





import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:  # picklable for multiprocessing






				"""simple docstring"""


				return x.sum()






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Tuple:  # picklable for multiprocessing






				"""simple docstring"""


				return i + 1



@dataclass
class  __lowerCAmelCase	:






			"""simple docstring"""




			lowerCamelCase_					:					int
			lowerCamelCase_					:					str




class  __lowerCAmelCase	(      _a					):






			"""simple docstring"""




			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  {}
							snake_case_						:   Union[str, Any]           =  []
							snake_case_						:   Dict           =  1
							snake_case_						:   Union[str, Any]           =  [1, 2]
							snake_case_						:   Tuple           =  {'''a''': 1, '''b''': 2}
							snake_case_						:   Union[str, Any]           =  {'''a''': [1, 2], '''b''': [3, 4]}
							snake_case_						:   List[Any]           =  {'''a''': {'''1''': 1}, '''b''': 2}
							snake_case_						:   Optional[Any]           =  {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
							snake_case_						:   int           =  {}
							snake_case_						:   List[Any]           =  []
							snake_case_						:   Dict           =  2
							snake_case_						:   Any           =  [2, 3]
							snake_case_						:   Any           =  {'''a''': 2, '''b''': 3}
							snake_case_						:   List[Any]           =  {'''a''': [2, 3], '''b''': [4, 5]}
							snake_case_						:   Any           =  {'''a''': {'''1''': 2}, '''b''': 3}
							snake_case_						:   List[Any]           =  {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )

							snake_case_						:   int           =  2
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )

							snake_case_						:   str           =  {'''a''': np.eye(2   ), '''b''': np.zeros(3   ), '''c''': np.ones(2   )}
							snake_case_						:   Union[str, Any]           =  {'''a''': 2, '''b''': 0, '''c''': 2}
							snake_case_						:   Tuple           =  {
							    '''a''': np.eye(2   ).astype(__magic_name__   ),
							    '''b''': np.zeros(3   ).astype(__magic_name__   ),
							    '''c''': np.ones(2   ).astype(__magic_name__   ),
							}
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							map_numpy=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(
							    {k: v.tolist() for k, v in map_nested(__magic_name__ ,							__magic_name__ ,							map_numpy=__magic_name__   ).items()} ,							{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,							)
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							map_numpy=__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(
							    {k: v.tolist() for k, v in map_nested(__magic_name__ ,							__magic_name__ ,							map_numpy=__magic_name__ ,							num_proc=__magic_name__   ).items()} ,							{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,							)
							with self.assertRaises(__magic_name__   ):  # can't pickle a local lambda
											map_nested(lambda __magic_name__   : x + 1 ,							__magic_name__ ,							num_proc=__magic_name__   )






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Dict           =  {'''a''': 1, '''b''': 2}
							snake_case_						:   Any           =  {'''a''': 3, '''b''': 4}
							snake_case_						:   Tuple           =  {'''a''': 5, '''b''': 6}
							snake_case_						:   List[Any]           =  sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))]   )
							self.assertEqual(sorted(zip_dict(__magic_name__ ,							__magic_name__ ,							__magic_name__   )   ) ,							__magic_name__   )






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							class  __lowerCAmelCase	:






										"""simple docstring"""




										lowerCamelCase_					:					Union[str, Any]	      =				'''bar'''




							snake_case_						:   int           =  Foo()
							self.assertEqual(foo.my_attr ,							'''bar'''   )
							with temporary_assignment(__magic_name__ ,							'''my_attr''' ,							'''BAR'''   ):
											self.assertEqual(foo.my_attr ,							'''BAR'''   )
							self.assertEqual(foo.my_attr ,							'''bar'''   )






@pytest.mark.parametrize(
    '''iterable_length, num_proc, expected_num_proc'''			,    [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]			,    )
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


				with patch('''datasets.utils.py_utils._single_map_nested'''			) as mock_single_map_nested, patch(
				    '''datasets.parallel.parallel.Pool'''			) as mock_multiprocessing_pool:
								snake_case_						:   Optional[int]           =  {f'''{i}''': i for i in range(_UpperCamelCase			)}
								snake_case_						:   Optional[Any]           =  map_nested(lambda _UpperCamelCase			: x + 10			,    _UpperCamelCase			,    num_proc=_UpperCamelCase			,    parallel_min_length=16			)
								if expected_num_proc == 1:
												assert mock_single_map_nested.called
												assert not mock_multiprocessing_pool.called
								else:
												assert not mock_single_map_nested.called
												assert mock_multiprocessing_pool.called
												assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc



class  __lowerCAmelCase	(      _a					):






			"""simple docstring"""




			@require_tf
			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							import tensorflow as tf
							from tensorflow.keras import layers

							snake_case_						:   Optional[Any]           =  layers.Dense(2   )

							def gen_random_output():
											snake_case_						:   Union[str, Any]           =  tf.random.uniform((1, 3)   )
											return model(__magic_name__   ).numpy()

							with temp_seed(42 ,							set_tensorflow=__magic_name__   ):
											snake_case_						:   Tuple           =  gen_random_output()
							with temp_seed(42 ,							set_tensorflow=__magic_name__   ):
											snake_case_						:   int           =  gen_random_output()
							snake_case_						:   Any           =  gen_random_output()

							np.testing.assert_equal(__magic_name__ ,							__magic_name__   )
							self.assertGreater(np.abs(outa - outa   ).sum() ,							0   )






			@require_torch
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							import torch

							def gen_random_output():
											snake_case_						:   List[Any]           =  torch.nn.Linear(3 ,							2   )
											snake_case_						:   Dict           =  torch.rand(1 ,							3   )
											return model(__magic_name__   ).detach().numpy()

							with temp_seed(42 ,							set_pytorch=__magic_name__   ):
											snake_case_						:   Optional[int]           =  gen_random_output()
							with temp_seed(42 ,							set_pytorch=__magic_name__   ):
											snake_case_						:   Any           =  gen_random_output()
							snake_case_						:   List[str]           =  gen_random_output()

							np.testing.assert_equal(__magic_name__ ,							__magic_name__   )
							self.assertGreater(np.abs(outa - outa   ).sum() ,							0   )






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							def gen_random_output():
											return np.random.rand(1 ,							3   )

							with temp_seed(42   ):
											snake_case_						:   int           =  gen_random_output()
							with temp_seed(42   ):
											snake_case_						:   List[str]           =  gen_random_output()
							snake_case_						:   int           =  gen_random_output()

							np.testing.assert_equal(__magic_name__ ,							__magic_name__   )
							self.assertGreater(np.abs(outa - outa   ).sum() ,							0   )






@pytest.mark.parametrize('''input_data'''			,    [{}]			)
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


				snake_case_						:   Union[str, Any]           =  NestedDataStructure(_UpperCamelCase			).data
				assert output_data == input_data






@pytest.mark.parametrize(
    '''data, expected_output'''			,    [
        ({}, []),
        ([], []),
        ('''foo''', ['''foo''']),
        (['''foo''', '''bar'''], ['''foo''', '''bar''']),
        ([['''foo''', '''bar''']], ['''foo''', '''bar''']),
        ([[['''foo'''], ['''bar''']]], ['''foo''', '''bar''']),
        ([[['''foo'''], '''bar''']], ['''foo''', '''bar''']),
        ({'''a''': 1, '''b''': 2}, [1, 2]),
        ({'''a''': [1, 2], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[[3], [4]]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, [4]]}, [1, 2, 3, 4]),
        ({'''a''': {'''1''': 1}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': [2]}, [1, 2]),
    ]			,    )
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


				snake_case_						:   Tuple           =  NestedDataStructure(_UpperCamelCase			).flatten()
				assert output == expected_output






def 					lowerCamelCase_						(    )		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   Tuple           =  A(x=1			,    y='''foobar'''			)
				snake_case_						:   List[Any]           =  {'''x''': 1, '''y''': '''foobar'''}
				assert asdict(_UpperCamelCase			) == expected_output

				snake_case_						:   Tuple           =  {'''a''': {'''b''': A(x=10			,    y='''foo'''			)}, '''c''': [A(x=20			,    y='''bar'''			)]}
				snake_case_						:   List[str]           =  {'''a''': {'''b''': {'''x''': 10, '''y''': '''foo'''}}, '''c''': [{'''x''': 20, '''y''': '''bar'''}]}
				assert asdict(_UpperCamelCase			) == expected_output

				with pytest.raises(_UpperCamelCase			):
								asdict([1, A(x=10			,    y='''foo'''			)]			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				return text.split()






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				yield (time.time(), content)
				time.sleep(2			)
				yield (time.time(), content)






def 					lowerCamelCase_						(    )		->						Union[str, Any]:






				"""simple docstring"""


				with Pool(2			) as pool:
								snake_case_						:   int           =  list(iflatmap_unordered(_UpperCamelCase			,    _split_text			,    kwargs_iterable=[{'''text''': '''hello there'''}] * 10			)			)
								assert out.count('''hello'''			) == 10
								assert out.count('''there'''			) == 10
								assert len(_UpperCamelCase			) == 20

				# check multiprocess from pathos (uses dill for pickling)
				with multiprocess.Pool(2			) as pool:
								snake_case_						:   Any           =  list(iflatmap_unordered(_UpperCamelCase			,    _split_text			,    kwargs_iterable=[{'''text''': '''hello there'''}] * 10			)			)
								assert out.count('''hello'''			) == 10
								assert out.count('''there'''			) == 10
								assert len(_UpperCamelCase			) == 20

				# check that we get items as fast as possible
				with Pool(2			) as pool:
								snake_case_						:   Union[str, Any]           =  []
								for yield_time, content in iflatmap_unordered(
								    _UpperCamelCase			,    _aseconds_generator_of_aitems_with_timing			,    kwargs_iterable=[{'''content''': '''a'''}, {'''content''': '''b'''}]			):
												assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
												out.append(_UpperCamelCase			)
								assert out.count('''a'''			) == 2
								assert out.count('''b'''			) == 2
								assert len(_UpperCamelCase			) == 4







from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase=None			,    _UpperCamelCase=None			)		->						Optional[Any]:






				"""simple docstring"""


				if attention_mask is None:
								snake_case_						:   Optional[Any]           =  tf.cast(tf.math.not_equal(_UpperCamelCase			,    config.pad_token_id			)			,    tf.inta			)
				return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class  __lowerCAmelCase	:
			lowerCamelCase_					:					Optional[Any]	      =				OPTConfig
			lowerCamelCase_					:					str	      =				{}
			lowerCamelCase_					:					Tuple	      =				'''gelu'''






			def __init__(self ,							__magic_name__ ,							__magic_name__=13 ,							__magic_name__=7 ,							__magic_name__=True ,							__magic_name__=False ,							__magic_name__=99 ,							__magic_name__=16 ,							__magic_name__=2 ,							__magic_name__=4 ,							__magic_name__=4 ,							__magic_name__="gelu" ,							__magic_name__=0.1 ,							__magic_name__=0.1 ,							__magic_name__=20 ,							__magic_name__=2 ,							__magic_name__=1 ,							__magic_name__=0 ,							__magic_name__=16 ,							__magic_name__=16 ,							)					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  parent
							snake_case_						:   int           =  batch_size
							snake_case_						:   Optional[int]           =  seq_length
							snake_case_						:   Dict           =  is_training
							snake_case_						:   str           =  use_labels
							snake_case_						:   List[str]           =  vocab_size
							snake_case_						:   Dict           =  hidden_size
							snake_case_						:   Optional[Any]           =  num_hidden_layers
							snake_case_						:   Union[str, Any]           =  num_attention_heads
							snake_case_						:   Optional[Any]           =  intermediate_size
							snake_case_						:   Optional[Any]           =  hidden_act
							snake_case_						:   Dict           =  hidden_dropout_prob
							snake_case_						:   Optional[int]           =  attention_probs_dropout_prob
							snake_case_						:   Dict           =  max_position_embeddings
							snake_case_						:   Any           =  eos_token_id
							snake_case_						:   Union[str, Any]           =  pad_token_id
							snake_case_						:   List[Any]           =  bos_token_id
							snake_case_						:   List[Any]           =  embed_dim
							snake_case_						:   Optional[int]           =  word_embed_proj_dim
							snake_case_						:   Any           =  False






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  ids_tensor([self.batch_size, self.seq_length - 1] ,							self.vocab_size   )
							snake_case_						:   Tuple           =  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   ) ,							1   )
							snake_case_						:   Union[str, Any]           =  tf.concat([input_ids, eos_tensor] ,							axis=1   )

							snake_case_						:   Optional[int]           =  self.config_cls(
							    vocab_size=self.vocab_size ,							hidden_size=self.hidden_size ,							num_hidden_layers=self.num_hidden_layers ,							num_attention_heads=self.num_attention_heads ,							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_id=self.eos_token_id ,							bos_token_id=self.bos_token_id ,							pad_token_id=self.pad_token_id ,							embed_dim=self.embed_dim ,							word_embed_proj_dim=self.word_embed_proj_dim ,							is_encoder_decoder=__magic_name__ ,							**self.config_updates ,							)
							snake_case_						:   Optional[Any]           =  prepare_opt_inputs_dict(__magic_name__ ,							__magic_name__   )
							return config, inputs_dict






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  TFOPTModel(config=__magic_name__   )
							snake_case_						:   str           =  inputs_dict['''input_ids''']

							snake_case_						:   Tuple           =  input_ids[:1, :]
							snake_case_						:   Union[str, Any]           =  inputs_dict['''attention_mask'''][:1, :]
							snake_case_						:   List[Any]           =  1

							# first forward pass
							snake_case_						:   int           =  model(__magic_name__ ,							attention_mask=__magic_name__ ,							use_cache=__magic_name__   )

							snake_case_      ,							snake_case_						:   Any           =  outputs.to_tuple()

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

							# append to next input_ids and
							snake_case_						:   Any           =  tf.concat([input_ids, next_tokens] ,							axis=-1   )
							snake_case_						:   List[str]           =  tf.concat([attention_mask, next_attn_mask] ,							axis=-1   )

							snake_case_						:   List[str]           =  model(__magic_name__ ,							attention_mask=__magic_name__   )[0]
							snake_case_						:   List[Any]           =  model(__magic_name__ ,							attention_mask=__magic_name__ ,							past_key_values=__magic_name__   )[0]

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

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

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




@require_tf
class  __lowerCAmelCase	(      _a,					_a,					unittest.TestCase					):
			lowerCamelCase_					:					Any	      =				(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
			lowerCamelCase_					:					List[str]	      =				(TFOPTForCausalLM,) if is_tf_available() else ()
			lowerCamelCase_					:					Optional[int]	      =				(
			    {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
			)
			lowerCamelCase_					:					List[str]	      =				False
			lowerCamelCase_					:					Tuple	      =				False
			lowerCamelCase_					:					str	      =				False
			lowerCamelCase_					:					List[str]	      =				10






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   str           =  TFOPTModelTester(self   )
							snake_case_						:   Tuple           =  ConfigTester(self ,							config_class=__magic_name__   )






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							self.config_tester.run_common_tests()






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Any           =  self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*__magic_name__   )






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_      ,							snake_case_						:   List[Any]           =  self.model_tester.prepare_config_and_inputs_for_common()

							def _get_word_embedding_weight(__magic_name__ ,							__magic_name__   ):
											if hasattr(__magic_name__ ,							'''weight'''   ):
															return embedding_layer.weight
											else:
															# Here we build the word embeddings weights if not exists.
															# And then we retry to get the attribute once built.
															model.build()
															if hasattr(__magic_name__ ,							'''weight'''   ):
																			return embedding_layer.weight
															else:
																			return None

							for model_class in self.all_model_classes:
											for size in [config.vocab_size - 10, config.vocab_size + 10]:
															# build the embeddings
															snake_case_						:   Optional[Any]           =  model_class(config=__magic_name__   )
															snake_case_						:   str           =  _get_word_embedding_weight(__magic_name__ ,							model.get_input_embeddings()   )
															snake_case_						:   Tuple           =  _get_word_embedding_weight(__magic_name__ ,							model.get_output_embeddings()   )

															# reshape the embeddings
															model.resize_token_embeddings(__magic_name__   )
															snake_case_						:   int           =  _get_word_embedding_weight(__magic_name__ ,							model.get_input_embeddings()   )
															snake_case_						:   Union[str, Any]           =  _get_word_embedding_weight(__magic_name__ ,							model.get_output_embeddings()   )

															# check that the resized embeddings size matches the desired size.
															snake_case_						:   List[str]           =  size if size is not None else config.vocab_size

															self.assertEqual(new_input_embeddings.shape[0] ,							__magic_name__   )

															# check that weights remain the same after resizing
															snake_case_						:   str           =  True
															for pa, pa in zip(old_input_embeddings.value() ,							new_input_embeddings.value()   ):
																			if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
																							snake_case_						:   int           =  False
															self.assertTrue(__magic_name__   )

															if old_output_embeddings is not None and new_output_embeddings is not None:
																			self.assertEqual(new_output_embeddings.shape[0] ,							__magic_name__   )

																			snake_case_						:   Optional[int]           =  True
																			for pa, pa in zip(old_output_embeddings.value() ,							new_output_embeddings.value()   ):
																							if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
																											snake_case_						:   Optional[Any]           =  False
																			self.assertTrue(__magic_name__   )






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return tf.constant(_UpperCamelCase			,    dtype=tf.intaa			)



@require_tf
class  __lowerCAmelCase	(      unittest.TestCase					):
			lowerCamelCase_					:					Dict	      =				99






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  tf.ones((4, 1) ,							dtype=tf.intaa   ) * 2
							snake_case_						:   Dict           =  tf.concat([ids_tensor((4, 6) ,							self.vocab_size - 3   ) + 3, eos_column_vector] ,							axis=1   )
							snake_case_						:   List[str]           =  input_ids.shape[0]
							snake_case_						:   str           =  OPTConfig(
							    vocab_size=self.vocab_size ,							hidden_size=24 ,							num_hidden_layers=2 ,							num_attention_heads=2 ,							ffn_dim=32 ,							max_position_embeddings=48 ,							eos_token_id=2 ,							pad_token_id=1 ,							bos_token_id=0 ,							)
							return config, input_ids, batch_size




@require_sentencepiece
@require_tf
class  __lowerCAmelCase	(      unittest.TestCase					):






			@slow
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  TFOPTModel.from_pretrained('''facebook/opt-350m'''   )
							snake_case_						:   Optional[Any]           =  _long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]   )
							snake_case_						:   Any           =  tf.not_equal(__magic_name__ ,							model.config.pad_token_id   )
							with tf.GradientTape():
											snake_case_						:   int           =  model(input_ids=__magic_name__ ,							attention_mask=__magic_name__   ).last_hidden_state
							snake_case_						:   int           =  (1, 11, 512)
							self.assertEqual(output.shape ,							__magic_name__   )
							snake_case_						:   str           =  tf.constant(
							    [[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]]   )
							self.assertTrue(np.allclose(output[:, :3, :3] ,							__magic_name__ ,							atol=4e-3   )   )

							snake_case_						:   Union[str, Any]           =  tf.function(__magic_name__ ,							jit_compile=__magic_name__   )
							snake_case_						:   Any           =  xla_generate(__magic_name__ ,							__magic_name__   )[0]
							self.assertTrue(np.allclose(output[:, :3, :3] ,							__magic_name__ ,							atol=4e-2   )   )




@require_tf
@slow
class  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							super().setUp()
							snake_case_						:   Optional[Any]           =  '''facebook/opt-350m'''






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   int           =  TFOPTForCausalLM.from_pretrained(self.path_model   )
							snake_case_						:   Optional[Any]           =  GPTaTokenizer.from_pretrained(self.path_model   )

							snake_case_						:   List[Any]           =  [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of''',
							    '''Paris is the capital of France and''',
							    '''Computers and mobile phones have taken''',
							]
							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
							snake_case_						:   List[str]           =  tokenizer(__magic_name__ ,							return_tensors='''tf''' ,							padding=__magic_name__ ,							add_special_tokens=__magic_name__   )
							snake_case_						:   Optional[Any]           =  tf.math.reduce_mean(model(inputs.input_ids ,							attention_mask=inputs.attention_mask   )[0] ,							axis=-1   )
							snake_case_						:   int           =  tf.constant(
							    [
							        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
							        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
							        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
							        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
							    ]   )
							self.assertTrue(np.allclose(__magic_name__ ,							__magic_name__ ,							atol=1e-4   )   )

							snake_case_						:   str           =  tf.function(__magic_name__ ,							jit_compile=__magic_name__   )
							snake_case_						:   Optional[int]           =  tf.math.reduce_mean(xla_generate(inputs.input_ids ,							attention_mask=inputs.attention_mask   )[0] ,							axis=-1   )
							self.assertTrue(np.allclose(__magic_name__ ,							__magic_name__ ,							atol=1e-4   )   )




@require_tf
@slow
class  __lowerCAmelCase	(      unittest.TestCase					):






			@property
			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							return [
							    "Today is a beautiful day and I want",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Any           =  '''facebook/opt-125m'''

							snake_case_						:   List[str]           =  [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of New York, the city''',
							    '''Paris is the capital of France and the capital''',
							    '''Computers and mobile phones have taken over the''',
							]

							snake_case_						:   Optional[Any]           =  []
							snake_case_						:   Any           =  GPTaTokenizer.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[Any]           =  TFOPTForCausalLM.from_pretrained(__magic_name__   )

							for prompt in self.prompts:
											snake_case_						:   List[Any]           =  tokenizer(__magic_name__ ,							return_tensors='''tf'''   ).input_ids

											snake_case_						:   Any           =  model.generate(__magic_name__ ,							max_length=10   )

											snake_case_						:   Union[str, Any]           =  tokenizer.batch_decode(__magic_name__ ,							skip_special_tokens=__magic_name__   )
											predicted_outputs += generated_string

							self.assertListEqual(__magic_name__ ,							__magic_name__   )






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Dict           =  '''facebook/opt-350m'''

							snake_case_						:   int           =  GPTaTokenizer.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[Any]           =  TFOPTForCausalLM.from_pretrained(__magic_name__   )

							snake_case_						:   List[str]           =  '''left'''

							# use different length sentences to test batching
							snake_case_						:   Optional[Any]           =  [
							    '''Hello, my dog is a little''',
							    '''Today, I''',
							]

							snake_case_						:   Tuple           =  tokenizer(__magic_name__ ,							return_tensors='''tf''' ,							padding=__magic_name__   )
							snake_case_						:   int           =  inputs['''input_ids''']

							snake_case_						:   Optional[int]           =  model.generate(input_ids=__magic_name__ ,							attention_mask=inputs['''attention_mask''']   )

							snake_case_						:   Any           =  tokenizer(sentences[0] ,							return_tensors='''tf'''   ).input_ids
							snake_case_						:   int           =  model.generate(input_ids=__magic_name__   )

							snake_case_						:   List[str]           =  inputs_non_padded.shape[-1] - tf.math.reduce_sum(
							    tf.cast(inputs['''attention_mask'''][-1] ,							tf.intaa   )   )
							snake_case_						:   Optional[Any]           =  tokenizer(sentences[1] ,							return_tensors='''tf'''   ).input_ids
							snake_case_						:   int           =  model.generate(input_ids=__magic_name__ ,							max_length=model.config.max_length - num_paddings   )

							snake_case_						:   Union[str, Any]           =  tokenizer.batch_decode(__magic_name__ ,							skip_special_tokens=__magic_name__   )
							snake_case_						:   List[Any]           =  tokenizer.decode(output_non_padded[0] ,							skip_special_tokens=__magic_name__   )
							snake_case_						:   str           =  tokenizer.decode(output_padded[0] ,							skip_special_tokens=__magic_name__   )

							snake_case_						:   Any           =  [
							    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
							    '''Today, I was in the middle of a conversation with a friend about the''',
							]
							self.assertListEqual(__magic_name__ ,							__magic_name__   )
							self.assertListEqual(__magic_name__ ,							[non_padded_sentence, padded_sentence]   )






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''facebook/opt-350m'''

							snake_case_						:   Optional[Any]           =  [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of San Francisco, the city''',
							    '''Paris is the capital of France and the capital''',
							    '''Computers and mobile phones have taken over the''',
							]

							snake_case_						:   Dict           =  []
							snake_case_						:   Tuple           =  GPTaTokenizer.from_pretrained(__magic_name__   )
							snake_case_						:   Tuple           =  TFOPTForCausalLM.from_pretrained(__magic_name__   )

							for prompt in self.prompts:
											snake_case_						:   Tuple           =  tokenizer(__magic_name__ ,							return_tensors='''tf'''   ).input_ids

											snake_case_						:   int           =  model.generate(__magic_name__ ,							max_length=10   )

											snake_case_						:   Optional[int]           =  tokenizer.batch_decode(__magic_name__ ,							skip_special_tokens=__magic_name__   )
											predicted_outputs += generated_string

							self.assertListEqual(__magic_name__ ,							__magic_name__   )
