



'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
							import tensorflow as tf

							from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class 			UpperCamelCase_ :
	lowercase          = BlenderbotSmallConfig
	lowercase          = {}
	lowercase          = 'gelu'





	def __init__(  self		,		A		,		A=13		,		A=7		,		A=True		,		A=False		,		A=99		,		A=32		,		A=2		,		A=4		,		A=37		,		A=0.1		,		A=0.1		,		A=20		,		A=2		,		A=1		,		A=0		,		)			->			str:
								UpperCAmelCase     :		str							     =		parent
								UpperCAmelCase     :		Any							     =		batch_size
								UpperCAmelCase     :		List[str]							     =		seq_length
								UpperCAmelCase     :		Any							     =		is_training
								UpperCAmelCase     :		Any							     =		use_labels
								UpperCAmelCase     :		Union[str, Any]							     =		vocab_size
								UpperCAmelCase     :		Tuple							     =		hidden_size
								UpperCAmelCase     :		Optional[Any]							     =		num_hidden_layers
								UpperCAmelCase     :		Optional[int]							     =		num_attention_heads
								UpperCAmelCase     :		List[str]							     =		intermediate_size

								UpperCAmelCase     :		Union[str, Any]							     =		hidden_dropout_prob
								UpperCAmelCase     :		Tuple							     =		attention_probs_dropout_prob
								UpperCAmelCase     :		Union[str, Any]							     =		max_position_embeddings
								UpperCAmelCase     :		List[str]							     =		eos_token_id
								UpperCAmelCase     :		Optional[int]							     =		pad_token_id
								UpperCAmelCase     :		str							     =		bos_token_id





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Any							     =		ids_tensor([self.batch_size, self.seq_length - 1]		,		self.vocab_size    )
								UpperCAmelCase     :		Tuple							     =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )		,		1    )
								UpperCAmelCase     :		Dict							     =		tf.concat([input_ids, eos_tensor]		,		axis=1    )

								UpperCAmelCase     :		int							     =		ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size    )

								UpperCAmelCase     :		Optional[int]							     =		self.config_cls(
								    vocab_size=self.vocab_size		,		d_model=self.hidden_size		,		encoder_layers=self.num_hidden_layers		,		decoder_layers=self.num_hidden_layers		,		encoder_attention_heads=self.num_attention_heads		,		decoder_attention_heads=self.num_attention_heads		,		encoder_ffn_dim=self.intermediate_size		,		decoder_ffn_dim=self.intermediate_size		,		dropout=self.hidden_dropout_prob		,		attention_dropout=self.attention_probs_dropout_prob		,		max_position_embeddings=self.max_position_embeddings		,		eos_token_ids=[2]		,		bos_token_id=self.bos_token_id		,		pad_token_id=self.pad_token_id		,		decoder_start_token_id=self.pad_token_id		,		**self.config_updates		,		)
								UpperCAmelCase     :		Optional[Any]							     =		prepare_blenderbot_small_inputs_dict(A		,		A		,		A    )
								return config, inputs_dict






	def  _lowercase(  self		,		A		,		A    )			->			Tuple:
								UpperCAmelCase     :		List[str]							     =		TFBlenderbotSmallModel(config=A    ).get_decoder()
								UpperCAmelCase     :		Dict							     =		inputs_dict["""input_ids"""]

								UpperCAmelCase     :		Union[str, Any]							     =		input_ids[:1, :]
								UpperCAmelCase     :		List[Any]							     =		inputs_dict["""attention_mask"""][:1, :]
								UpperCAmelCase     :		int							     =		inputs_dict["""head_mask"""]
								UpperCAmelCase     :		int							     =		1

								# first forward pass
								UpperCAmelCase     :		List[str]							     =		model(A		,		attention_mask=A		,		head_mask=A		,		use_cache=A    )

								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[int]							     =		outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								UpperCAmelCase     :		Any							     =		ids_tensor((self.batch_size, 3)		,		config.vocab_size    )
								UpperCAmelCase     :		List[str]							     =		tf.cast(ids_tensor((self.batch_size, 3)		,		2    )		,		tf.inta    )

								# append to next input_ids and
								UpperCAmelCase     :		Optional[int]							     =		tf.concat([input_ids, next_tokens]		,		axis=-1    )
								UpperCAmelCase     :		Optional[Any]							     =		tf.concat([attention_mask, next_attn_mask]		,		axis=-1    )

								UpperCAmelCase     :		List[str]							     =		model(A		,		attention_mask=A    )[0]
								UpperCAmelCase     :		Union[str, Any]							     =		model(A		,		attention_mask=A		,		past_key_values=A    )[0]

								self.parent.assertEqual(next_tokens.shape[1]		,		output_from_past.shape[1]    )

								# select random slice
								UpperCAmelCase     :		Any							     =		int(ids_tensor((1,)		,		output_from_past.shape[-1]    )    )
								UpperCAmelCase     :		Optional[int]							     =		output_from_no_past[:, -3:, random_slice_idx]
								UpperCAmelCase     :		Any							     =		output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(A		,		A		,		rtol=1e-3    )




def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase=None	,				_lowercase=None	,				_lowercase=None	,				_lowercase=None	,				_lowercase=None	,				)      ->			Dict:
							if attention_mask is None:
														UpperCAmelCase     :		int							     =		tf.cast(tf.math.not_equal(_lowercase	,				config.pad_token_id		)	,				tf.inta		)
							if decoder_attention_mask is None:
														UpperCAmelCase     :		Union[str, Any]							     =		tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape	,				dtype=tf.inta		),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]	,				config.pad_token_id		)	,				tf.inta		),
														    ]	,				axis=-1	,				)
							if head_mask is None:
														UpperCAmelCase     :		Any							     =		tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
							if decoder_head_mask is None:
														UpperCAmelCase     :		Optional[Any]							     =		tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
							if cross_attn_head_mask is None:
														UpperCAmelCase     :		str							     =		tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}

@require_tf
class 			UpperCamelCase_ (							__magic_name__      ,     __magic_name__      ,     unittest.TestCase			):
	lowercase          = (
	    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
	)
	lowercase          = (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
	lowercase          = (
	    {
	        'conversational': TFBlenderbotSmallForConditionalGeneration,
	        'feature-extraction': TFBlenderbotSmallModel,
	        'summarization': TFBlenderbotSmallForConditionalGeneration,
	        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
	        'translation': TFBlenderbotSmallForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	lowercase          = True
	lowercase          = False
	lowercase          = False





	def  _lowercase(  self    )			->			Dict:
								UpperCAmelCase     :		List[Any]							     =		TFBlenderbotSmallModelTester(self    )
								UpperCAmelCase     :		List[str]							     =		ConfigTester(self		,		config_class=A    )





	def  _lowercase(  self    )			->			Any:
								self.config_tester.run_common_tests()






	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		Optional[Any]							     =		self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*A    )




@require_tokenizers
@require_tf
class 			UpperCamelCase_ (							unittest.TestCase			):
	lowercase          = [
	    'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
	    '  i\'m going to throw up.\nand why is that?'
	]
	lowercase          = 'facebook/blenderbot_small-90M'





	@cached_property
	def  _lowercase(  self    )			->			Optional[Any]:
								# use "old" tokenizer here because of bug when downloading new tokenizer
								return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""    )





	@cached_property
	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[Any]							     =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
								return model






	@slow
	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		str							     =		self.tokenizer(self.src_text		,		return_tensors="""tf"""    )
								UpperCAmelCase     :		Tuple							     =		self.model.generate(
								    model_inputs.input_ids		,		attention_mask=model_inputs.attention_mask		,		num_beams=2		,		use_cache=A		,		)
								UpperCAmelCase     :		str							     =		self.tokenizer.batch_decode(generated_ids.numpy()		,		skip_special_tokens=A    )[0]
								assert generated_words in (
								    "i don't know. i just feel like i'm going to throw up. it's not fun.",
								    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
								    "i'm not sure. i just feel like i've been in a bad situation.",
								)






'''simple docstring'''




import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


a   :			List[Any]					  =    """__DUMMY_TRANSFORMERS_USER__"""
a   :			Tuple					  =    """Dummy User"""
a   :			Optional[Any]					  =    """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"""

a   :			Optional[Any]					  =    """https://hub-ci.huggingface.co"""
a   :			List[Any]					  =    CI_HUB_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
a   :			Tuple					  =    CI_HUB_ENDPOINT + """/{repo_id}/resolve/{revision}/{filename}"""
a   :			str					  =    Path("""~/.huggingface/hub_ci_token""").expanduser()



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							monkeypatch.setattr(
							    """huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"""	,				_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			List[str]:
							monkeypatch.setattr("""datasets.config.HF_ENDPOINT"""	,				_lowercase		)
							monkeypatch.setattr("""datasets.config.HUB_DATASETS_URL"""	,				_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			Any:
							monkeypatch.setattr("""huggingface_hub.hf_api.HfFolder.path_token"""	,				_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Optional[Any]:
							HfFolder.save_token(_lowercase		)
							yield
							HfFolder.delete_token()



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					)      ->			str:
							return HfApi(endpoint=_lowercase		)



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase		)      ->			Union[str, Any]:
							UpperCAmelCase     :		str							     =		HfFolder.get_token()
							HfFolder.save_token(_lowercase		)
							yield CI_HUB_USER_TOKEN
							if previous_token is not None:
														HfFolder.save_token(_lowercase		)



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			Any:
							def _cleanup_repo(_lowercase		):
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)

							return _cleanup_repo



@pytest.fixture
def       __lowerCamelCase  (					_lowercase		)      ->			List[str]:
							@contextmanager
							def _temporary_repo(_lowercase		):
														try:
																					yield repo_id
														finally:
																					cleanup_repo(_lowercase		)

							return _temporary_repo



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[str]:
							UpperCAmelCase     :		str							     =		F'''repo_txt_data-{int(time.time() * 10e3		)}'''
							UpperCAmelCase     :		List[Any]							     =		F'''{CI_HUB_USER}/{repo_name}'''
							hf_api.create_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""	,				private=_lowercase		)
							hf_api.upload_file(
							    token=_lowercase	,				path_or_fileobj=str(_lowercase		)	,				path_in_repo="""data/text_data.txt"""	,				repo_id=_lowercase	,				repo_type="""dataset"""	,				)
							yield repo_id
							try:
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass



@pytest.fixture()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[Any]:
							return hf_private_dataset_repo_txt_data_



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Optional[int]							     =		F'''repo_zipped_txt_data-{int(time.time() * 10e3		)}'''
							UpperCAmelCase     :		Optional[int]							     =		F'''{CI_HUB_USER}/{repo_name}'''
							hf_api.create_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""	,				private=_lowercase		)
							hf_api.upload_file(
							    token=_lowercase	,				path_or_fileobj=str(_lowercase		)	,				path_in_repo="""data.zip"""	,				repo_id=_lowercase	,				repo_type="""dataset"""	,				)
							yield repo_id
							try:
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass



@pytest.fixture()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[str]:
							return hf_private_dataset_repo_zipped_txt_data_



@pytest.fixture(scope="""session"""		)
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Tuple:
							UpperCAmelCase     :		List[Any]							     =		F'''repo_zipped_img_data-{int(time.time() * 10e3		)}'''
							UpperCAmelCase     :		List[str]							     =		F'''{CI_HUB_USER}/{repo_name}'''
							hf_api.create_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""	,				private=_lowercase		)
							hf_api.upload_file(
							    token=_lowercase	,				path_or_fileobj=str(_lowercase		)	,				path_in_repo="""data.zip"""	,				repo_id=_lowercase	,				repo_type="""dataset"""	,				)
							yield repo_id
							try:
														hf_api.delete_repo(_lowercase	,				token=_lowercase	,				repo_type="""dataset"""		)
							except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
														pass



@pytest.fixture()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			List[Any]:
							return hf_private_dataset_repo_zipped_img_data_

