				_A = None ,				):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   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
													__SCREAMING_SNAKE_CASE										=   tokenizer.num_special_tokens_to_add(_lowerCAmelCase						)
													__SCREAMING_SNAKE_CASE										=   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
													__SCREAMING_SNAKE_CASE										=   [' '.join([tokenizer.unk_token]						) * seq_length] * batch_size
													__SCREAMING_SNAKE_CASE										=   dict(tokenizer(_lowerCAmelCase ,				return_tensors=_lowerCAmelCase						)						)
													return common_inputs






						def 			_A      (       self ,				_A ,				_A = -1 ,				_A = -1 ,				_A = False ,				_A = None ,				):







													'''simple docstring'''

													if self.task in ["default", "seq2seq-lm"]:
																				__SCREAMING_SNAKE_CASE										=   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":
																				__SCREAMING_SNAKE_CASE										=   self._generate_dummy_inputs_for_causal_lm(
																				    _lowerCAmelCase ,				batch_size=_lowerCAmelCase ,				seq_length=_lowerCAmelCase ,				is_pair=_lowerCAmelCase ,				framework=_lowerCAmelCase						)
													else:
																				__SCREAMING_SNAKE_CASE										=   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 ,				_A ,				_A ,				_A ,				_A						):







													'''simple docstring'''

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













'''simple docstring'''


import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
       import torch

       from transformers import AutoModelForCausalLM

@require_torch
class  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   tokenizer.decode(greedy_ids[0]		)

     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)
     # The greedy text should be printed to stdout, except for the final "\n" in the streamer
     _lowerCAmelCase						      =   cs.out[:-1]

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   tokenizer.decode(greedy_ids[0]		)

     _lowerCAmelCase						      =   TextIteratorStreamer(_lowerCAmelCase		)
     _lowerCAmelCase						      =   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
     _lowerCAmelCase						      =   Thread(target=model.generate      ,  kwargs=_lowerCAmelCase		)
     thread.start()
     _lowerCAmelCase						      =   ""
     for new_text in streamer:
         streamer_text += new_text

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   greedy_ids[:, input_ids.shape[1] :]
     _lowerCAmelCase						      =   tokenizer.decode(new_greedy_ids[0]		)

     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase      ,  skip_prompt=_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)
     # The greedy text should be printed to stdout, except for the final "\n" in the streamer
     _lowerCAmelCase						      =   cs.out[:-1]

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
     # with actual models -- the dummy models' tokenizers are not aligned with their models, and
     # `skip_special_tokens=True` has no effect on them
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("distilgpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("distilgpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   torch.ones((1, 5)      ,  device=_lowerCAmelCase		).long() * model.config.bos_token_id
     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase      ,  skip_special_tokens=_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=1      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)

     # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
     # re-tokenized, must only contain one token
     _lowerCAmelCase						      =   cs.out[:-1]  # Remove the final "\n"
     _lowerCAmelCase						      =   tokenizer(_lowerCAmelCase      ,  return_tensors="pt"		)
     self.assertEqual(streamer_text_tokenized.input_ids.shape      ,  (1, 1)		)





 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   TextIteratorStreamer(_lowerCAmelCase      ,  timeout=0.001		)
     _lowerCAmelCase						      =   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
     _lowerCAmelCase						      =   Thread(target=model.generate      ,  kwargs=_lowerCAmelCase		)
     thread.start()

     # The streamer will timeout after 0.001 seconds, so an exception will be raised
     with self.assertRaises(_lowerCAmelCase		):
         _lowerCAmelCase						      =   ""
         for new_text in streamer:
             streamer_text += new_text


