"""simple docstring"""






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
       __lowerCAmelCase					: int        ="""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     UpperCAmelCase__ (       lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :List[str]=None      ,	lowerCAmelCase__  :List[Any]=None      ,	lowerCAmelCase__  :Union[str, Any]=None      ,	lowerCAmelCase__  :List[Any]=None      ,	lowerCAmelCase__  :List[str]=None      ,	lowerCAmelCase__  :str=None      ,	)				->					Any:
      '''simple docstring'''


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

      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=0.0_2	, ):







            """simple docstring"""



            lowercase														=			parent
            lowercase														=			batch_size
            lowercase														=			seq_length
            lowercase														=			is_training
            lowercase														=			use_labels
            lowercase														=			vocab_size
            lowercase														=			hidden_size
            lowercase														=			num_hidden_layers
            lowercase														=			num_attention_heads
            lowercase														=			intermediate_size
            lowercase														=			hidden_act
            lowercase														=			hidden_dropout_prob
            lowercase														=			attention_probs_dropout_prob
            lowercase														=			max_position_embeddings
            lowercase														=			eos_token_id
            lowercase														=			pad_token_id
            lowercase														=			bos_token_id
            lowercase														=			initializer_range

      def        A__ (		self		):







            """simple docstring"""



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

            lowercase														=			shift_tokens_right(__lowerCAmelCase	, 1	, 2		)

            lowercase														=			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	, )
            lowercase														=			prepare_blenderbot_inputs_dict(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
            return config, inputs_dict

      def        A__ (		self		):







            """simple docstring"""



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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			20
            lowercase														=			model_class_name(__lowerCAmelCase		)

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

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

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

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

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

            lowercase														=			model.decode(__lowerCAmelCase	, __lowerCAmelCase		)

            lowercase														=			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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			20
            lowercase														=			model_class_name(__lowerCAmelCase		)

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

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

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

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

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

            lowercase														=			model.decode(__lowerCAmelCase	, __lowerCAmelCase	, decoder_attention_mask=__lowerCAmelCase		)

            lowercase														=			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			):
      snake_case__    :     str													=			99

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			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	, )

            lowercase														=			input_ids.shape[0]
            lowercase														=			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        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase							,							lowercase														=			self._get_config_and_data()
            lowercase														=			FlaxBlenderbotForConditionalGeneration(__lowerCAmelCase		)
            lowercase														=			lm_model(input_ids=__lowerCAmelCase		)
            lowercase														=			(batch_size, input_ids.shape[1], config.vocab_size)
            self.assertEqual(outputs["""logits"""].shape	, __lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			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	, )
            lowercase														=			FlaxBlenderbotForConditionalGeneration(__lowerCAmelCase		)
            lowercase														=			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]	, dtype=np.intaa		)
            lowercase														=			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]	, dtype=np.intaa		)
            lowercase														=			lm_model(input_ids=__lowerCAmelCase	, decoder_input_ids=__lowerCAmelCase		)
            lowercase														=			(*summary.shape, config.vocab_size)
            self.assertEqual(outputs["""logits"""].shape	, __lowerCAmelCase		)







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]	, dtype=np.intaa		)
            lowercase														=			shift_tokens_right(__lowerCAmelCase	, 1	, 2		)
            lowercase														=			np.equal(__lowerCAmelCase	, 1		).astype(np.floataa		).sum()
            lowercase														=			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		( lowerCAmelCase						,					unittest.TestCase						,					lowerCAmelCase			):
      snake_case__    :     Optional[Any]													=			True
      snake_case__    :     Tuple													=			(
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      snake_case__    :     int													=			(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			FlaxBlenderbotModelTester(self		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase														=			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        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase														=			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        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__		):
                        lowercase														=			self._prepare_for_class(__lowerCAmelCase	, __lowerCAmelCase		)
                        lowercase														=			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"""		):
                              lowercase														=			encode_jitted(**__lowerCAmelCase		).to_tuple()

                        with self.subTest("""JIT Disabled"""		):
                              with jax.disable_jit():
                                    lowercase														=			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        A__ (		self		):







            """simple docstring"""



            lowercase							,							lowercase														=			self.model_tester.prepare_config_and_inputs_for_common()

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

                        lowercase														=			{
                            """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"""		):
                              lowercase														=			decode_jitted(**__lowerCAmelCase		).to_tuple()

                        with self.subTest("""JIT Disabled"""		):
                              with jax.disable_jit():
                                    lowercase														=			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        A__ (		self		):







            """simple docstring"""



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







      @unittest.skipUnless(jax_device != """cpu"""	, """3B test too slow on CPU."""		)
      @slow
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			{"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
            lowercase														=			{"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

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

            lowercase														=			["""Sam"""]
            lowercase														=			tokenizer(__lowerCAmelCase	, return_tensors="""jax"""		)

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

            lowercase														=			tokenizer.batch_decode(__lowerCAmelCase	, **__lowerCAmelCase		)
            assert generated_txt[0].strip() == tgt_text




"""simple docstring"""






def     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :int	)				->					list:
      '''simple docstring'''


      lowercase														=			word.split()

      def justify(lowerCAmelCase__  :list      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :int	) -> str:
            lowercase														=			max_width - width
            lowercase														=			len(lowerCAmelCase__	)
            if len(lowerCAmelCase__	) == 1:
                  # if there is only word in line
                  # just insert overall_spaces_count for the remainder of line
                  return line[0] + " " * overall_spaces_count
            else:
                  lowercase														=			words_count - 1
                  # num_spaces_between_words_list[i] : tells you to insert
                  # num_spaces_between_words_list[i] spaces
                  # after word on line[i]
                  lowercase														=			spaces_to_insert_between_words * [
                      overall_spaces_count // spaces_to_insert_between_words
                  ]
                  lowercase														=			(
                      overall_spaces_count % spaces_to_insert_between_words
                  )
                  # distribute spaces via round robin to the left words
                  for i in range(lowerCAmelCase__	):
                        num_spaces_between_words_list[i] += 1
                  lowercase														=			[]
                  for i in range(lowerCAmelCase__	):
                        # add the word
                        aligned_words_list.append(line[i]	)
                        # add the spaces to insert
                        aligned_words_list.append(num_spaces_between_words_list[i] * """ """	)
                  # just add the last word to the sentence
                  aligned_words_list.append(line[-1]	)
                  # join the aligned words list to form a justified line
                  return "".join(lowerCAmelCase__	)

      lowercase														=			[]
      lowercase														=			[]
      lowercase														=			0
      for word in words:
            if width + len(lowerCAmelCase__	) + len(lowerCAmelCase__	) <= max_width:
                  # keep adding words until we can fill out max_width
                  # width = sum of length of all words (without overall_spaces_count)
                  # len(word) = length of current word
                  # len(line) = number of overall_spaces_count to insert between words
                  line.append(lowerCAmelCase__	)
                  width += len(lowerCAmelCase__	)
            else:
                  # justify the line and add it to result
                  answer.append(justify(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)	)
                  # reset new line and new width
                  lowercase							,							lowercase														=			[word], len(lowerCAmelCase__	)
      lowercase														=			max_width - width - len(lowerCAmelCase__	)
      answer.append(""" """.join(lowerCAmelCase__	) + (remaining_spaces + 1) * """ """	)
      return answer


if __name__ == "__main__":
       from doctest import testmod

       testmod()



