import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    lowerCAmelCase__      		=					'platform'

    import jax
    import jax.numpy as jnp

    from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__=None      ,    lowerCAmelCase__=None      ):
       if attention_mask is None:
              lowerCAmelCase__	      =   np.where(input_ids != config.pad_token_id      ,    1      ,    0      )
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
       }
@require_flax
class 							a_					:






 '''simple docstring'''






 def __init__(     self   :    Dict     ,       lowercase__   :    Union[str, Any]     ,       lowercase__   :    Tuple=13     ,       lowercase__   :    Optional[Any]=7     ,       lowercase__   :    int=True     ,       lowercase__   :    int=False     ,       lowercase__   :    Optional[Any]=99     ,       lowercase__   :    str=16     ,       lowercase__   :    Any=2     ,       lowercase__   :    str=4     ,       lowercase__   :    Union[str, Any]=4     ,       lowercase__   :    int="gelu"     ,       lowercase__   :    List[Any]=0.1     ,       lowercase__   :    Any=0.1     ,       lowercase__   :    Dict=20     ,       lowercase__   :    Any=2     ,       lowercase__   :    str=1     ,       lowercase__   :    Dict=0     ,       lowercase__   :    Any=16     ,       lowercase__   :    Optional[Any]=16     ,       lowercase__   :    Optional[Any]=0.02     ,       ):







        '''simple docstring'''



        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__	      =   initializer_range
        lowerCAmelCase__	      =   False




 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,       self.vocab_size)     ,       3     ,       self.vocab_size)
        lowerCAmelCase__	      =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,       dtype=np.intaa))     ,       -1)

        lowerCAmelCase__	      =   OPTConfig(
            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     ,       is_encoder_decoder=lowercase__     ,       word_embed_proj_dim=self.word_embed_proj_dim     ,       initializer_range=self.initializer_range     ,       use_cache=lowercase__     ,       )
        lowerCAmelCase__	      =   prepare_opt_inputs_dict(lowercase__     ,       lowercase__)
        return config, inputs_dict




 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.prepare_config_and_inputs()
        return config, inputs_dict




 def 						__snake_case		(     self   :    int     ,       lowercase__   :    Optional[Any]     ,       lowercase__   :    Tuple     ,       lowercase__   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   20
        lowerCAmelCase__	      =   model_class_name(lowercase__)

        lowerCAmelCase__	      =   inputs_dict['input_ids']
        lowerCAmelCase__	      =   inputs_dict['attention_mask']

        lowerCAmelCase__	      =   model.init_cache(input_ids.shape[0]     ,       lowercase__)
        lowerCAmelCase__	      =   jnp.ones((input_ids.shape[0], max_length)     ,       dtype='i4')

        lowerCAmelCase__	      =   jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1)[None, :]     ,       (input_ids.shape[0], input_ids.shape[-1] - 1)     ,       )
        lowerCAmelCase__	      =   model(
            input_ids[:, :-1]     ,       attention_mask=lowercase__     ,       past_key_values=lowercase__     ,       position_ids=lowercase__     ,       )

        lowerCAmelCase__	      =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,       dtype='i4')
        lowerCAmelCase__	      =   model(
            input_ids[:, -1:]     ,       attention_mask=lowercase__     ,       past_key_values=outputs_cache.past_key_values     ,       position_ids=lowercase__     ,       )

        lowerCAmelCase__	      =   model(lowercase__)

        lowerCAmelCase__	      =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
        self.parent.assertTrue(diff < 1e-3     ,       msg=F"""Max diff is {diff}""")




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    str     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    int):







        '''simple docstring'''



        lowerCAmelCase__	      =   20
        lowerCAmelCase__	      =   model_class_name(lowercase__)

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   (
            inputs_dict['input_ids'],
            inputs_dict['attention_mask'],
        )

        lowerCAmelCase__	      =   jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])),
            ]     ,       axis=-1     ,       )

        lowerCAmelCase__	      =   model.init_cache(input_ids.shape[0]     ,       lowercase__)
        lowerCAmelCase__	      =   jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1)[None, :]     ,       (input_ids.shape[0], input_ids.shape[-1] - 1)     ,       )

        lowerCAmelCase__	      =   model(
            input_ids[:, :-1]     ,       attention_mask=lowercase__     ,       past_key_values=lowercase__     ,       position_ids=lowercase__     ,       )
        lowerCAmelCase__	      =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,       dtype='i4')
        lowerCAmelCase__	      =   model(
            input_ids[:, -1:]     ,       past_key_values=outputs_cache.past_key_values     ,       attention_mask=lowercase__     ,       position_ids=lowercase__     ,       )

        lowerCAmelCase__	      =   model(lowercase__     ,       attention_mask=lowercase__)

        lowerCAmelCase__	      =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
        self.parent.assertTrue(diff < 1e-3     ,       msg=F"""Max diff is {diff}""")






@require_flax
class 							a_					(		SCREAMING_SNAKE_CASE			,	unittest.TestCase			,	SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_      					=   (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
 UpperCAmelCase_      					=   () if is_flax_available() else ()




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   FlaxOPTModelTester(self)




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(lowercase__     ,       lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(lowercase__     ,       lowercase__     ,       lowercase__)




 @slow
 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        for model_class_name in self.all_model_classes:
               lowerCAmelCase__	      =   model_class_name.from_pretrained('facebook/opt-125m')
               lowerCAmelCase__	      =   np.ones((1, 1)) * model.config.eos_token_id
               lowerCAmelCase__	      =   model(lowercase__)
               self.assertIsNotNone(lowercase__)






@require_sentencepiece
@require_flax
class 							a_					(		unittest.TestCase		):






 '''simple docstring'''






 @slow
 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__	      =   FlaxOPTModel.from_pretrained('facebook/opt-350m')
        lowerCAmelCase__	      =   jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]])
        lowerCAmelCase__	      =   model(input_ids=lowercase__).last_hidden_state
        lowerCAmelCase__	      =   (1, 11, 512)
        self.assertEqual(output.shape     ,       lowercase__)
        lowerCAmelCase__	      =   jnp.array(
            [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]])
        self.assertTrue(jnp.allclose(output[:, :3, :3]     ,       lowercase__     ,       atol=4e-2))






@require_flax
@slow
class 							a_					(		unittest.TestCase		):






 '''simple docstring'''






 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        super().setUp()
        lowerCAmelCase__	      =   'facebook/opt-350m'




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        lowerCAmelCase__	      =   FlaxOPTForCausalLM.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(lowercase__     ,       return_tensors='jax'     ,       padding=lowercase__     ,       add_special_tokens=lowercase__)
        lowerCAmelCase__	      =   model(inputs.input_ids     ,       attention_mask=inputs.attention_mask)[0].mean(axis=-1)
        lowerCAmelCase__	      =   jnp.array(
            [
                [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(jnp.allclose(lowercase__     ,       lowercase__     ,       atol=4e-2))

        lowerCAmelCase__	      =   jax.jit(lowercase__)
        lowerCAmelCase__	      =   model(inputs.input_ids     ,       attention_mask=inputs.attention_mask)[0].mean(axis=-1)
        self.assertTrue(jnp.allclose(lowercase__     ,       lowercase__     ,       atol=4e-2))






@require_flax
@slow
class 							a_					(		unittest.TestCase		):






 '''simple docstring'''






 @property
 def 						__snake_case		(     self   :    Union[str, Any]):







        '''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 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        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__	      =   FlaxOPTForCausalLM.from_pretrained(lowercase__)
        lowerCAmelCase__	      =   GPTaTokenizer.from_pretrained(lowercase__)

        for prompt in self.prompts:
               lowerCAmelCase__	      =   tokenizer(lowercase__     ,       return_tensors='jax').input_ids

               lowerCAmelCase__	      =   model.generate(lowercase__     ,       max_length=10)
               lowerCAmelCase__	      =   generated_ids[0]

               lowerCAmelCase__	      =   tokenizer.batch_decode(lowercase__     ,       skip_special_tokens=lowercase__)
               predicted_outputs += generated_string

        self.assertListEqual(lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        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__	      =   FlaxOPTForCausalLM.from_pretrained(lowercase__)
        lowerCAmelCase__	      =   GPTaTokenizer.from_pretrained(lowercase__)

        for prompt in self.prompts:
               lowerCAmelCase__	      =   tokenizer(lowercase__     ,       return_tensors='jax').input_ids

               lowerCAmelCase__	      =   model.generate(lowercase__     ,       max_length=10)
               lowerCAmelCase__	      =   generated_ids[0]

               lowerCAmelCase__	      =   tokenizer.batch_decode(lowercase__     ,       skip_special_tokens=lowercase__)
               predicted_outputs += generated_string

        self.assertListEqual(lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   'facebook/opt-125m'
        lowerCAmelCase__	      =   [
            'Today is a beautiful day and I want to thank',
            'In the city of Rome Canaver Canaver Canaver Canaver',
        ]
        lowerCAmelCase__	      =   FlaxOPTForCausalLM.from_pretrained(lowercase__)
        lowerCAmelCase__	      =   GPTaTokenizer.from_pretrained(lowercase__)
        lowerCAmelCase__	      =   tokenizer(
            [
                'Today is a beautiful day and I want to',
                'In the city of',
            ]     ,       return_tensors='jax'     ,       padding=lowercase__     ,       )

        lowerCAmelCase__	      =   jax.jit(model.generate)

        lowerCAmelCase__	      =   jit_generate(inputs['input_ids']     ,       attention_mask=inputs['attention_mask']).sequences

        lowerCAmelCase__	      =   tokenizer.batch_decode(lowercase__     ,       skip_special_tokens=lowercase__)

        self.assertIsNotNone(lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   'facebook/opt-350m'

        lowerCAmelCase__	      =   GPTaTokenizer.from_pretrained(lowercase__)
        lowerCAmelCase__	      =   FlaxOPTForCausalLM.from_pretrained(lowercase__)

        lowerCAmelCase__	      =   'left'

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

        lowerCAmelCase__	      =   tokenizer(lowercase__     ,       return_tensors='jax'     ,       padding=lowercase__)
        lowerCAmelCase__	      =   inputs['input_ids']

        lowerCAmelCase__	      =   model.generate(input_ids=lowercase__     ,       attention_mask=inputs['attention_mask']     ,       trace=lowercase__)

        lowerCAmelCase__	      =   tokenizer(sentences[0]     ,       return_tensors='jax').input_ids
        lowerCAmelCase__	      =   model.generate(input_ids=lowercase__)

        lowerCAmelCase__	      =   inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
        lowerCAmelCase__	      =   tokenizer(sentences[1]     ,       return_tensors='jax').input_ids
        lowerCAmelCase__	      =   model.generate(input_ids=lowercase__     ,       max_length=model.config.max_length - num_paddings)

        lowerCAmelCase__	      =   tokenizer.batch_decode(outputs[0]     ,       skip_special_tokens=lowercase__)
        lowerCAmelCase__	      =   tokenizer.decode(output_non_padded[0][0]     ,       skip_special_tokens=lowercase__)
        lowerCAmelCase__	      =   tokenizer.decode(output_padded[0][0]     ,       skip_special_tokens=lowercase__)

        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(lowercase__     ,       lowercase__)
        self.assertListEqual(lowercase__     ,       [non_padded_sentence, padded_sentence])

import random
import sys

from . import cryptomath_module as cryptomath

lowerCAmelCase__      		=					(
    r' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'
    r'abcdefghijklmnopqrstuvwxyz{|}~'
)






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if mode == "encrypt":
              if key_a == 1:
                     sys.exit(
                         'The affine cipher becomes weak when key '
                         'A is set to 1. Choose different key'      )
              if key_b == 0:
                     sys.exit(
                         'The affine cipher becomes weak when key '
                         'B is set to 0. Choose different key'      )
       if key_a < 0 or key_b < 0 or key_b > len(lowerCAmelCase__      ) - 1:
              sys.exit(
                  'Key A must be greater than 0 and key B must '
                  F"""be between 0 and {len(lowerCAmelCase__      ) - 1}."""      )
       if cryptomath.gcd(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      ) != 1:
              sys.exit(
                  F"""Key A {key_a} and the symbol set size {len(lowerCAmelCase__      )} """
                  'are not relatively prime. Choose a different key.'      )






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__      ,   lowerCAmelCase__	      =   divmod(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      )
       check_keys(lowerCAmelCase__      ,    lowerCAmelCase__      ,    'encrypt'      )
       lowerCAmelCase__	      =   ''
       for symbol in message:
              if symbol in SYMBOLS:
                     lowerCAmelCase__	      =   SYMBOLS.find(lowerCAmelCase__      )
                     cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(lowerCAmelCase__      )]
              else:
                     cipher_text += symbol
       return cipher_text






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__      ,   lowerCAmelCase__	      =   divmod(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      )
       check_keys(lowerCAmelCase__      ,    lowerCAmelCase__      ,    'decrypt'      )
       lowerCAmelCase__	      =   ''
       lowerCAmelCase__	      =   cryptomath.find_mod_inverse(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      )
       for symbol in message:
              if symbol in SYMBOLS:
                     lowerCAmelCase__	      =   SYMBOLS.find(lowerCAmelCase__      )
                     plain_text += SYMBOLS[
                         (sym_index - key_b) * mod_inverse_of_key_a % len(lowerCAmelCase__      )
                     ]
              else:
                     plain_text += symbol
       return plain_text






def  __lowerCamelCase						(		):
       while True:
              lowerCAmelCase__	      =   random.randint(2      ,    len(lowerCAmelCase__      )      )
              lowerCAmelCase__	      =   random.randint(2      ,    len(lowerCAmelCase__      )      )
              if cryptomath.gcd(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      ) == 1 and key_b % len(lowerCAmelCase__      ) != 0:
                     return key_b * len(lowerCAmelCase__      ) + key_b






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   input('Enter message: '      ).strip()
       lowerCAmelCase__	      =   int(input('Enter key [2000 - 9000]: '      ).strip()      )
       lowerCAmelCase__	      =   input('Encrypt/Decrypt [E/D]: '      ).strip().lower()

       if mode.startswith('e'      ):
              lowerCAmelCase__	      =   'encrypt'
              lowerCAmelCase__	      =   encrypt_message(lowerCAmelCase__      ,    lowerCAmelCase__      )
       elif mode.startswith('d'      ):
              lowerCAmelCase__	      =   'decrypt'
              lowerCAmelCase__	      =   decrypt_message(lowerCAmelCase__      ,    lowerCAmelCase__      )
       print(F"""\n{mode.title()}ed text: \n{translated}"""      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    # main()
