
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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, TFMBartForConditionalGeneration, TFMBartModel





@require_tf
class       _lowerCAmelCase   :
				_lowercase     						=MBartConfig
				_lowercase     						={}
				_lowercase     						="gelu"




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=13							,							_UpperCamelCase=7							,							_UpperCamelCase=True							,							_UpperCamelCase=False							,							_UpperCamelCase=99							,							_UpperCamelCase=32							,							_UpperCamelCase=2							,							_UpperCamelCase=4							,							_UpperCamelCase=37							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=20							,							_UpperCamelCase=2							,							_UpperCamelCase=1							,							_UpperCamelCase=0							,							)							-> Union[str, Any]:
							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_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      max_position_embeddings
							lowerCAmelCase_         =      eos_token_id
							lowerCAmelCase_         =      pad_token_id
							lowerCAmelCase_         =      bos_token_id




				def 	__a		(   self	)							-> Union[str, Any]:
							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_         =      ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size	)

							lowerCAmelCase_         =      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							,							)
							lowerCAmelCase_         =      prepare_mbart_inputs_dict(UpperCAmelCase_							,							UpperCAmelCase_							,							UpperCAmelCase_	)
							return config, inputs_dict




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase	)							-> List[str]:
							lowerCAmelCase_         =      TFMBartModel(config=UpperCAmelCase_	).get_decoder()
							lowerCAmelCase_         =      inputs_dict["input_ids"]

							lowerCAmelCase_         =      input_ids[:1, :]
							lowerCAmelCase_         =      inputs_dict["attention_mask"][:1, :]
							lowerCAmelCase_         =      inputs_dict["head_mask"]
							lowerCAmelCase_         =      1

							# first forward pass
							lowerCAmelCase_         =      model(UpperCAmelCase_							,							attention_mask=UpperCAmelCase_							,							head_mask=UpperCAmelCase_							,							use_cache=UpperCAmelCase_	)

							lowerCAmelCase_      ,    lowerCAmelCase_         =      outputs.to_tuple()
							lowerCAmelCase_         =      past_key_values[1]






def   lowerCamelCase__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    Tuple       ,				__lowerCAmelCase    :    int=None       ,				__lowerCAmelCase    :    Optional[int]=None       ,				__lowerCAmelCase    :    Dict=None       ,				__lowerCAmelCase    :    Tuple=None       ,				__lowerCAmelCase    :    Tuple=None       ,				):




			"""simple docstring"""


			if attention_mask is None:
						lowerCAmelCase_         =      tf.cast(tf.math.not_equal(_snake_case       ,				config.pad_token_id							)       ,				tf.inta							)
			if decoder_attention_mask is None:
						lowerCAmelCase_         =      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:
						lowerCAmelCase_         =      tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
			if decoder_head_mask is None:
						lowerCAmelCase_         =      tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
			if cross_attn_head_mask is None:
						lowerCAmelCase_         =      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       _lowerCAmelCase   (      __a   , __a   , unittest.TestCase			):
				_lowercase     						=(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
				_lowercase     						=(TFMBartForConditionalGeneration,) if is_tf_available() else ()
				_lowercase     						=(
				    {
				        "conversational": TFMBartForConditionalGeneration,
				        "feature-extraction": TFMBartModel,
				        "summarization": TFMBartForConditionalGeneration,
				        "text2text-generation": TFMBartForConditionalGeneration,
				        "translation": TFMBartForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				_lowercase     						=True
				_lowercase     						=False
				_lowercase     						=False




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Dict:
							if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
										# Exception encountered when calling layer '...'
										return True

							return False




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      TFMBartModelTester(self	)
							lowerCAmelCase_         =      ConfigTester(self							,							config_class=UpperCAmelCase_	)




				def 	__a		(   self	)							-> str:
							self.config_tester.run_common_tests()




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_	)





@require_sentencepiece
@require_tokenizers
@require_tf
class       _lowerCAmelCase   (      unittest.TestCase			):
				_lowercase     						=[
				    " UN Chief Says There Is No Military Solution in Syria",
				]
				_lowercase     						=[
				    "Şeful ONU declară că nu există o soluţie militară în Siria",
				]
				_lowercase     						="facebook/mbart-large-en-ro"




				@cached_property
				def 	__a		(   self	)							-> int:
							return AutoTokenizer.from_pretrained(self.model_name	)




				@cached_property
				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
							return model




				def 	__a		(   self							,							**_UpperCamelCase	)							-> Optional[int]:
							lowerCAmelCase_         =      self.translate_src_text(**UpperCAmelCase_	)
							self.assertListEqual(self.expected_text							,							UpperCAmelCase_	)




				def 	__a		(   self							,							**_UpperCamelCase	)							-> List[Any]:
							lowerCAmelCase_         =      self.tokenizer(self.src_text							,							**UpperCAmelCase_							,							return_tensors="tf"	)
							lowerCAmelCase_         =      self.model.generate(
							    model_inputs.input_ids							,							attention_mask=model_inputs.attention_mask							,							num_beams=2	)
							lowerCAmelCase_         =      self.tokenizer.batch_decode(UpperCAmelCase_							,							skip_special_tokens=UpperCAmelCase_	)
							return generated_words




				@slow
				def 	__a		(   self	)							-> Union[str, Any]:
							self._assert_generated_batch_equal_expected()








'''simple docstring'''





def 							lowerCamelCase	(					):





    '''simple docstring'''


    return [list(range(1_000 - i  ,-1_000 - i  ,-1							)							) for i in range(1_000							)]


SCREAMING_SNAKE_CASE__          =  generate_large_matrix()
SCREAMING_SNAKE_CASE__          =  (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    assert all(row == sorted(_snake_case  ,reverse=_snake_case							) for row in grid							)
    assert all(list(_snake_case							) == sorted(_snake_case  ,reverse=_snake_case							) for col in zip(*_snake_case							)							)




def 							lowerCamelCase	(					_snake_case	:    list[int]							):





    '''simple docstring'''


    lowercase__									=    0
    lowercase__									=    len(_snake_case							) - 1

    # Edge cases such as no values or all numbers are negative.
    if not array or array[0] < 0:
        return 0

    while right + 1 > left:
        lowercase__									=    (left + right) // 2
        lowercase__									=    array[mid]

        # Num must be negative and the index must be greater than or equal to 0.
        if num < 0 and array[mid - 1] >= 0:
            return mid

        if num >= 0:
            lowercase__									=    mid + 1
        else:
            lowercase__									=    mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
    return len(_snake_case							)




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    lowercase__									=    0
    lowercase__									=    len(grid[0]							)

    for i in range(len(_snake_case							)							):
        lowercase__									=    find_negative_index(grid[i][:bound]							)
        total += bound
    return (len(_snake_case							) * len(grid[0]							)) - total




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    return len([number for row in grid for number in row if number < 0]							)




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    lowercase__									=    0
    for row in grid:
        for i, number in enumerate(_snake_case							):
            if number < 0:
                total += len(_snake_case							) - i
                break
    return total




def 							lowerCamelCase	(					):





    '''simple docstring'''


    from timeit import timeit

    print("Running benchmarks"							)
    lowercase__									=    (
        "from __main__ import count_negatives_binary_search, "
        "count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
    )
    for func in (
        "count_negatives_binary_search",  # took 0.7727 seconds
        "count_negatives_brute_force_with_break",  # took 4.6505 seconds
        "count_negatives_brute_force",  # took 12.8160 seconds
    ):
        lowercase__									=    timeit(f'''{func}(grid=grid)'''  ,setup=_snake_case  ,number=500							)
        print(f'''{func}() took {time:0.4f} seconds'''							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()




