





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   __SCREAMING_SNAKE_CASE     :







       def __init__( self	:      Any						,   A	:      Optional[int]						,   A	:      List[str]=1_4						,   A	:      List[str]=7						,   A	:      Union[str, Any]=True						,   A	:      List[Any]=True						,   A	:      Tuple=False						,   A	:      Tuple=True						,   A	:      Union[str, Any]=9_9						,   A	:      Any=3_2						,   A	:      Optional[Any]=4						,   A	:      Dict=4						,   A	:      List[Any]=4						,   A	:      Any=3_7						,   A	:      Dict="gelu"						,   A	:      Dict=0.1						,   A	:      Union[str, Any]=0.1						,   A	:      Optional[Any]=5_1_2						,   A	:      Union[str, Any]=0.02						,   )  ->Optional[int]:
          lowerCamelCase__  :		str     =				parent
          lowerCamelCase__  :		List[str]     =				batch_size
          lowerCamelCase__  :		List[Any]     =				seq_length
          lowerCamelCase__  :		List[str]     =				is_training
          lowerCamelCase__  :		Optional[int]     =				use_input_mask
          lowerCamelCase__  :		List[Any]     =				use_token_type_ids
          lowerCamelCase__  :		int     =				use_labels
          lowerCamelCase__  :		str     =				vocab_size
          lowerCamelCase__  :		List[Any]     =				hidden_size
          lowerCamelCase__  :		List[str]     =				rotary_dim
          lowerCamelCase__  :		Optional[int]     =				num_hidden_layers
          lowerCamelCase__  :		Any     =				num_attention_heads
          lowerCamelCase__  :		Tuple     =				intermediate_size
          lowerCamelCase__  :		Tuple     =				hidden_act
          lowerCamelCase__  :		Tuple     =				hidden_dropout_prob
          lowerCamelCase__  :		str     =				attention_probs_dropout_prob
          lowerCamelCase__  :		Tuple     =				max_position_embeddings
          lowerCamelCase__  :		List[str]     =				initializer_range
          lowerCamelCase__  :		Tuple     =				None
          lowerCamelCase__  :		Union[str, Any]     =				vocab_size - 1
          lowerCamelCase__  :		List[Any]     =				vocab_size - 1
          lowerCamelCase__  :		Union[str, Any]     =				vocab_size - 1







       def 		__lowerCamelCase       ( self	:      Any     )  ->str:
          lowerCamelCase__  :		Tuple     =				ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size     )

          lowerCamelCase__  :		Any     =				None
          if self.use_input_mask:
             lowerCamelCase__  :		Tuple     =				random_attention_mask([self.batch_size, self.seq_length]     )

          lowerCamelCase__  :		Union[str, Any]     =				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=__lowerCamelCase						,   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	:      List[str]     )  ->str:
          lowerCamelCase__  :		Union[str, Any]     =				self.prepare_config_and_inputs()
          lowerCamelCase__  :		int     =				config_and_inputs
          lowerCamelCase__  :		int     =				{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict







       def 		__lowerCamelCase       ( self	:      Union[str, Any]						,   A	:      Tuple						,   A	:      Optional[int]						,   A	:      Optional[int]						,   A	:      Any     )  ->Dict:
          lowerCamelCase__  :		Union[str, Any]     =				2_0
          lowerCamelCase__  :		Any     =				model_class_name(__lowerCamelCase     )

          lowerCamelCase__  :		Dict     =				model.init_cache(input_ids.shape[0]						,   __lowerCamelCase     )
          lowerCamelCase__  :		Dict     =				jnp.ones((input_ids.shape[0], max_decoder_length)						,   dtype='''i4'''     )

          lowerCamelCase__  :		Any     =				jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1     )[None, :]						,   (input_ids.shape[0], input_ids.shape[-1] - 1)     )
          lowerCamelCase__  :		Optional[int]     =				model(
              input_ids[:, :-1]						,   attention_mask=__lowerCamelCase						,   past_key_values=__lowerCamelCase						,   position_ids=__lowerCamelCase						,   )

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

          lowerCamelCase__  :		List[Any]     =				model(__lowerCamelCase     )

          lowerCamelCase__  :		Union[str, Any]     =				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	:      int						,   A	:      Any						,   A	:      Tuple						,   A	:      List[str]						,   A	:      Dict     )  ->Optional[int]:
          lowerCamelCase__  :		Tuple     =				2_0
          lowerCamelCase__  :		Tuple     =				model_class_name(__lowerCamelCase     )

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

          lowerCamelCase__  :		int     =				model.init_cache(input_ids.shape[0]						,   __lowerCamelCase     )
          lowerCamelCase__  :		Union[str, Any]     =				jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1     )[None, :]						,   (input_ids.shape[0], input_ids.shape[-1] - 1)     )

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

          lowerCamelCase__  :		Any     =				model(__lowerCamelCase						,   attention_mask=__lowerCamelCase     )

          lowerCamelCase__  :		Optional[int]     =				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   __SCREAMING_SNAKE_CASE     (					_snake_case	,_snake_case	,unittest.TestCase   ):
       _UpperCAmelCase :    Union[str, Any]					       =       (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
       _UpperCAmelCase :    int					       =       (FlaxGPTJForCausalLM,) if is_flax_available() else ()







       def 		__lowerCamelCase       ( self	:      List[Any]     )  ->Dict:
          lowerCamelCase__  :		Optional[int]     =				FlaxGPTJModelTester(self     )







       def 		__lowerCamelCase       ( self	:      Dict     )  ->List[str]:
          for model_class_name in self.all_model_classes:
             lowerCamelCase__  :		int     =				self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase     )







       def 		__lowerCamelCase       ( self	:      Optional[int]     )  ->Optional[Any]:
          for model_class_name in self.all_model_classes:
             lowerCamelCase__  :		Tuple     =				self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase     )







       @tooslow
       def 		__lowerCamelCase       ( self	:      Dict     )  ->str:
          lowerCamelCase__  :		List[Any]     =				GPTaTokenizer.from_pretrained('''gpt2'''						,   pad_token='''<|endoftext|>'''						,   padding_side='''left'''     )
          lowerCamelCase__  :		int     =				tokenizer(['''Hello this is a long string''', '''Hey''']						,   return_tensors='''np'''						,   padding=__lowerCamelCase						,   truncation=__lowerCamelCase     )

          lowerCamelCase__  :		Tuple     =				FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''     )
          lowerCamelCase__  :		Tuple     =				False
          lowerCamelCase__  :		Optional[int]     =				model.config.eos_token_id

          lowerCamelCase__  :		List[str]     =				jax.jit(model.generate     )

          lowerCamelCase__  :		Any     =				jit_generate(
              inputs['''input_ids''']						,   attention_mask=inputs['''attention_mask''']						,   pad_token_id=tokenizer.pad_token_id     ).sequences

          lowerCamelCase__  :		Dict     =				tokenizer.batch_decode(__lowerCamelCase						,   skip_special_tokens=__lowerCamelCase     )

          lowerCamelCase__  :		List[Any]     =				[
              '''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(__lowerCamelCase						,   __lowerCamelCase     )







       @is_pt_flax_cross_test
       def 		__lowerCamelCase       ( self	:      List[str]     )  ->Dict:
          lowerCamelCase__  :		Optional[int]     =				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__  :		str     =				self._prepare_for_class(__lowerCamelCase						,   __lowerCamelCase     )
                lowerCamelCase__  :		int     =				{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

                lowerCamelCase__  :		Tuple     =				pt_inputs['''input_ids'''].shape
                lowerCamelCase__  :		Optional[int]     =				np.random.randint(0						,   seq_length - 1						,   size=(batch_size,)     )
                for batch_idx, start_index in enumerate(__lowerCamelCase     ):
                   lowerCamelCase__  :		Dict     =				0
                   lowerCamelCase__  :		Any     =				1
                   lowerCamelCase__  :		Union[str, Any]     =				0
                   lowerCamelCase__  :		Any     =				1
                lowerCamelCase__  :		str     =				pt_model_class(__lowerCamelCase     ).eval()
                lowerCamelCase__  :		List[str]     =				model_class(__lowerCamelCase						,   dtype=jnp.floataa     )

                lowerCamelCase__  :		Any     =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,   __lowerCamelCase     )
                lowerCamelCase__  :		Tuple     =				fx_state

                with torch.no_grad():
                   lowerCamelCase__  :		Union[str, Any]     =				pt_model(**__lowerCamelCase     ).to_tuple()

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

                with tempfile.TemporaryDirectory() as tmpdirname:
                   pt_model.save_pretrained(__lowerCamelCase     )
                   lowerCamelCase__  :		Optional[Any]     =				model_class.from_pretrained(__lowerCamelCase						,   from_pt=__lowerCamelCase     )

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







       @is_pt_flax_cross_test
       def 		__lowerCamelCase       ( self	:      List[str]     )  ->Union[str, Any]:
          lowerCamelCase__  :		List[str]     =				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__  :		Optional[Any]     =				self._prepare_for_class(__lowerCamelCase						,   __lowerCamelCase     )
                lowerCamelCase__  :		Optional[int]     =				{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

                lowerCamelCase__  :		Union[str, Any]     =				pt_model_class(__lowerCamelCase     ).eval()
                lowerCamelCase__  :		Tuple     =				model_class(__lowerCamelCase						,   dtype=jnp.floataa     )

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

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

                with torch.no_grad():
                   lowerCamelCase__  :		List[str]     =				pt_model(**__lowerCamelCase     ).to_tuple()

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

                with tempfile.TemporaryDirectory() as tmpdirname:
                   fx_model.save_pretrained(__lowerCamelCase     )
                   lowerCamelCase__  :		List[Any]     =				pt_model_class.from_pretrained(__lowerCamelCase						,   from_flax=__lowerCamelCase     )

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

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







       @tooslow
       def 		__lowerCamelCase       ( self	:      str     )  ->Optional[Any]:
          for model_class_name in self.all_model_classes:
             lowerCamelCase__  :		str     =				model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''     )
             lowerCamelCase__  :		Any     =				model(np.ones((1, 1)     )     )
             self.assertIsNotNone(__lowerCamelCase     )







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase												=						{
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						['''MaskFormerFeatureExtractor''']
    __lowerCamelCase												=						['''MaskFormerImageProcessor''']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						[
        '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MaskFormerForInstanceSegmentation''',
        '''MaskFormerModel''',
        '''MaskFormerPreTrainedModel''',
    ]
    __lowerCamelCase												=						[
        '''MaskFormerSwinBackbone''',
        '''MaskFormerSwinModel''',
        '''MaskFormerSwinPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase												=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)



