





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    __UpperCamelCase      (    a,     a,     a=None,     a=None) ->Optional[int]:
					if attention_mask is None:
										lowerCamelCase__       =       tf.cast(tf.math.not_equal(a,     config.pad_token_id),     tf.inta)
					return {"input_ids": input_ids, "attention_mask": attention_mask}

@require_tf
class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					A__				 =    OPTConfig
					A__				 =    {}
					A__				 =    "gelu"





					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase=13	,  _lowerCAmelCase=7	,  _lowerCAmelCase=True	,  _lowerCAmelCase=False	,  _lowerCAmelCase=99	,  _lowerCAmelCase=16	,  _lowerCAmelCase=2	,  _lowerCAmelCase=4	,  _lowerCAmelCase=4	,  _lowerCAmelCase="gelu"	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase=20	,  _lowerCAmelCase=2	,  _lowerCAmelCase=1	,  _lowerCAmelCase=0	,  _lowerCAmelCase=16	,  _lowerCAmelCase=16	,  ):
										lowerCamelCase__       =       parent
										lowerCamelCase__       =       batch_size
										lowerCamelCase__       =       seq_length
										lowerCamelCase__       =       is_training
										lowerCamelCase__       =       use_labels
										lowerCamelCase__       =       vocab_size
										lowerCamelCase__       =       hidden_size
										lowerCamelCase__       =       num_hidden_layers
										lowerCamelCase__       =       num_attention_heads
										lowerCamelCase__       =       intermediate_size
										lowerCamelCase__       =       hidden_act
										lowerCamelCase__       =       hidden_dropout_prob
										lowerCamelCase__       =       attention_probs_dropout_prob
										lowerCamelCase__       =       max_position_embeddings
										lowerCamelCase__       =       eos_token_id
										lowerCamelCase__       =       pad_token_id
										lowerCamelCase__       =       bos_token_id
										lowerCamelCase__       =       embed_dim
										lowerCamelCase__       =       word_embed_proj_dim
										lowerCamelCase__       =       False





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       ids_tensor([self.batch_size, self.seq_length - 1]	,  self.vocab_size	)
										lowerCamelCase__       =       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)	,  1	)
										lowerCamelCase__       =       tf.concat([input_ids, eos_tensor]	,  axis=1	)

										lowerCamelCase__       =       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=_lowerCAmelCase	,  **self.config_updates	,  )
										lowerCamelCase__       =       prepare_opt_inputs_dict(_lowerCAmelCase	,  _lowerCAmelCase	)
										return config, inputs_dict






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       TFOPTModel(config=_lowerCAmelCase	)
										lowerCamelCase__       =       inputs_dict["input_ids"]

										lowerCamelCase__       =       input_ids[:1, :]
										lowerCamelCase__       =       inputs_dict["attention_mask"][:1, :]
										lowerCamelCase__       =       1

										# first forward pass
										lowerCamelCase__       =       model(_lowerCAmelCase	,  attention_mask=_lowerCAmelCase	,  use_cache=_lowerCAmelCase	)

										lowerCamelCase__    ,		lowerCamelCase__       =       outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										lowerCamelCase__       =       ids_tensor((self.batch_size, 3)	,  config.vocab_size	)
										lowerCamelCase__       =       tf.cast(ids_tensor((self.batch_size, 3)	,  2	)	,  tf.inta	)

										# append to next input_ids and
										lowerCamelCase__       =       tf.concat([input_ids, next_tokens]	,  axis=-1	)
										lowerCamelCase__       =       tf.concat([attention_mask, next_attn_mask]	,  axis=-1	)

										lowerCamelCase__       =       model(_lowerCAmelCase	,  attention_mask=_lowerCAmelCase	)[0]
										lowerCamelCase__       =       model(_lowerCAmelCase	,  attention_mask=_lowerCAmelCase	,  past_key_values=_lowerCAmelCase	)[0]

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

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

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

@require_tf
class SCREAMING_SNAKE_CASE_ (					lowercase_							,       lowercase_							,       unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
					A__				 =    (TFOPTForCausalLM,) if is_tf_available() else ()
					A__				 =    (
					    {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
					)
					A__				 =    False
					A__				 =    False
					A__				 =    False
					A__				 =    1_0





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       TFOPTModelTester(self	)
										lowerCamelCase__       =       ConfigTester(self	,  config_class=_lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										self.config_tester.run_common_tests()





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase	)






					def 					__magic_name__ ( self	):
										lowerCamelCase__    ,		lowerCamelCase__       =       self.model_tester.prepare_config_and_inputs_for_common()

										def _get_word_embedding_weight(_lowerCAmelCase	,  _lowerCAmelCase	):
															if hasattr(_lowerCAmelCase	,  "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(_lowerCAmelCase	,  "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
																				lowerCamelCase__       =       model_class(config=_lowerCAmelCase	)
																				lowerCamelCase__       =       _get_word_embedding_weight(_lowerCAmelCase	,  model.get_input_embeddings()	)
																				lowerCamelCase__       =       _get_word_embedding_weight(_lowerCAmelCase	,  model.get_output_embeddings()	)

																				# reshape the embeddings
																				model.resize_token_embeddings(_lowerCAmelCase	)
																				lowerCamelCase__       =       _get_word_embedding_weight(_lowerCAmelCase	,  model.get_input_embeddings()	)
																				lowerCamelCase__       =       _get_word_embedding_weight(_lowerCAmelCase	,  model.get_output_embeddings()	)

																				# check that the resized embeddings size matches the desired size.
																				lowerCamelCase__       =       size if size is not None else config.vocab_size

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

																				# check that weights remain the same after resizing
																				lowerCamelCase__       =       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:
																														lowerCamelCase__       =       False
																				self.assertTrue(_lowerCAmelCase	)

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

																									lowerCamelCase__       =       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:
																																			lowerCamelCase__       =       False
																									self.assertTrue(_lowerCAmelCase	)





def    __UpperCamelCase      (    a) ->Any:
					return tf.constant(a,     dtype=tf.intaa)

@require_tf
class SCREAMING_SNAKE_CASE_ (					unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    9_9





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       tf.ones((4, 1)	,  dtype=tf.intaa	) * 2
										lowerCamelCase__       =       tf.concat([ids_tensor((4, 6)	,  self.vocab_size - 3	) + 3, eos_column_vector]	,  axis=1	)
										lowerCamelCase__       =       input_ids.shape[0]
										lowerCamelCase__       =       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 SCREAMING_SNAKE_CASE_ (					unittest.TestCase   ):





					"""simple docstring"""


					@slow
					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       TFOPTModel.from_pretrained("facebook/opt-350m"	)
										lowerCamelCase__       =       _long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]	)
										lowerCamelCase__       =       tf.not_equal(_lowerCAmelCase	,  model.config.pad_token_id	)
										with tf.GradientTape():
															lowerCamelCase__       =       model(input_ids=_lowerCAmelCase	,  attention_mask=_lowerCAmelCase	).last_hidden_state
										lowerCamelCase__       =       (1, 11, 512)
										self.assertEqual(output.shape	,  _lowerCAmelCase	)
										lowerCamelCase__       =       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]	,  _lowerCAmelCase	,  atol=4E-3	)	)

										lowerCamelCase__       =       tf.function(_lowerCAmelCase	,  jit_compile=_lowerCAmelCase	)
										lowerCamelCase__       =       xla_generate(_lowerCAmelCase	,  _lowerCAmelCase	)[0]
										self.assertTrue(np.allclose(output[:, :3, :3]	,  _lowerCAmelCase	,  atol=4E-2	)	)

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





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										super().setUp()
										lowerCamelCase__       =       "facebook/opt-350m"






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       TFOPTForCausalLM.from_pretrained(self.path_model	)
										lowerCamelCase__       =       GPTaTokenizer.from_pretrained(self.path_model	)

										lowerCamelCase__       =       [
										    "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
										lowerCamelCase__       =       tokenizer(_lowerCAmelCase	,  return_tensors="tf"	,  padding=_lowerCAmelCase	,  add_special_tokens=_lowerCAmelCase	)
										lowerCamelCase__       =       tf.math.reduce_mean(model(inputs.input_ids	,  attention_mask=inputs.attention_mask	)[0]	,  axis=-1	)
										lowerCamelCase__       =       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(_lowerCAmelCase	,  _lowerCAmelCase	,  atol=1E-4	)	)

										lowerCamelCase__       =       tf.function(_lowerCAmelCase	,  jit_compile=_lowerCAmelCase	)
										lowerCamelCase__       =       tf.math.reduce_mean(xla_generate(inputs.input_ids	,  attention_mask=inputs.attention_mask	)[0]	,  axis=-1	)
										self.assertTrue(np.allclose(_lowerCAmelCase	,  _lowerCAmelCase	,  atol=1E-4	)	)

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





					"""simple docstring"""


					@property
					def 					__magic_name__ ( self	):
										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 					__magic_name__ ( self	):
										lowerCamelCase__       =       "facebook/opt-125m"

										lowerCamelCase__       =       [
										    "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",
										]

										lowerCamelCase__       =       []
										lowerCamelCase__       =       GPTaTokenizer.from_pretrained(_lowerCAmelCase	)
										lowerCamelCase__       =       TFOPTForCausalLM.from_pretrained(_lowerCAmelCase	)

										for prompt in self.prompts:
															lowerCamelCase__       =       tokenizer(_lowerCAmelCase	,  return_tensors="tf"	).input_ids

															lowerCamelCase__       =       model.generate(_lowerCAmelCase	,  max_length=10	)

															lowerCamelCase__       =       tokenizer.batch_decode(_lowerCAmelCase	,  skip_special_tokens=_lowerCAmelCase	)
															predicted_outputs += generated_string

										self.assertListEqual(_lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       "facebook/opt-350m"

										lowerCamelCase__       =       GPTaTokenizer.from_pretrained(_lowerCAmelCase	)
										lowerCamelCase__       =       TFOPTForCausalLM.from_pretrained(_lowerCAmelCase	)

										lowerCamelCase__       =       "left"

										# use different length sentences to test batching
										lowerCamelCase__       =       [
										    "Hello, my dog is a little",
										    "Today, I",
										]

										lowerCamelCase__       =       tokenizer(_lowerCAmelCase	,  return_tensors="tf"	,  padding=_lowerCAmelCase	)
										lowerCamelCase__       =       inputs["input_ids"]

										lowerCamelCase__       =       model.generate(input_ids=_lowerCAmelCase	,  attention_mask=inputs["attention_mask"]	)

										lowerCamelCase__       =       tokenizer(sentences[0]	,  return_tensors="tf"	).input_ids
										lowerCamelCase__       =       model.generate(input_ids=_lowerCAmelCase	)

										lowerCamelCase__       =       inputs_non_padded.shape[-1] - tf.math.reduce_sum(
										    tf.cast(inputs["attention_mask"][-1]	,  tf.intaa	)	)
										lowerCamelCase__       =       tokenizer(sentences[1]	,  return_tensors="tf"	).input_ids
										lowerCamelCase__       =       model.generate(input_ids=_lowerCAmelCase	,  max_length=model.config.max_length - num_paddings	)

										lowerCamelCase__       =       tokenizer.batch_decode(_lowerCAmelCase	,  skip_special_tokens=_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.decode(output_non_padded[0]	,  skip_special_tokens=_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.decode(output_padded[0]	,  skip_special_tokens=_lowerCAmelCase	)

										lowerCamelCase__       =       [
										    "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(_lowerCAmelCase	,  _lowerCAmelCase	)
										self.assertListEqual(_lowerCAmelCase	,  [non_padded_sentence, padded_sentence]	)






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       "facebook/opt-350m"

										lowerCamelCase__       =       [
										    "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",
										]

										lowerCamelCase__       =       []
										lowerCamelCase__       =       GPTaTokenizer.from_pretrained(_lowerCAmelCase	)
										lowerCamelCase__       =       TFOPTForCausalLM.from_pretrained(_lowerCAmelCase	)

										for prompt in self.prompts:
															lowerCamelCase__       =       tokenizer(_lowerCAmelCase	,  return_tensors="tf"	).input_ids

															lowerCamelCase__       =       model.generate(_lowerCAmelCase	,  max_length=10	)

															lowerCamelCase__       =       tokenizer.batch_decode(_lowerCAmelCase	,  skip_special_tokens=_lowerCAmelCase	)
															predicted_outputs += generated_string

										self.assertListEqual(_lowerCAmelCase	,  _lowerCAmelCase	)








from __future__ import annotations

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__    ,		lowerCamelCase__       =       text, pattern
										lowerCamelCase__    ,		lowerCamelCase__       =       len(_lowerCAmelCase	), len(_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):

										for i in range(self.patLen - 1	,  -1	,  -1	):
															if char == self.pattern[i]:
																				return i
										return -1





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):

										for i in range(self.patLen - 1	,  -1	,  -1	):
															if self.pattern[i] != self.text[current_pos + i]:
																				return current_pos + i
										return -1






					def 					__magic_name__ ( self	):
										# searches pattern in text and returns index positions
										lowerCamelCase__       =       []
										for i in range(self.textLen - self.patLen + 1	):
															lowerCamelCase__       =       self.mismatch_in_text(_lowerCAmelCase	)
															if mismatch_index == -1:
																				positions.append(_lowerCAmelCase	)
															else:
																				lowerCamelCase__       =       self.match_in_pattern(self.text[mismatch_index]	)
																				lowerCamelCase__       =       (
																				    mismatch_index - match_index
																				)  # shifting index lgtm [py/multiple-definition]
										return positions

A_												=     "ABAABA"
A_												=     "AB"
A_												=     BoyerMooreSearch(text, pattern)
A_												=     bms.bad_character_heuristic()

if len(positions) == 0:
						print("No match found")
else:
						print("Pattern found in following positions: ")
						print(positions)

