"""simple docstring"""
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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.models.blenderbot_small.modeling_flax_blenderbot_small import (
         FlaxBlenderbotSmallForConditionalGeneration,
         FlaxBlenderbotSmallModel,
         shift_tokens_right,
     )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			Optional[int]=None	,     _UpperCamelCase    :			Any=None	,     _UpperCamelCase    :			Dict=None	,     _UpperCamelCase    :			Tuple=None	,     _UpperCamelCase    :			List[Any]=None	,     _UpperCamelCase    :			List[Any]=None	,     )   ->			str:





      """simple docstring"""






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



    """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						,      ):






          """simple docstring"""

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

    def 						snake_case	(			self      ):






          """simple docstring"""

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

          snake_case        =     shift_tokens_right(_UpperCAmelCase						,      1						,      2      )

          snake_case        =     BlenderbotSmallConfig(
              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=_UpperCAmelCase						,      )
          snake_case        =     prepare_blenderbot_inputs_dict(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase      )
          return config, inputs_dict

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     self.prepare_config_and_inputs()
          return config, inputs_dict

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






          """simple docstring"""

          snake_case        =     20
          snake_case        =     model_class_name(_UpperCAmelCase      )

          snake_case        =     model.encode(inputs_dict['input_ids']      )

          snake_case        =     (
              inputs_dict['decoder_input_ids'],
              inputs_dict['decoder_attention_mask'],
          )

          snake_case        =     model.init_cache(decoder_input_ids.shape[0]						,      _UpperCAmelCase						,      _UpperCAmelCase      )
          snake_case        =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,      dtype='i4'      )

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

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

          snake_case        =     model.decode(_UpperCAmelCase						,      _UpperCAmelCase      )

          snake_case        =     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						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






          """simple docstring"""

          snake_case        =     20
          snake_case        =     model_class_name(_UpperCAmelCase      )

          snake_case        =     model.encode(inputs_dict['input_ids']      )

          snake_case        =     (
              inputs_dict['decoder_input_ids'],
              inputs_dict['decoder_attention_mask'],
          )

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

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

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

          snake_case        =     model.decode(_UpperCAmelCase						,      _UpperCAmelCase						,      decoder_attention_mask=_UpperCAmelCase      )

          snake_case        =     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 					lowerCAmelCase_      (     unittest.TestCase   ):



    """simple docstring"""
    _lowerCAmelCase       :      Union[str, Any]     					=  99

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     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						,      )

          snake_case        =     input_ids.shape[0]
          snake_case        =     BlenderbotSmallConfig(
              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 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     self._get_config_and_data()
          snake_case        =     FlaxBlenderbotSmallForConditionalGeneration(_UpperCAmelCase      )
          snake_case        =     lm_model(input_ids=_UpperCAmelCase      )
          snake_case        =     (batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs['logits'].shape						,      _UpperCAmelCase      )

    def 						snake_case	(			self      ):






          """simple docstring"""

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

    def 						snake_case	(			self      ):






          """simple docstring"""

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





@require_flax
class 					lowerCAmelCase_      (     _UpperCamelCase       ,      unittest.TestCase       ,      _UpperCamelCase   ):



    """simple docstring"""
    _lowerCAmelCase       :      Union[str, Any]     					=  True
    _lowerCAmelCase       :      Union[str, Any]     					=  (
        (
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    _lowerCAmelCase       :      List[str]     					=  (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     FlaxBlenderbotSmallModelTester(self      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
                self.model_tester.check_use_cache_forward(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     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(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase      )

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__      ):
                      snake_case        =     self._prepare_for_class(_UpperCAmelCase						,      _UpperCAmelCase      )
                      snake_case        =     model_class(_UpperCAmelCase      )

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

                      with self.subTest('JIT Enabled'      ):
                            snake_case        =     encode_jitted(**_UpperCAmelCase      ).to_tuple()

                      with self.subTest('JIT Disabled'      ):
                            with jax.disable_jit():
                                  snake_case        =     encode_jitted(**_UpperCAmelCase      ).to_tuple()

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

    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__      ):
                      snake_case        =     model_class(_UpperCAmelCase      )
                      snake_case        =     model.encode(inputs_dict['input_ids']						,      inputs_dict['attention_mask']      )

                      snake_case        =     {
                          '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=_UpperCAmelCase						,      decoder_attention_mask=_UpperCAmelCase						,      encoder_outputs=_UpperCAmelCase						,      )

                      with self.subTest('JIT Enabled'      ):
                            snake_case        =     decode_jitted(**_UpperCAmelCase      ).to_tuple()

                      with self.subTest('JIT Disabled'      ):
                            with jax.disable_jit():
                                  snake_case        =     decode_jitted(**_UpperCAmelCase      ).to_tuple()

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

    @slow
    def 						snake_case	(			self      ):






          """simple docstring"""

          for model_class_name in self.all_model_classes:
                snake_case        =     model_class_name.from_pretrained('facebook/blenderbot_small-90M'      )
                # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                snake_case        =     np.ones((1, 1)      ) * model.config.eos_token_id
                snake_case        =     model(_UpperCAmelCase      )
                self.assertIsNotNone(_UpperCAmelCase      )



"""simple docstring"""
def 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			bool:





						"""simple docstring"""






						if not all(x.isalpha() for x in string   ):
												raise ValueError('String must only contain alphabetic characters.'   )

						snake_case        =     sorted(string.lower()   )
						return len(_UpperCamelCase   ) == len(set(_UpperCamelCase   )   )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					input("Enter a string ").strip()

					SCREAMING_SNAKE_CASE__   =					is_isogram(input_str)
					print(f"""{input_str} is {'an' if isogram else 'not an'} isogram.""")


