



import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
  import torch



class  __lowerCAmelCase     :




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase=14	,				__UpperCAmelCase=7	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=False	,				__UpperCAmelCase=True	,				__UpperCAmelCase=99	,				__UpperCAmelCase=32	,				__UpperCAmelCase=4	,				__UpperCAmelCase=4	,				__UpperCAmelCase=4	,				__UpperCAmelCase=37	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=512	,				__UpperCAmelCase=0.02	,				):



    '''simple docstring'''
    __lowerCamelCase													=    parent
    __lowerCamelCase													=    batch_size
    __lowerCamelCase													=    seq_length
    __lowerCamelCase													=    is_training
    __lowerCamelCase													=    use_input_mask
    __lowerCamelCase													=    use_token_type_ids
    __lowerCamelCase													=    use_labels
    __lowerCamelCase													=    vocab_size
    __lowerCamelCase													=    hidden_size
    __lowerCamelCase													=    rotary_dim
    __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													=    initializer_range
    __lowerCamelCase													=    None
    __lowerCamelCase													=    vocab_size - 1
    __lowerCamelCase													=    vocab_size - 1
    __lowerCamelCase													=    vocab_size - 1




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size				)

    __lowerCamelCase													=    None
    if self.use_input_mask:
       __lowerCamelCase													=    random_attention_mask([self.batch_size, self.seq_length]				)

    __lowerCamelCase													=    GPTJConfig(
        vocab_size=self.vocab_size	,				n_embd=self.hidden_size	,				n_layer=self.num_hidden_layers	,				n_head=self.num_attention_heads	,				n_positions=self.max_position_embeddings	,				use_cache=snake_case_	,				bos_token_id=self.bos_token_id	,				eos_token_id=self.eos_token_id	,				pad_token_id=self.pad_token_id	,				rotary_dim=self.rotary_dim	,				)

    return (config, input_ids, input_mask)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.prepare_config_and_inputs()
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    config_and_inputs
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    20
    __lowerCamelCase													=    model_class_name(snake_case_				)

    __lowerCamelCase													=    model.init_cache(input_ids.shape[0]	,				snake_case_				)
    __lowerCamelCase													=    jnp.ones((input_ids.shape[0], max_decoder_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=snake_case_	,				past_key_values=snake_case_	,				position_ids=snake_case_	,				)

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

    __lowerCamelCase													=    model(snake_case_				)

    __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        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    20
    __lowerCamelCase													=    model_class_name(snake_case_				)

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

    __lowerCamelCase													=    model.init_cache(input_ids.shape[0]	,				snake_case_				)
    __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=snake_case_	,				past_key_values=snake_case_	,				position_ids=snake_case_	,				)
    __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=snake_case_	,				position_ids=snake_case_	,				)

    __lowerCamelCase													=    model(snake_case_	,				attention_mask=snake_case_				)

    __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  __lowerCAmelCase     (	_a  ,	_a  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 lowerCAmelCase__       					=  (FlaxGPTJForCausalLM,) if is_flax_available() else ()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    FlaxGPTJModelTester(self				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for model_class_name in self.all_model_classes:
       __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(snake_case_	,				snake_case_	,				snake_case_	,				snake_case_				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for model_class_name in self.all_model_classes:
       __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           snake_case_	,				snake_case_	,				snake_case_	,				snake_case_				)




 @tooslow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    GPTaTokenizer.from_pretrained('''gpt2'''	,				pad_token='''<|endoftext|>'''	,				padding_side='''left'''				)
    __lowerCamelCase													=    tokenizer(['''Hello this is a long string''', '''Hey''']	,				return_tensors='''np'''	,				padding=snake_case_	,				truncation=snake_case_				)

    __lowerCamelCase													=    FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''				)
    __lowerCamelCase													=    False
    __lowerCamelCase													=    model.config.eos_token_id

    __lowerCamelCase													=    jax.jit(model.generate				)

    __lowerCamelCase													=    jit_generate(
        inputs['''input_ids''']	,				attention_mask=inputs['''attention_mask''']	,				pad_token_id=tokenizer.pad_token_id				).sequences

    __lowerCamelCase													=    tokenizer.batch_decode(snake_case_	,				skip_special_tokens=snake_case_				)

    __lowerCamelCase													=    [
        '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
        '''Hey, I\'m a little late to the party. I\'m going to''',
    ]

    self.assertListEqual(snake_case_	,				snake_case_				)




 @is_pt_flax_cross_test
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__				):
          # prepare inputs
          __lowerCamelCase													=    self._prepare_for_class(snake_case_	,				snake_case_				)
          __lowerCamelCase													=    {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

          # load corresponding PyTorch class
          __lowerCamelCase													=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
          __lowerCamelCase													=    getattr(snake_case_	,				snake_case_				)

          __lowerCamelCase							,__lowerCamelCase													=    pt_inputs['''input_ids'''].shape
          __lowerCamelCase													=    np.random.randint(0	,				seq_length - 1	,				size=(batch_size,)				)
          for batch_idx, start_index in enumerate(snake_case_				):
             __lowerCamelCase													=    0
             __lowerCamelCase													=    1
             __lowerCamelCase													=    0
             __lowerCamelCase													=    1
          __lowerCamelCase													=    pt_model_class(snake_case_				).eval()
          __lowerCamelCase													=    model_class(snake_case_	,				dtype=jnp.floataa				)

          __lowerCamelCase													=    convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,				snake_case_				)
          __lowerCamelCase													=    fx_state

          with torch.no_grad():
             __lowerCamelCase													=    pt_model(**snake_case_				).to_tuple()

          __lowerCamelCase													=    fx_model(**snake_case_				).to_tuple()
          self.assertEqual(len(snake_case_				)	,				len(snake_case_				)	,				'''Output lengths differ between Flax and PyTorch'''				)
          for fx_output, pt_output in zip(snake_case_	,				snake_case_				):
             self.assert_almost_equals(fx_output[:, -1]	,				pt_output[:, -1].numpy()	,				4E-2				)

          with tempfile.TemporaryDirectory() as tmpdirname:
             pt_model.save_pretrained(snake_case_				)
             __lowerCamelCase													=    model_class.from_pretrained(snake_case_	,				from_pt=snake_case_				)

          __lowerCamelCase													=    fx_model_loaded(**snake_case_				).to_tuple()
          self.assertEqual(
              len(snake_case_				)	,				len(snake_case_				)	,				'''Output lengths differ between Flax and PyTorch'''				)
          for fx_output_loaded, pt_output in zip(snake_case_	,				snake_case_				):
             self.assert_almost_equals(fx_output_loaded[:, -1]	,				pt_output[:, -1].numpy()	,				4E-2				)




 @is_pt_flax_cross_test
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__				):
          # prepare inputs
          __lowerCamelCase													=    self._prepare_for_class(snake_case_	,				snake_case_				)
          __lowerCamelCase													=    {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

          # load corresponding PyTorch class
          __lowerCamelCase													=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
          __lowerCamelCase													=    getattr(snake_case_	,				snake_case_				)

          __lowerCamelCase													=    pt_model_class(snake_case_				).eval()
          __lowerCamelCase													=    model_class(snake_case_	,				dtype=jnp.floataa				)

          __lowerCamelCase													=    load_flax_weights_in_pytorch_model(snake_case_	,				fx_model.params				)
          __lowerCamelCase							,__lowerCamelCase													=    pt_inputs['''input_ids'''].shape
          __lowerCamelCase													=    np.random.randint(0	,				seq_length - 1	,				size=(batch_size,)				)
          for batch_idx, start_index in enumerate(snake_case_				):
             __lowerCamelCase													=    0
             __lowerCamelCase													=    1
             __lowerCamelCase													=    0
             __lowerCamelCase													=    1

          # make sure weights are tied in PyTorch
          pt_model.tie_weights()

          with torch.no_grad():
             __lowerCamelCase													=    pt_model(**snake_case_				).to_tuple()

          __lowerCamelCase													=    fx_model(**snake_case_				).to_tuple()
          self.assertEqual(len(snake_case_				)	,				len(snake_case_				)	,				'''Output lengths differ between Flax and PyTorch'''				)
          for fx_output, pt_output in zip(snake_case_	,				snake_case_				):
             self.assert_almost_equals(fx_output[:, -1]	,				pt_output[:, -1].numpy()	,				4E-2				)

          with tempfile.TemporaryDirectory() as tmpdirname:
             fx_model.save_pretrained(snake_case_				)
             __lowerCamelCase													=    pt_model_class.from_pretrained(snake_case_	,				from_flax=snake_case_				)

          with torch.no_grad():
             __lowerCamelCase													=    pt_model_loaded(**snake_case_				).to_tuple()

          self.assertEqual(
              len(snake_case_				)	,				len(snake_case_				)	,				'''Output lengths differ between Flax and PyTorch'''				)
          for fx_output, pt_output in zip(snake_case_	,				snake_case_				):
             self.assert_almost_equals(fx_output[:, -1]	,				pt_output[:, -1].numpy()	,				4E-2				)




 @tooslow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for model_class_name in self.all_model_classes:
       __lowerCamelCase													=    model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''				)
       __lowerCamelCase													=    model(np.ones((1, 1)				)				)
       self.assertIsNotNone(snake_case_				)











from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_			=					logging.get_logger(__name__)

a_			=					{
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  """poolformer"""




 def __init__(						self	,				__UpperCAmelCase=3	,				__UpperCAmelCase=16	,				__UpperCAmelCase=16	,				__UpperCAmelCase=3	,				__UpperCAmelCase=4.0	,				__UpperCAmelCase=[2, 2, 6, 2]	,				__UpperCAmelCase=[64, 128, 320, 512]	,				__UpperCAmelCase=[7, 3, 3, 3]	,				__UpperCAmelCase=[4, 2, 2, 2]	,				__UpperCAmelCase=[2, 1, 1, 1]	,				__UpperCAmelCase=4	,				__UpperCAmelCase=0.0	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=True	,				__UpperCAmelCase=1E-5	,				__UpperCAmelCase=0.02	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    num_channels
    __lowerCamelCase													=    patch_size
    __lowerCamelCase													=    stride
    __lowerCamelCase													=    padding
    __lowerCamelCase													=    pool_size
    __lowerCamelCase													=    hidden_sizes
    __lowerCamelCase													=    mlp_ratio
    __lowerCamelCase													=    depths
    __lowerCamelCase													=    patch_sizes
    __lowerCamelCase													=    strides
    __lowerCamelCase													=    num_encoder_blocks
    __lowerCamelCase													=    drop_path_rate
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    use_layer_scale
    __lowerCamelCase													=    layer_scale_init_value
    __lowerCamelCase													=    initializer_range
    super().__init__(**__UpperCAmelCase				)



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  version.parse("""1.11"""   )




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return OrderedDict(
        [
            ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
        ]				)




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return 2E-3






