import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, 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
    _SCREAMING_SNAKE_CASE										=						"""platform"""

    import jax
    import jax.numpy as jnp

    from transformers import BlenderbotTokenizer
    from transformers.models.blenderbot.modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        shift_tokens_right,
    )






def  snake_case  (       snake_case__			:str   ,	snake_case__			:Optional[int]   ,	snake_case__			:List[Any]=None   ,	snake_case__			:Union[str, Any]=None   ,	snake_case__			:Optional[Any]=None   ,	snake_case__			:List[Any]=None   ,	snake_case__			:Any=None   ,	snake_case__			:Tuple=None   ,	)						->   Any:



       if attention_mask is None:
              _A        =  np.where(input_ids != config.pad_token_id   ,	1   ,	0)
       if decoder_attention_mask is None:
              _A        =  np.where(decoder_input_ids != config.pad_token_id   ,	1   ,	0)
       if head_mask is None:
              _A        =  np.ones((config.encoder_layers, config.encoder_attention_heads))
       if decoder_head_mask is None:
              _A        =  np.ones((config.decoder_layers, config.decoder_attention_heads))
       if cross_attn_head_mask is None:
              _A        =  np.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": attention_mask,
       }



class 				a							:







 """simple docstring"""






 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_=32		,				lowerCAmelCase_=2		,				lowerCAmelCase_=1		,				lowerCAmelCase_=0		,				lowerCAmelCase_=0.02		,				)  ->					Union[str, Any]:
        _A        =  parent
        _A        =  batch_size
        _A        =  seq_length
        _A        =  is_training
        _A        =  use_labels
        _A        =  vocab_size
        _A        =  hidden_size
        _A        =  num_hidden_layers
        _A        =  num_attention_heads
        _A        =  intermediate_size
        _A        =  hidden_act
        _A        =  hidden_dropout_prob
        _A        =  attention_probs_dropout_prob
        _A        =  max_position_embeddings
        _A        =  eos_token_id
        _A        =  pad_token_id
        _A        =  bos_token_id
        _A        =  initializer_range



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,				self.vocab_size							)		,				3		,				self.vocab_size							)
        _A        =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,				dtype=np.intaa							))		,				-1							)

        _A        =  shift_tokens_right(lowerCAmelCase__		,				1		,				2							)

        _A        =  BlenderbotConfig(
            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_id=self.eos_token_id		,				bos_token_id=self.bos_token_id		,				pad_token_id=self.pad_token_id		,				initializer_range=self.initializer_range		,				use_cache=lowerCAmelCase__		,				)
        _A        =  prepare_blenderbot_inputs_dict(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)
        return config, inputs_dict



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A   ,  _A        =  self.prepare_config_and_inputs()
        return config, inputs_dict



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[Any]:
        _A        =  20
        _A        =  model_class_name(lowerCAmelCase__							)

        _A        =  model.encode(inputs_dict["""input_ids"""]							)

        _A   ,  _A        =  (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        _A        =  model.init_cache(decoder_input_ids.shape[0]		,				lowerCAmelCase__		,				lowerCAmelCase__							)
        _A        =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,				dtype="""i4"""							)

        _A        =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]		,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,				)
        _A        =  model.decode(
            decoder_input_ids[:, :-1]		,				lowerCAmelCase__		,				decoder_attention_mask=lowerCAmelCase__		,				past_key_values=lowerCAmelCase__		,				decoder_position_ids=lowerCAmelCase__		,				)

        _A        =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,				dtype="""i4"""							)
        _A        =  model.decode(
            decoder_input_ids[:, -1:]		,				lowerCAmelCase__		,				decoder_attention_mask=lowerCAmelCase__		,				past_key_values=outputs_cache.past_key_values		,				decoder_position_ids=lowerCAmelCase__		,				)

        _A        =  model.decode(lowerCAmelCase__		,				lowerCAmelCase__							)

        _A        =  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      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Optional[Any]:
        _A        =  20
        _A        =  model_class_name(lowerCAmelCase__							)

        _A        =  model.encode(inputs_dict["""input_ids"""]							)

        _A   ,  _A        =  (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        _A        =  jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
            ]		,				axis=-1		,				)

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

        _A        =  model.decode(
            decoder_input_ids[:, :-1]		,				lowerCAmelCase__		,				decoder_attention_mask=lowerCAmelCase__		,				past_key_values=lowerCAmelCase__		,				decoder_position_ids=lowerCAmelCase__		,				)
        _A        =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,				dtype="""i4"""							)
        _A        =  model.decode(
            decoder_input_ids[:, -1:]		,				lowerCAmelCase__		,				past_key_values=outputs_cache.past_key_values		,				decoder_attention_mask=lowerCAmelCase__		,				decoder_position_ids=lowerCAmelCase__		,				)

        _A        =  model.decode(lowerCAmelCase__		,				lowerCAmelCase__		,				decoder_attention_mask=lowerCAmelCase__							)

        _A        =  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							(			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:List[Any]    			=		99



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ]		,				dtype=np.intaa		,				)

        _A        =  input_ids.shape[0]
        _A        =  BlenderbotConfig(
            vocab_size=self.vocab_size		,				d_model=24		,				encoder_layers=2		,				decoder_layers=2		,				encoder_attention_heads=2		,				decoder_attention_heads=2		,				encoder_ffn_dim=32		,				decoder_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



 def      UpperCAmelCase							(					self							)  ->					str:
        _A   ,  _A   ,  _A        =  self._get_config_and_data()
        _A        =  FlaxBlenderbotForConditionalGeneration(lowerCAmelCase__							)
        _A        =  lm_model(input_ids=lowerCAmelCase__							)
        _A        =  (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["""logits"""].shape		,				lowerCAmelCase__							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  BlenderbotConfig(
            vocab_size=self.vocab_size		,				d_model=14		,				encoder_layers=2		,				decoder_layers=2		,				encoder_attention_heads=2		,				decoder_attention_heads=2		,				encoder_ffn_dim=8		,				decoder_ffn_dim=8		,				max_position_embeddings=48		,				)
        _A        =  FlaxBlenderbotForConditionalGeneration(lowerCAmelCase__							)
        _A        =  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,				dtype=np.intaa							)
        _A        =  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,				dtype=np.intaa							)
        _A        =  lm_model(input_ids=lowerCAmelCase__		,				decoder_input_ids=lowerCAmelCase__							)
        _A        =  (*summary.shape, config.vocab_size)
        self.assertEqual(outputs["""logits"""].shape		,				lowerCAmelCase__							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,				dtype=np.intaa							)
        _A        =  shift_tokens_right(lowerCAmelCase__		,				1		,				2							)
        _A        =  np.equal(lowerCAmelCase__		,				1							).astype(np.floataa							).sum()
        _A        =  np.equal(lowerCAmelCase__		,				1							).astype(np.floataa							).sum()
        self.assertEqual(shifted.shape		,				input_ids.shape							)
        self.assertEqual(lowerCAmelCase__		,				n_pad_before - 1							)
        self.assertTrue(np.equal(shifted[:, 0]		,				2							).all()							)



@require_flax
class 				a							(			_UpperCAmelCase       ,			unittest.TestCase       ,			_UpperCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Any    			=		True
 lowerCamelCase	:Any    			=		(
     (
         FlaxBlenderbotModel,
         FlaxBlenderbotForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 lowerCamelCase	:Optional[Any]    			=		(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  FlaxBlenderbotModelTester(self							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A   ,  _A        =  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(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__							):
                      _A        =  self._prepare_for_class(lowerCAmelCase__		,				lowerCAmelCase__							)
                      _A        =  model_class(lowerCAmelCase__							)

                      @jax.jit
                      def encode_jitted(lowerCAmelCase_		,				lowerCAmelCase_=None		,				**lowerCAmelCase_							):
                             return model.encode(input_ids=lowerCAmelCase__		,				attention_mask=lowerCAmelCase__							)

                      with self.subTest("""JIT Enabled"""							):
                             _A        =  encode_jitted(**lowerCAmelCase__							).to_tuple()

                      with self.subTest("""JIT Disabled"""							):
                             with jax.disable_jit():
                                    _A        =  encode_jitted(**lowerCAmelCase__							).to_tuple()

                      self.assertEqual(len(lowerCAmelCase__							)		,				len(lowerCAmelCase__							)							)
                      for jitted_output, output in zip(lowerCAmelCase__		,				lowerCAmelCase__							):
                             self.assertEqual(jitted_output.shape		,				output.shape							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__							):
                      _A        =  model_class(lowerCAmelCase__							)
                      _A        =  model.encode(inputs_dict["""input_ids"""]		,				inputs_dict["""attention_mask"""]							)

                      _A        =  {
                          """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                          """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                          """encoder_outputs""": encoder_outputs,
                      }

                      @jax.jit
                      def decode_jitted(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							):
                             return model.decode(
                                 decoder_input_ids=lowerCAmelCase__		,				decoder_attention_mask=lowerCAmelCase__		,				encoder_outputs=lowerCAmelCase__		,				)

                      with self.subTest("""JIT Enabled"""							):
                             _A        =  decode_jitted(**lowerCAmelCase__							).to_tuple()

                      with self.subTest("""JIT Disabled"""							):
                             with jax.disable_jit():
                                    _A        =  decode_jitted(**lowerCAmelCase__							).to_tuple()

                      self.assertEqual(len(lowerCAmelCase__							)		,				len(lowerCAmelCase__							)							)
                      for jitted_output, output in zip(lowerCAmelCase__		,				lowerCAmelCase__							):
                             self.assertEqual(jitted_output.shape		,				output.shape							)



 @slow
 def      UpperCAmelCase							(					self							)  ->					Tuple:


        for model_class_name in self.all_model_classes:
               _A        =  model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""							)
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               _A        =  np.ones((1, 1)							) * model.config.eos_token_id
               _A        =  model(lowerCAmelCase__							)
               self.assertIsNotNone(lowerCAmelCase__							)



 @unittest.skipUnless(jax_device != """cpu"""		,				"""3B test too slow on CPU."""							)
 @slow
 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  {"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
        _A        =  {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

        _A        =  FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""		,				from_pt=lowerCAmelCase__							)
        _A        =  BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""							)

        _A        =  ["""Sam"""]
        _A        =  tokenizer(lowerCAmelCase__		,				return_tensors="""jax"""							)

        _A        =  model.generate(**lowerCAmelCase__		,				**lowerCAmelCase__							)
        _A        =  """Sam is a great name. It means \"sun\" in Gaelic."""

        _A        =  tokenizer.batch_decode(lowerCAmelCase__		,				**lowerCAmelCase__							)
        assert generated_txt[0].strip() == tgt_text





from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__a  :     List[str]       =  Lock()





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				global process_lock

				# we perform n swaps since after n swaps we know we are sorted
				# we *could* stop early if we are sorted already, but it takes as long to
				# find out we are sorted as it does to sort the list with this algorithm
				for i in range(0							,			10							):
								if (i + position) % 2 == 0 and r_send is not None:
												# send your value to your right neighbor
												process_lock.acquire()
												r_send[1].send(lowercase							)
												process_lock.release()

												# receive your right neighbor's value
												process_lock.acquire()
												__lowercase 			=							rr_cv[0].recv()
												process_lock.release()

												# take the lower value since you are on the left
												__lowercase 			=							min(lowercase							,			lowercase							)
								elif (i + position) % 2 != 0 and l_send is not None:
												# send your value to your left neighbor
												process_lock.acquire()
												l_send[1].send(lowercase							)
												process_lock.release()

												# receive your left neighbor's value
												process_lock.acquire()
												__lowercase 			=							lr_cv[0].recv()
												process_lock.release()

												# take the higher value since you are on the right
												__lowercase 			=							max(lowercase							,			lowercase							)
    # after all swaps are performed, send the values back to main
				result_pipe[1].send(lowercase							)





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							[]
				__lowercase 			=							[]
				# initialize the list of pipes where the values will be retrieved
				for _ in arr:
								result_pipe.append(Pipe()							)
				# creates the processes
				# the first and last process only have one neighbor so they are made outside
				# of the loop
				__lowercase 			=							Pipe()
				__lowercase 			=							Pipe()
				process_array_.append(
				    Process(
				        target=lowercase							,			args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])							,			)							)
				__lowercase 			=							temp_rs
				__lowercase 			=							temp_rr

				for i in range(1							,			len(lowercase							) - 1							):
								__lowercase 			=							Pipe()
								__lowercase 			=							Pipe()
								process_array_.append(
								    Process(
								        target=lowercase							,			args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])							,			)							)
								__lowercase 			=							temp_rs
								__lowercase 			=							temp_rr

				process_array_.append(
				    Process(
				        target=lowercase							,			args=(
				            len(lowercase							) - 1,
				            arr[len(lowercase							) - 1],
				            temp_ls,
				            None,
				            temp_lr,
				            None,
				            result_pipe[len(lowercase							) - 1],
				        )							,			)							)

				# start the processes
				for p in process_array_:
								p.start()

				# wait for the processes to end and write their values to the list
				for p in range(0							,			len(lowercase							)							):
								__lowercase 			=							result_pipe[p][0].recv()
								process_array_[p].join()
				return arr





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							list(range(10							,			0							,			-1							)							)
				print('''Initial List'''							)
				print(*lowercase							)
				__lowercase 			=							odd_even_transposition(lowercase							)
				print('''Sorted List\n'''							)
				print(*lowercase							)


if __name__ == "__main__":
		main()