

import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
    import os

    import jax.numpy as jnp
    from jax import jit

    from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
    from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

    __A			:					Optional[Any]						=			'0.12'  # assumed parallelism: 8


if is_torch_available():
    import torch





def 	__a     (      A__			:  Any			,  A__			:  Tuple			,  A__			:  str=None       ):
    if rng is None:
        SCREAMING_SNAKE_CASE										=   random.Random()

    SCREAMING_SNAKE_CASE										=   1
    for dim in shape:
        total_dims *= dim

    SCREAMING_SNAKE_CASE										=   []
    for _ in range(A__       ):
        values.append(rng.randint(0			,  vocab_size - 1       )       )

    SCREAMING_SNAKE_CASE										=   np.array(A__			,  dtype=jnp.intaa       ).reshape(A__       )

    return output





def 	__a     (      A__			:  Union[str, Any]			,  A__			:  Union[str, Any]=None       ):
    SCREAMING_SNAKE_CASE										=   ids_tensor(A__			,  vocab_size=2			,  rng=A__       )
    # make sure that at least one token is attended to for each batch
    SCREAMING_SNAKE_CASE										=   1
    return attn_mask





@require_flax
class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   lowerCamelCase__				=      None
   lowerCamelCase__				=      ()





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

       # cut to half length & take max batch_size 3
       SCREAMING_SNAKE_CASE										=   2
       SCREAMING_SNAKE_CASE										=   inputs["input_ids"].shape[-1] // 2
       SCREAMING_SNAKE_CASE										=   inputs["input_ids"][:max_batch_size, :sequence_length]

       SCREAMING_SNAKE_CASE										=   jnp.ones_like(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   attention_mask[:max_batch_size, :sequence_length]

       # generate max 5 tokens
       SCREAMING_SNAKE_CASE										=   input_ids.shape[-1] + 5
       if config.eos_token_id is not None and config.pad_token_id is None:
           # hack to allow generate for models such as GPT2 as is done in `generate()`
           SCREAMING_SNAKE_CASE										=   config.eos_token_id
       return config, input_ids, attention_mask, max_length





   @is_pt_flax_cross_test
   def      _snake_case		(       self					:			List[str]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   False
       SCREAMING_SNAKE_CASE										=   max_length
       SCREAMING_SNAKE_CASE										=   0

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
           SCREAMING_SNAKE_CASE										=   getattr(__lowerCamelCase     , __lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   pt_model_class(__lowerCamelCase			).eval()
           SCREAMING_SNAKE_CASE										=   load_flax_weights_in_pytorch_model(__lowerCamelCase     , flax_model.params			)

           SCREAMING_SNAKE_CASE										=   flax_model.generate(__lowerCamelCase			).sequences
           SCREAMING_SNAKE_CASE										=   pt_model.generate(torch.tensor(__lowerCamelCase     , dtype=torch.long			)			)

           if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
               SCREAMING_SNAKE_CASE										=   flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

           self.assertListEqual(pt_generation_outputs.numpy().tolist()     , flax_generation_outputs.tolist()			)





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   False
       SCREAMING_SNAKE_CASE										=   max_length

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase			).sequences

           self.assertListEqual(generation_outputs.tolist()     , jit_generation_outputs.tolist()			)





   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   True
       SCREAMING_SNAKE_CASE										=   max_length

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase			).sequences

           self.assertListEqual(generation_outputs.tolist()     , jit_generation_outputs.tolist()			)





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   False
       SCREAMING_SNAKE_CASE										=   max_length
       SCREAMING_SNAKE_CASE										=   2

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase			).sequences

           self.assertListEqual(generation_outputs.tolist()     , jit_generation_outputs.tolist()			)





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   False
       SCREAMING_SNAKE_CASE										=   max_length
       SCREAMING_SNAKE_CASE										=   2
       SCREAMING_SNAKE_CASE										=   2

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[0]     , input_ids.shape[0] * config.num_return_sequences			)





   def      _snake_case		(       self					:			Dict			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   True
       SCREAMING_SNAKE_CASE										=   max_length
       SCREAMING_SNAKE_CASE										=   0.8
       SCREAMING_SNAKE_CASE										=   10
       SCREAMING_SNAKE_CASE										=   0.3
       SCREAMING_SNAKE_CASE										=   1
       SCREAMING_SNAKE_CASE										=   8
       SCREAMING_SNAKE_CASE										=   9

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase			).sequences

           self.assertListEqual(generation_outputs.tolist()     , jit_generation_outputs.tolist()			)





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   max_length
       SCREAMING_SNAKE_CASE										=   1
       SCREAMING_SNAKE_CASE										=   8
       SCREAMING_SNAKE_CASE										=   9

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase			).sequences

           self.assertListEqual(generation_outputs.tolist()     , jit_generation_outputs.tolist()			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()
       SCREAMING_SNAKE_CASE										=   max_length
       SCREAMING_SNAKE_CASE										=   2
       SCREAMING_SNAKE_CASE										=   1
       SCREAMING_SNAKE_CASE										=   8
       SCREAMING_SNAKE_CASE										=   9

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase			).sequences

           self.assertListEqual(generation_outputs.tolist()     , jit_generation_outputs.tolist()			)





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()

       # pad attention mask on the left
       SCREAMING_SNAKE_CASE										=   attention_mask.at[(0, 0)].set(0			)

       SCREAMING_SNAKE_CASE										=   False
       SCREAMING_SNAKE_CASE										=   max_length

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase     , attention_mask=__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase     , attention_mask=__lowerCamelCase			).sequences

           self.assertListEqual(generation_outputs.tolist()     , jit_generation_outputs.tolist()			)





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()

       # pad attention mask on the left
       SCREAMING_SNAKE_CASE										=   attention_mask.at[(0, 0)].set(0			)

       SCREAMING_SNAKE_CASE										=   True
       SCREAMING_SNAKE_CASE										=   max_length

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase     , attention_mask=__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase     , attention_mask=__lowerCamelCase			).sequences

           self.assertListEqual(generation_outputs.tolist()     , jit_generation_outputs.tolist()			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._get_input_ids_and_config()

       # pad attention mask on the left
       SCREAMING_SNAKE_CASE										=   attention_mask.at[(0, 0)].set(0			)

       SCREAMING_SNAKE_CASE										=   2
       SCREAMING_SNAKE_CASE										=   max_length

       for model_class in self.all_generative_model_classes:
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.generate(__lowerCamelCase     , attention_mask=__lowerCamelCase			).sequences
           self.assertEqual(generation_outputs.shape[-1]     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   jit(model.generate			)
           SCREAMING_SNAKE_CASE										=   jit_generate(__lowerCamelCase     , attention_mask=__lowerCamelCase			).sequences

           self.assertListEqual(generation_outputs.tolist()     , jit_generation_outputs.tolist()			)





@require_flax
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Dict			):
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"			)
       SCREAMING_SNAKE_CASE										=   FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"			)

       SCREAMING_SNAKE_CASE										=   "Hello world"
       SCREAMING_SNAKE_CASE										=   tokenizer(__lowerCamelCase     , return_tensors="np"			).input_ids

       # typos are quickly detected (the correct argument is `do_sample`)
       with self.assertRaisesRegex(__lowerCamelCase     , "do_samples"			):
           model.generate(__lowerCamelCase     , do_samples=__lowerCamelCase			)

       # arbitrary arguments that will not be used anywhere are also not accepted
       with self.assertRaisesRegex(__lowerCamelCase     , "foo"			):
           SCREAMING_SNAKE_CASE										=   {"foo": "bar"}
           model.generate(__lowerCamelCase     , **__lowerCamelCase			)


from typing import Any

import numpy as np





def 	__a     (      A__			:  np.ndarray       ):
    return np.array_equal(A__			,  matrix.conjugate().T       )





def 	__a     (      A__			:  np.ndarray			,  A__			:  np.ndarray       ):
    SCREAMING_SNAKE_CASE										=   v.conjugate().T
    SCREAMING_SNAKE_CASE										=   v_star.dot(A__       )
    assert isinstance(A__			,  np.ndarray       )
    return (v_star_dot.dot(A__       )) / (v_star.dot(A__       ))





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]       )
    SCREAMING_SNAKE_CASE										=   np.array([[1], [2], [3]]       )
    assert is_hermitian(A__       ), F"{a} is not hermitian."
    print(rayleigh_quotient(A__			,  A__       )       )

    SCREAMING_SNAKE_CASE										=   np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]       )
    assert is_hermitian(A__       ), F"{a} is not hermitian."
    assert rayleigh_quotient(A__			,  A__       ) == float(3       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    tests()