




'''simple docstring'''


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFResNetForImageClassification, TFResNetModel
    from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class     A					:







       def __init__(						self :						Optional[int]							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						Optional[int]=3							,    lowerCAmelCase_ :						Optional[Any]=32							,    lowerCAmelCase_ :						Optional[Any]=3							,    lowerCAmelCase_ :						List[str]=10							,    lowerCAmelCase_ :						Optional[Any]=[10, 20, 30, 40]							,    lowerCAmelCase_ :						Union[str, Any]=[1, 1, 2, 1]							,    lowerCAmelCase_ :						Union[str, Any]=True							,    lowerCAmelCase_ :						Dict=True							,    lowerCAmelCase_ :						str="relu"							,    lowerCAmelCase_ :						List[Any]=3							,    lowerCAmelCase_ :						List[Any]=None							,    )    ->							Optional[int]:


              """simple docstring"""


              _a			  =       parent
              _a			  =       batch_size
              _a			  =       image_size
              _a			  =       num_channels
              _a			  =       embeddings_size
              _a			  =       hidden_sizes
              _a			  =       depths
              _a			  =       is_training
              _a			  =       use_labels
              _a			  =       hidden_act
              _a			  =       num_labels
              _a			  =       scope
              _a			  =       len(lowerCAmelCase_      )







       def      __lowerCAmelCase    (						self :						int      )    ->							Union[str, Any]:


              """simple docstring"""


              _a			  =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

              _a			  =       None
              if self.use_labels:
                     _a			  =       ids_tensor([self.batch_size]							,    self.num_labels      )

              _a			  =       self.get_config()

              return config, pixel_values, labels







       def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							int:


              """simple docstring"""


              return ResNetConfig(
                  num_channels=self.num_channels							,    embeddings_size=self.embeddings_size							,    hidden_sizes=self.hidden_sizes							,    depths=self.depths							,    hidden_act=self.hidden_act							,    num_labels=self.num_labels							,    image_size=self.image_size							,    )







       def      __lowerCAmelCase    (						self :						Optional[int]							,    lowerCAmelCase_ :						Dict							,    lowerCAmelCase_ :						List[str]							,    lowerCAmelCase_ :						Any      )    ->							int:


              """simple docstring"""


              _a			  =       TFResNetModel(config=lowerCAmelCase_      )
              _a			  =       model(lowerCAmelCase_      )
              # expected last hidden states: B, C, H // 32, W // 32
              self.parent.assertEqual(
                  result.last_hidden_state.shape							,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,    )







       def      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						Optional[Any]							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						Dict      )    ->							Optional[int]:


              """simple docstring"""


              _a			  =       self.num_labels
              _a			  =       TFResNetForImageClassification(lowerCAmelCase_      )
              _a			  =       model(lowerCAmelCase_							,    labels=lowerCAmelCase_      )
              self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)      )







       def      __lowerCAmelCase    (						self :						List[str]      )    ->							List[str]:


              """simple docstring"""


              _a			  =       self.prepare_config_and_inputs()
              _a  ,							_a  ,							_a			  =       config_and_inputs
              _a			  =       {'''pixel_values''': pixel_values}
              return config, inputs_dict



@require_tf
class     A					(	_a  ,_a  ,unittest.TestCase					):
       lowercase_           =       (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
       lowercase_           =       (
           {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
           if is_tf_available()
           else {}
       )

       lowercase_           =       False
       lowercase_           =       False
       lowercase_           =       False
       lowercase_           =       False
       lowercase_           =       False







       def      __lowerCAmelCase    (						self :						Tuple      )    ->							str:


              """simple docstring"""


              _a			  =       TFResNetModelTester(self      )
              _a			  =       ConfigTester(self							,    config_class=lowerCAmelCase_							,    has_text_modality=lowerCAmelCase_      )







       def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							int:


              """simple docstring"""


              self.create_and_test_config_common_properties()
              self.config_tester.create_and_test_config_to_json_string()
              self.config_tester.create_and_test_config_to_json_file()
              self.config_tester.create_and_test_config_from_and_save_pretrained()
              self.config_tester.create_and_test_config_with_num_labels()
              self.config_tester.check_config_can_be_init_without_params()
              self.config_tester.check_config_arguments_init()







       def      __lowerCAmelCase    (						self :						int      )    ->							Union[str, Any]:


              """simple docstring"""


              return







       @unittest.skip(reason='''ResNet does not use inputs_embeds'''      )
       def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							int:


              """simple docstring"""


              pass







       @unittest.skip(reason='''ResNet does not support input and output embeddings'''      )
       def      __lowerCAmelCase    (						self :						List[str]      )    ->							Union[str, Any]:


              """simple docstring"""


              pass







       def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							List[Any]:


              """simple docstring"""


              _a  ,							_a			  =       self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     _a			  =       model_class(lowerCAmelCase_      )
                     _a			  =       inspect.signature(model.call      )
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     _a			  =       [*signature.parameters.keys()]

                     _a			  =       ['''pixel_values''']
                     self.assertListEqual(arg_names[:1]							,    lowerCAmelCase_      )







       def      __lowerCAmelCase    (						self :						Tuple      )    ->							str:


              """simple docstring"""


              _a			  =       self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*lowerCAmelCase_      )







       def      __lowerCAmelCase    (						self :						Any      )    ->							List[str]:


              """simple docstring"""


              def check_hidden_states_output(lowerCAmelCase_ :						Any							,    lowerCAmelCase_ :						str							,    lowerCAmelCase_ :						Dict      ):
                     _a			  =       model_class(lowerCAmelCase_      )
                     _a			  =       model(**self._prepare_for_class(lowerCAmelCase_							,    lowerCAmelCase_      )      )

                     _a			  =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                     _a			  =       self.model_tester.num_stages
                     self.assertEqual(len(lowerCAmelCase_      )							,    expected_num_stages + 1      )

                     # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
                     self.assertListEqual(
                         list(hidden_states[0].shape[-2:]      )							,    [self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,    )

              _a  ,							_a			  =       self.model_tester.prepare_config_and_inputs_for_common()
              _a			  =       ['''basic''', '''bottleneck''']
              for model_class in self.all_model_classes:
                     for layer_type in layers_type:
                            _a			  =       layer_type
                            _a			  =       True
                            check_hidden_states_output(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_      )

                            # check that output_hidden_states also work using config
                            del inputs_dict["output_hidden_states"]
                            _a			  =       True

                            check_hidden_states_output(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_      )







       def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							Union[str, Any]:


              """simple docstring"""


              _a			  =       self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_      )







       @slow
       def      __lowerCAmelCase    (						self :						Dict      )    ->							Optional[int]:


              """simple docstring"""


              for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     _a			  =       TFResNetModel.from_pretrained(lowerCAmelCase_      )
                     self.assertIsNotNone(lowerCAmelCase_      )



def 	snake_case_					():





       '''simple docstring'''


       _a			  =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
       return image

@require_tf
@require_vision
class     A					(	unittest.TestCase					):







       @cached_property
       def      __lowerCAmelCase    (						self :						int      )    ->							Tuple:


              """simple docstring"""


              return (
                  AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
                  if is_vision_available()
                  else None
              )







       @slow
       def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Tuple:


              """simple docstring"""


              _a			  =       TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )

              _a			  =       self.default_image_processor
              _a			  =       prepare_img()
              _a			  =       image_processor(images=lowerCAmelCase_							,    return_tensors='''tf'''      )

              # forward pass
              _a			  =       model(**lowerCAmelCase_      )

              # verify the logits
              _a			  =       tf.TensorShape((1, 10_00)      )
              self.assertEqual(outputs.logits.shape							,    lowerCAmelCase_      )

              _a			  =       tf.constant([-11.10_69, -9.7_8_7_7, -8.3_7_7_7]      )

              self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()							,    lowerCAmelCase_							,    atol=1e-4      )      )







'''simple docstring'''


import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
				from transformers import LlamaTokenizerFast
except ImportError as e:
				warnings.warn(e)
				warnings.warn(
				    'The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion'
				)
				_snake_case   :   int     							=	None
_snake_case   :   Tuple     							=	{
    '7B': 11008,
    '13B': 13824,
    '30B': 17920,
    '65B': 22016,
    '70B': 28672,
}
_snake_case   :   int     							=	{
    '7B': 1,
    '7Bf': 1,
    '13B': 2,
    '13Bf': 2,
    '30B': 4,
    '65B': 8,
    '70B': 8,
    '70Bf': 8,
}
def 	snake_case_					(UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						Optional[Any]=1				,		UpperCamelCase :						Optional[Any]=256   ):





							'''simple docstring'''


							return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3   )   ) + multiple_of - 1) // multiple_of)
def 	snake_case_					(UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							with open(UpperCamelCase				,		'''r'''   ) as f:
														return json.load(UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						str				,		UpperCamelCase :						str   ):





							'''simple docstring'''


							with open(UpperCamelCase				,		'''w'''   ) as f:
														json.dump(UpperCamelCase				,		UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						List[str]				,		UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						str=True   ):





							'''simple docstring'''


							os.makedirs(UpperCamelCase				,		exist_ok=UpperCamelCase   )
							_a			  =       os.path.join(UpperCamelCase				,		'''tmp'''   )
							os.makedirs(UpperCamelCase				,		exist_ok=UpperCamelCase   )

							_a			  =       read_json(os.path.join(UpperCamelCase				,		'''params.json'''   )   )
							_a			  =       NUM_SHARDS[model_size]
							_a			  =       params['''n_layers''']
							_a			  =       params['''n_heads''']
							_a			  =       n_heads // num_shards
							_a			  =       params['''dim''']
							_a			  =       dim // n_heads
							_a			  =       10000.0
							_a			  =       1.0 / (base ** (torch.arange(0				,		UpperCamelCase				,		2   ).float() / dims_per_head))

							if "n_kv_heads" in params:
														_a			  =       params['''n_kv_heads''']  # for GQA / MQA
														_a			  =       n_heads_per_shard // num_key_value_heads
														_a			  =       dim // num_key_value_heads
							else:  # compatibility with other checkpoints
														_a			  =       n_heads
														_a			  =       n_heads_per_shard
														_a			  =       dim

							# permute for sliced rotary
							def permute(UpperCamelCase :						List[str]				,		UpperCamelCase :						str=n_heads				,		UpperCamelCase :						int=dim				,		UpperCamelCase :						Optional[Any]=dim   ):
														return w.view(UpperCamelCase				,		dima // n_heads // 2				,		2				,		UpperCamelCase   ).transpose(1				,		2   ).reshape(UpperCamelCase				,		UpperCamelCase   )

							print(f'Fetching all parameters from the checkpoint at {input_base_path}.'   )
							# Load weights
							if model_size == "7B":
														# Not sharded
														# (The sharded implementation would also work, but this is simpler.)
														_a			  =       torch.load(os.path.join(UpperCamelCase				,		'''consolidated.00.pth'''   )				,		map_location='''cpu'''   )
							else:
														# Sharded
														_a			  =       [
														    torch.load(os.path.join(UpperCamelCase				,		f'consolidated.{i:02d}.pth'   )				,		map_location='''cpu'''   )
														    for i in range(UpperCamelCase   )
														]
							_a			  =       0
							_a			  =       {'''weight_map''': {}}
							for layer_i in range(UpperCamelCase   ):
														_a			  =       f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
														if model_size == "7B":
																					# Unsharded
																					_a			  =       {
																					    f'model.layers.{layer_i}.self_attn.q_proj.weight': permute(
																					        loaded[f'layers.{layer_i}.attention.wq.weight']   ),
																					    f'model.layers.{layer_i}.self_attn.k_proj.weight': permute(
																					        loaded[f'layers.{layer_i}.attention.wk.weight']   ),
																					    f'model.layers.{layer_i}.self_attn.v_proj.weight': loaded[f'layers.{layer_i}.attention.wv.weight'],
																					    f'model.layers.{layer_i}.self_attn.o_proj.weight': loaded[f'layers.{layer_i}.attention.wo.weight'],
																					    f'model.layers.{layer_i}.mlp.gate_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w1.weight'],
																					    f'model.layers.{layer_i}.mlp.down_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w2.weight'],
																					    f'model.layers.{layer_i}.mlp.up_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w3.weight'],
																					    f'model.layers.{layer_i}.input_layernorm.weight': loaded[f'layers.{layer_i}.attention_norm.weight'],
																					    f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[f'layers.{layer_i}.ffn_norm.weight'],
																					}
														else:
																					# Sharded
																					# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
																					# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
																					# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

																					_a			  =       {
																					    f'model.layers.{layer_i}.input_layernorm.weight': loaded[0][
																					        f'layers.{layer_i}.attention_norm.weight'
																					    ].clone(),
																					    f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[0][
																					        f'layers.{layer_i}.ffn_norm.weight'
																					    ].clone(),
																					}
																					_a			  =       permute(
																					    torch.cat(
																					        [
																					            loaded[i][f'layers.{layer_i}.attention.wq.weight'].view(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
																					            for i in range(UpperCamelCase   )
																					        ]				,		dim=0				,		).reshape(UpperCamelCase				,		UpperCamelCase   )   )
																					_a			  =       permute(
																					    torch.cat(
																					        [
																					            loaded[i][f'layers.{layer_i}.attention.wk.weight'].view(
																					                UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
																					            for i in range(UpperCamelCase   )
																					        ]				,		dim=0				,		).reshape(UpperCamelCase				,		UpperCamelCase   )				,		UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase				,		)
																					_a			  =       torch.cat(
																					    [
																					        loaded[i][f'layers.{layer_i}.attention.wv.weight'].view(
																					            UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
																					        for i in range(UpperCamelCase   )
																					    ]				,		dim=0				,		).reshape(UpperCamelCase				,		UpperCamelCase   )

																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(UpperCamelCase   )]				,		dim=1   )
																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(UpperCamelCase   )]				,		dim=0   )
																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(UpperCamelCase   )]				,		dim=1   )
																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(UpperCamelCase   )]				,		dim=0   )

														_a			  =       inv_freq
														for k, v in state_dict.items():
																					_a			  =       filename
																					param_count += v.numel()
														torch.save(UpperCamelCase				,		os.path.join(UpperCamelCase				,		UpperCamelCase   )   )

							_a			  =       f'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
							if model_size == "7B":
														# Unsharded
														_a			  =       {
														    '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
														    '''model.norm.weight''': loaded['''norm.weight'''],
														    '''lm_head.weight''': loaded['''output.weight'''],
														}
							else:
														_a			  =       {
														    '''model.norm.weight''': loaded[0]['''norm.weight'''],
														    '''model.embed_tokens.weight''': torch.cat(
														        [loaded[i]['''tok_embeddings.weight'''] for i in range(UpperCamelCase   )]				,		dim=1   ),
														    '''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(UpperCamelCase   )]				,		dim=0   ),
														}

							for k, v in state_dict.items():
														_a			  =       filename
														param_count += v.numel()
							torch.save(UpperCamelCase				,		os.path.join(UpperCamelCase				,		UpperCamelCase   )   )

							# Write configs
							_a			  =       {'''total_size''': param_count * 2}
							write_json(UpperCamelCase				,		os.path.join(UpperCamelCase				,		'''pytorch_model.bin.index.json'''   )   )
							_a			  =       params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
							_a			  =       params['''multiple_of'''] if '''multiple_of''' in params else 256
							_a			  =       LlamaConfig(
							    hidden_size=UpperCamelCase				,		intermediate_size=compute_intermediate_size(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )				,		num_attention_heads=params['''n_heads''']				,		num_hidden_layers=params['''n_layers''']				,		rms_norm_eps=params['''norm_eps''']				,		num_key_value_heads=UpperCamelCase				,		)
							config.save_pretrained(UpperCamelCase   )

							# Make space so we can load the model properly now.
							del state_dict
							del loaded
							gc.collect()

							print('''Loading the checkpoint in a Llama model.'''   )
							_a			  =       LlamaForCausalLM.from_pretrained(UpperCamelCase				,		torch_dtype=torch.floataa				,		low_cpu_mem_usage=UpperCamelCase   )
							# Avoid saving this as part of the config.
							del model.config._name_or_path

							print('''Saving in the Transformers format.'''   )
							model.save_pretrained(UpperCamelCase				,		safe_serialization=UpperCamelCase   )
							shutil.rmtree(UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Tuple   ):





							'''simple docstring'''


							_a			  =       LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
							print(f'Saving a {tokenizer_class.__name__} to {tokenizer_path}.'   )
							_a			  =       tokenizer_class(UpperCamelCase   )
							tokenizer.save_pretrained(UpperCamelCase   )
def 	snake_case_					():





							'''simple docstring'''


							_a			  =       argparse.ArgumentParser()
							parser.add_argument(
							    '''--input_dir'''				,		help='''Location of LLaMA weights, which contains tokenizer.model and model folders'''				,		)
							parser.add_argument(
							    '''--model_size'''				,		choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only''']				,		)
							parser.add_argument(
							    '''--output_dir'''				,		help='''Location to write HF model and tokenizer'''				,		)
							parser.add_argument('''--safe_serialization'''				,		type=UpperCamelCase				,		help='''Whether or not to save using `safetensors`.'''   )
							_a			  =       parser.parse_args()
							if args.model_size != "tokenizer_only":
														write_model(
														    model_path=args.output_dir				,		input_base_path=os.path.join(args.input_dir				,		args.model_size   )				,		model_size=args.model_size				,		safe_serialization=args.safe_serialization				,		)
							_a			  =       os.path.join(args.input_dir				,		'''tokenizer.model'''   )
							write_tokenizer(args.output_dir				,		UpperCamelCase   )


if __name__ == "__main__":
				main()

